High speed large scale automated isolation of SARS-CoV-2 from clinical samples using miniaturized co-culture coupled with high content screening

  1. Rania Francis
  2. Marion Le Bideau
  3. Priscilla Jardot
  4. Clio Grimaldier
  5. Didier Raoult
  6. Jacques Yaacoub Bou Khalil
  7. Bernard La Scola

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

SARS-CoV-2, a novel coronavirus infecting humans, is responsible for the current COVID-19 global pandemic. If several strains could be isolated worldwide, especially for in-vitro drug susceptibility testing and vaccine development, few laboratories routinely isolate SARS-CoV-2. This is due to the fact that the current co-culture strategy is highly time consuming and requires working in a biosafety level 3 laboratory. In this work, we present a new strategy based on high content screening automated microscopy (HCS) allowing large scale isolation of SARS-CoV-2 from clinical samples in 1 week. A randomized panel of 104 samples, including 72 tested positive by RT-PCR and 32 tested negative, were processed with our HCS procedure and were compared to the classical isolation procedure. Isolation rate was 43 % with both strategies on RT-PCR positive samples, and was correlated with the initial RNA viral load in the samples, where we obtained a positivity threshold of 27 Ct. Co-culture delays were shorter with HCS strategy, where 80 % of the positive samples were recovered by the third day of co-culture, as compared to only 25 % with the classic strategy. Moreover, only the HCS strategy allowed us to recover all the positive elements after 1 week of co-culture. This system allows rapid and automated screening of clinical samples with minimal operator work load, thus reducing the risks of contamination.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.14.097295: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the local ethics committee of IHU (Institut Hospitalo-Universitaire) - Méditerranée Infection (No. 2020-01).
    RandomizationLarge scale co-culture of clinical samples: We applied this strategy for the detection of SARS-CoV-2 in randomly chosen 104 anonymized nasopharyngeal swab samples.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    50 µl were then inoculated on a monolayer of Vero E6 cells cultured in 96-well microplates.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical analysis: The R Studio® and XLSTAT software were used to perform all statistical tests included in this paper. 6.
    XLSTAT
    suggested: (XLSTAT, RRID:SCR_016299)

    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:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • 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 10.1101/2020.05.14.097295 on bioRxiv