Developing multiplex ddPCR assays for SARS-CoV-2 detection based on probe mix and amplitude based multiplexing

  1. Raphael Nyaruaba
  2. Changchang Li
  3. Caroline Mwaliko
  4. Matilu Mwau
  5. Nelson Odiwuor
  6. Elishiba Muturi
  7. Caroline Muema
  8. Jin Xiong
  9. Junhua Li
  10. Junping Yu
  11. Hongping Wei

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  1. ScreenIT

    SciScore for 10.1101/2020.10.05.20207506: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Research on developing new diagnostic techniques for COVID-19 using clinical samples has also been approved by the ethical committee of Wuhan Institute of Virology (2020FCA001).
    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
    Sample 1 was a sample containing only the SARS-CoV-2 genome (SARS-CoV-2 only) obtained from cultured virus in Vero E6 cells to represent research work; sample 2 was the human gene sample (IC only) pooled from oral swabs of healthy volunteers to represent negative clinical samples during diagnosis; and sample 3 was the SARS-CoV-2 virus from Vero E6 cells spiked with the human gene (SARS-CoV-2+IC) to represent a positive clinical sample during diagnosis.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    All ddPCR data were generated using BioRad’s QuantaSoft™ software version 1.7.4.0197.
    BioRad’s
    suggested: None
    QuantaSoft™
    suggested: None
    However, for the triplex probe mix and fourplex assays, the QuantaSoft™ Analysis Pro software version 1.0.596 was used for analysis.
    QuantaSoft™ Analysis Pro
    suggested: None
    Further statistical analysis was done using the GraphPad Prism Version 6.01 software.
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.
    • No protocol registration statement was detected.

    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 10.1080/14737159.2021.1865807
  3. Version published to 10.1101/2020.10.05.20207506 on medRxiv