Simultaneous identification of viruses and viral variants with programmable DNA nanobait

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Abstract

Respiratory infections are the major cause of death from infectious disease worldwide. Multiplexed diagnostic approaches are essential as many respiratory viruses have indistinguishable symptoms. We created self-assembled DNA nanobait that can simultaneously identify multiple short RNA targets. The nanobait approach relies on specific target selection via toehold-mediated strand displacement and rapid read-out via nanopore sensing. Here, we show this platform can concurrently identify several common respiratory viruses, detecting a panel of short targets of viral nucleic acids from multiple viruses. Our nanobait can be easily reprogrammed to discriminate viral variants, as we demonstrated for several key SARS-CoV-2 variants with single-nucleotide resolution. Lastly, we show that nanobait discriminates between samples extracted from oropharyngeal swabs from negative and positive SARS-CoV-2 patients without pre-amplification. Our system allows for multiplexed identification of native RNA molecules, providing a new scalable approach for diagnostics of multiple respiratory viruses in a single assay.

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  1. SciScore for 10.1101/2021.11.05.21265890: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Excess oligonucleotides were removed using Amicon® ultra 0.5 mL centrifugal filters with 100 kilodalton (kDa) cutoff with the washing buffer (10 mM Tris-HCl pH 8.0, 0.5 mM MgCl2).
    Amicon®
    suggested: None
    Image visualization and analysis were performed using Gwyddion.
    Gwyddion
    suggested: (Gwyddion, RRID:SCR_015583)

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.