Open-source RNA extraction and RT-qPCR methods for SARS-CoV-2 detection

  1. Thomas G. W. Graham
  2. Claire Dugast-Darzacq
  3. Gina M. Dailey
  4. Xammy H. Nguyenla
  5. Erik Van Dis
  6. Meagan N. Esbin
  7. Abrar Abidi
  8. Sarah A. Stanley
  9. Xavier Darzacq
  10. Robert Tjian

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Abstract

Re-opening of communities in the midst of the ongoing COVID-19 pandemic has ignited new waves of infections in many places around the world. Mitigating the risk of reopening will require widespread SARS-CoV-2 testing, which would be greatly facilitated by simple, rapid, and inexpensive testing methods. This study evaluates several protocols for RNA extraction and RT-qPCR that are simpler and less expensive than prevailing methods. First, isopropanol precipitation is shown to provide an effective means of RNA extraction from nasopharyngeal (NP) swab samples. Second, direct addition of NP swab samples to RT-qPCRs is evaluated without an RNA extraction step. A simple, inexpensive swab collection solution suitable for direct addition is validated using contrived swab samples. Third, an open-source master mix for RT-qPCR is described that permits detection of viral RNA in NP swab samples with a limit of detection of approximately 50 RNA copies per reaction. Quantification cycle (Cq) values for purified RNA from 30 known positive clinical samples showed a strong correlation (r 2 = 0.98) between this homemade master mix and commercial TaqPath master mix. Lastly, end-point fluorescence imaging is found to provide an accurate diagnostic readout without requiring a qPCR thermocycler. Adoption of these simple, open-source methods has the potential to reduce the time and expense of COVID-19 testing.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.16.20193466: (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
    Inactivated virus was added to cultured Vero E6 cells under BSL3 conditions, and viral infection was assessed by scoring for cytopathic effect (CPE) after 3 days and 7 days.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Software and Algorithms
    SentencesResources
    For a 20 µl reaction, 5 µl of 4x TaqPath master mix was combined with 1.5 µl of SARS-CoV-2 (2019-nCoV) CDC N1, N2, or RNase P qPCR Probe mixture (Integrated DNA Technologies, Cat. #10006606), RNA sample, and water to a final volume of 20 µl.
    Cat.
    suggested: None
    Quantification cycle (Cq) determination by the second-derivative method: Custom MATLAB code (available at https://gitlab.com/tjian-darzacq-lab/second-derivative-cq-analysis) was used to take the numerical second derivative of fluorescence intensity as a function of cycle number, averaged over a 3-cycle sliding window.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    The fluorescein and rhodamine channel images were overlaid in Fiji (https://imagej.net/Fiji) for Fig 7A-B.
    Fiji
    suggested: (Fiji, RRID:SCR_002285)

    Results from OddPub: Thank you for sharing your code.

    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 10.1371/journal.pone.0246647
  3. Version published to 10.1101/2020.09.16.20193466v1 on medRxiv