Evaluation of a Rapid and Accessible Reverse Transcription-Quantitative PCR Approach for SARS-CoV-2 Variant of Concern Identification

  1. Priscilla S.-W. Yeung
  2. Hannah Wang
  3. Mamdouh Sibai
  4. Daniel Solis
  5. Fumiko Yamamoto
  6. Naomi Iwai
  7. Becky Jiang
  8. Nathan Hammond
  9. Bernadette Truong
  10. Selamawit Bihon
  11. Suzette Santos
  12. Marilyn Mar
  13. Claire Mai
  14. Kenji O. Mfuh
  15. Jacob A. Miller
  16. ChunHong Huang
  17. Malaya K. Sahoo
  18. James L. Zehnder
  19. Benjamin A. Pinsky

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

The ability to distinguish between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) is of ongoing interest due to differences in transmissibility, responses to vaccination, clinical prognosis, and therapy. Although detailed genetic characterization requires whole-genome sequencing (WGS), targeted nucleic acid amplification tests can serve a complementary role in clinical settings, as they are more rapid and accessible than sequencing in most laboratories.

Article activity feed

  1. Version published to 10.1128/jcm.00178-22
  2. ScreenIT

    SciScore for 10.1101/2022.02.07.22270629: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was conducted with Stanford institutional review board approval (protocol 57519), and individual consent was waived.
    Consent: This study was conducted with Stanford institutional review board approval (protocol 57519), and individual consent was waived.
    Sex as a biological variablenot detected.
    RandomizationSamples with non-dominant variant typing by RT-qPCR were prioritized for sequencing, with the remaining isolates chosen randomly to fill a sequencing run.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Testing was performed at Stanford Clinical Virology Laboratory, which provides virologic testing for all Stanford-affiliated hospitals and outpatient centers in the San Francisco Bay Area.
    Stanford Clinical Virology Laboratory
    suggested: None
    Genomes were assembled via a custom assembly and bioinformatics pipeline using NCBI NC_045512.2 as reference.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    WGS data was deposited in GISAID (Supplemental Table 4).
    WGS
    suggested: None

    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:
    This RT-qPCR approach has several limitations as evidenced by its assay failure rate of 14% across all tested samples in our initial cohort. Because multiplex RT-qPCRs involve a mixture of multiple sets of primers and probes, they are inherently less sensitive than single-target assays. For samples with RNA concentrations near the lower limits of detection, freeze-thaw cycles could impact RNA stability, and may not yield consistent results due to stochastic variation. This issue could be alleviated by implementing a Ct/RLU filter to only genotype samples most likely to yield interpretable results. Within our 1,093 sample cohort, the lower assay failure rate in samples tested in our clinical virology laboratory (4%) compared to near-care settings (35%) is likely attributable to genotyping only specimens with higher viral RNA levels. Note, however, that even with such filtering, mutation analysis by RT-qPCR remains more sensitive than WGS. The other limitation to this approach is the rapidly changing variant landscape which may render such an assay obsolete in the matter of weeks. However, the inclusion of multiple targets in key residues that influence viral fitness helps guard against this possibility, as evidenced by our ability to detect the emergence of Omicron variant in our population. Still, flexibility and vigilance are required to re-design and re-validate these types of assays as novel variants emerge. In summary, we developed and validated a two-reaction multiplex R...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2022.02.07.22270629 on medRxiv