A Streamlined Approach to Rapidly Detect SARS-CoV-2 Infection Avoiding RNA Extraction: Workflow Validation

  1. Catia Mio
  2. Adriana Cifù
  3. Stefania Marzinotto
  4. Natascha Bergamin
  5. Chiara Caldana
  6. Silvia Cattarossi
  7. Sara Cmet
  8. Annarosa Cussigh
  9. Romina Martinella
  10. Jessica Zucco
  11. Roberto Verardo
  12. Claudio Schneider
  13. Barbara Marcon
  14. Stefania Zampieri
  15. Corrado Pipan
  16. Francesco Curcio

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has rapidly spread worldwide from the beginning of 2020. The presence of viral RNA in samples by nucleic acid (NA) molecular analysis is the only method available to diagnose COVID-19 disease and to assess patients’ viral load. Since the demand for laboratory reagents has increased, there has been a worldwide shortage of RNA extraction kits. We, therefore, developed a fast and cost-effective viral genome isolation method that, combined with quantitative RT-PCR assay, detects SARS-CoV-2 RNA in patient samples. The method relies on the addition of Proteinase K followed by a controlled heat-shock incubation and, then, E gene evaluation by RT-qPCR. It was validated for sensitivity, specificity, linearity, reproducibility, and precision. It detects as low as 10 viral copies/sample, is rapid, and has been characterized in 60 COVID-19-infected patients. Compared to automated extraction methods, our pretreatment guarantees the same positivity rate with the advantage of shortening the time of the analysis and reducing its cost. This is a rapid workflow meant to aid the healthcare system in the rapid identification of infected patients, such as during a pathogen-related outbreak. For its intrinsic characteristics, this workflow is suitable for large-scale screenings.

Article activity feed

  1. Version published to 10.1155/2020/8869424
  2. ScreenIT

    SciScore for 10.1101/2020.04.06.20054114: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval was obtained from the Medical Research Ethics Committee of the Region Friuli Venezia Giulia, Italy (Consent CEUR-2020-Os-033).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statical analysis: The statistical analyses were performed with GraphPad Prism 6.0.
    GraphPad Prism
    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: We detected the following sentences addressing limitations in the study:
    In many instances, the shortage has induced the limitation of testing only to patients with symptoms (often non-specific) but in this way many asymptomatic or paucisymptomatic individuals3,4 have not been tested and have become a major vehicle by which the infection has spread, first in China5 and then in the rest of the World. To overcome this issue, we set up an in-house protocol to pretreat swab samples before performing RT-qPCR. We compared results of the amplifications from automatedly extracted viral RNA with the swab-derived material treated as described in methods. As shown in Figure 1, Panel A, both samples displayed the same amplification profile with minimal differences. To further validate our data, we employed a technique with higher sensitivity, the droplet digital PCR (ddPCR). Using samples from the automated extraction and from our method, we found the same viral copies (i.e. 13000 copies/5µL) in both, showing that our in-house pretreatment protocol is a reliable and rapid method to assess SARS-CoV-2 infection directly from nasopharyngeal swabs (Fig. 1; Panel B). Furthermore, since the automated diagnostic extraction system used in this study requires 200µL swab-derived material as template, while our method only needs 100µL of material, we were able to detect twice as much of the viral genome for each µL of sample. Moreover, to assess correlation between our in-house RNA isolation method and the automated RNA extraction, linear regression was performed using ...

    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.

  3. Version published to 10.1101/2020.04.06.20054114v1 on medRxiv