Rapid detection of SARS-CoV2 by Ambient Mass Spectrometry Techniques

  1. L. Ford
  2. D. Simon
  3. J. Balog
  4. N. Jiwa
  5. J. Higginson
  6. E. Jones
  7. S. Mason
  8. V. Wu
  9. E. Manoli
  10. S. M. Stavrakaki
  11. J. McKenzie
  12. D. McGill
  13. H. Koguna
  14. J. Kinross
  15. Z. Takats

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Abstract

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption – Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April – June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.07.20207647: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Recruitment of patients: Participants were prospectively recruited to this feasibility study within a sub-collection of Imperial College Tissue Bank, which acts under the HTA license 12275 and research ethics committee approval 17/WA/0161.
    Consent: All participants gave written and informed consent for inclusion in the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The pre-processed data matrix was exported from AMX into Anaconda Spyder Python 3.7 and the sklearn support vector machine linear SVC algorithm was used for feature selection with L1 penalty norm and C=500 regularization parameter for classifications.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Box plots of features were created using ggplot2 package in R.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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 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.

  2. Version published to 10.1101/2020.10.07.20207647v1 on medRxiv