Sensitive one-step isothermal detection of pathogen-derived RNAs

  1. Chang Ha Woo
  2. Sungho Jang
  3. Giyoung Shin
  4. Gyoo Yeol Jung
  5. Jeong Wook Lee

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Abstract

The recent outbreaks of Ebola, Zika, MERS, and SARS-CoV-2 (2019-nCoV) require fast, simple, and sensitive onsite nucleic acid diagnostics that can be developed rapidly to prevent the spread of diseases. We have developed a SENsitive Splint-based one-step isothermal RNA detection (SENSR) method for rapid and straightforward onsite detection of pathogen RNAs with high sensitivity and specificity. SENSR consists of two simple enzymatic reactions: a ligation reaction by SplintR ligase and subsequent transcription by T7 RNA polymerase. The resulting transcript forms an RNA aptamer that induces fluorescence. Here, we demonstrate that SENSR is an effective and highly sensitive method for the detection of the current epidemic pathogen, severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2). We also show that the platform can be extended to the detection of five other pathogens. Overall, SENSR is a molecular diagnostic method that can be developed rapidly for onsite uses requiring high sensitivity, specificity, and short assaying times.

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    SciScore for 10.1101/2020.03.05.20031971: (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.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    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.

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  2. Version published to 10.1101/2020.03.05.20031971v2 on medRxiv
  3. Version published to 10.1101/2020.03.05.20031971v1 on medRxiv