Rapid and accurate identification of SARS-CoV-2 Omicron variants using droplet digital PCR (RT-ddPCR)

  1. Margaret G. Mills
  2. Pooneh Hajian
  3. Shah Mohamed Bakhash
  4. Hong Xie
  5. Derrek Mantzke
  6. Haiying Zhu
  7. Garrett A. Perchetti
  8. Meei-Li Huang
  9. Gregory Pepper
  10. Keith R. Jerome
  11. Pavitra Roychoudhury
  12. Alexander L. Greninger

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Abstract

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  1. Version published to 10.1016/j.jcv.2022.105218
  2. ScreenIT

    SciScore for 10.1101/2022.01.11.22268981: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: This study was approved under a waiver of consent by the University of Washington institutional review board.
    IRB: This study was approved under a waiver of consent by the University of Washington institutional review board.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Sample extraction: Total nucleic acids were extracted from nasal/pharyngeal and nasal swabs using either Roche MagNA Pure 96 instrument and DNA & Viral NA Small Volume kit or ThermoFisher KingFisher according to manufacturer instructions.
    ThermoFisher KingFisher
    suggested: None
    [41,42] Sequencing libraries were prepared using multiplexed amplicon panels from Swift Biosciences or Illumina COVIDSeq.
    Swift Biosciences
    suggested: None
    Data analysis was conducted with QuantaSoft Pro 1.0.596 version software, using two methods.
    QuantaSoft Pro
    suggested: None
    One high-concentration clinical specimen from each lineage was identified in the UWVL SARS-CoV-2 repository based on NGS results, and diluted in PBS into high-concentration (∼5000 copies/µl) mutation controls.
    NGS
    suggested: (ANGSD, RRID:SCR_021865)

    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:
    As is the case for all PCR-based genotyping methods, there are limitations to the utility of this RT-ddPCR assay. The example of ACG->ACA mutation in K417T illustrates that assay accuracy is subject to change with additional mutations. There is also no guarantee that the particular set of mutations of concern detected by the assay will continue to be mutations of concern in the future. However, direct targeting of those mutations of concern, rather than relying on larger-but-less-relevant genomic changes, increases the likelihood that the assay will continue to be useful to researchers and clinicians alike. Reliance on other changes like the 69-70 deletion increases the chances of false positives (as in the case of the SGTF-Delta specimen we identified) or false negatives (as in the case of the non-SGTF Omicron specimens that have been identified in several countries[50]). We have previously used RT-ddPCR to identify SARS-CoV-2 Alpha variant in clinical specimens, but here we expand both the number of mutational lineages that can be tracked with the assay and our understanding of how robust the assay is. It can accommodate a wide range of sample concentrations, coinfections, and other confounds, and still yields an accurate determination of SARS-CoV-2 mutations quickly enough to enable clinical decision-making.

    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.

    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 10.1101/2022.01.11.22268981v1 on medRxiv