Extracellular Vesicles Analysis in the COVID-19 Era: Insights on Serum Inactivation Protocols Towards Downstream Isolation and Analysis

  1. Roberto Frigerio
  2. Angelo Musicò
  3. Marco Brucale
  4. Andrea Ridolfi
  5. Silvia Galbiati
  6. Riccardo Vago
  7. Greta Bergamaschi
  8. Anna Ferretti
  9. Marcella Chiari
  10. Francesco Valle
  11. Alessandro Gori
  12. Marina Cretich

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Abstract

Since the outbreak of COVID-19 crisis, the handling of biological samples from confirmed or suspected SARS-CoV-2 positive individuals demanded the use of inactivation protocols to ensure laboratory operators safety. While not standardized, these practices can be roughly divided in two categories, namely heat inactivation and solvent-detergent treatments. As such, these routine procedures should also apply to samples intended for Extracellular Vesicles (EVs) analysis. Assessing the impact of virus inactivating pre-treatments is therefore of pivotal importance, given the well-known variability introduced by different pre-analytical steps on downstream EVs isolation and analysis. Arguably, shared guidelines on inactivation protocols tailored to best address EVs-specific requirements will be needed among the EVs community, yet deep investigations in this direction haven’t been reported so far.

In the attempt of sparking interest on this highly relevant topic, we here provide preliminary insights on SARS-CoV-2 inactivation practices to be adopted prior serum EVs analysis by comparing solvent/detergent treatment vs. heat inactivation. Our analysis entailed the evaluation of EVs recovery and purity along with biochemical, biophysical and biomolecular profiling by means of Nanoparticle Tracking Analysis, Western Blotting, Atomic Force Microscopy, miRNA content (digital droplet PCR) and tetraspanin assessment by microarrays. Our data suggest an increase in ultracentrifugation (UC) recovery following heat-treatment, however accompanied by a marked enrichment in EVs-associated contaminants. On the contrary, solvent/detergent treatment is promising for small EVs (< 150 nm range), yet a depletion of larger vesicular entities was detected. This work represents a first step towards the identification of optimal serum inactivation protocols targeted to EVs analysis.

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  1. Version published to 10.1101/2020.12.10.417758v3 on bioRxiv
  2. Version published to 10.1101/2020.12.10.417758v2 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.12.10.417758: (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

    Antibodies
    SentencesResources
    Subsequently, the samples were removed and the slides were washed with washing buffer and incubated with anti-CD9-Biotin, anti-CD63-Biotin and anti-CD81-Biotin antibodies (Ancell) 0.1mg/mL for 1 hour.
    anti-CD9-Biotin
    suggested: (Sigma-Aldrich Cat# SAB4700094, RRID:AB_11129622)
    anti-CD63-Biotin
    suggested: None
    anti-CD81-Biotin
    suggested: None
    Software and Algorithms
    SentencesResources
    A syringe pump with constant flow injection was used and three videos of 60 s were captured and analyzed with NTA software version 3.2.
    NTA
    suggested: None
    The concentration of the target was calculated automatically by the QuantaSoft™ software version 1.7.4 (Bio-Rad).
    QuantaSoft™
    suggested: None

    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.
    • No funding statement was detected.
    • 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.

  4. Version published to 10.1101/2020.12.10.417758v1 on bioRxiv