Generation of tonsil organoids as an ex vivo model for SARS-CoV-2 infection

  1. Han Kyung Kim
  2. Hyeryeon Kim
  3. Myoung Kyu Lee
  4. Woo Hee Choi
  5. Yejin Jang
  6. Jin Soo Shin
  7. Jun-Yeol Park
  8. Seong-In Hyun
  9. Kang Hyun Kim
  10. Hyun Wook Han
  11. Meehyein Kim
  12. Young Chang Lim
  13. Jongman Yoo

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Abstract

Palatine tonsil (hereinafter referred to as “tonsil”) plays role in the immune system’s first line of defense against foreign pathogens. Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide pandemic since the infection was first reported in China in December 2019. The aim of this study was to establish tonsil epithelial cell-derived organoids and to examine their feasibility as an ex vivo model for SARS-CoV-2 infection. Using an optimized protocol, we achieved 3D tonsil organoid culture from human tonsil tissue that reflects the distinctive characteristics of the tonsil epithelium, such as its cellular composition, histologic properties, and molecular biological features. Notably, we verified that SARS-CoV-2 can infect tonsil organoids with a robust replication efficiency. Furthermore, treatment with remdesivir, an antiviral agent, effectively protected them from viral infection. Therefore, tonsil organoids could be available for investigation of SARS-CoV-2 infection-mediated pathology and for preclinical screening of novel antiviral drug candidates.

One-sentence Summary

This study established tonsil epithelial cell-derived organoids and demonstrated their feasibility as an ex vivo model for SARS-CoV-2 infection.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.06.239574: (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

    Software and Algorithms
    SentencesResources
    Materials and Methods:
    Methods
    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a protocol registration statement.

    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. ScreenIT

    SciScore for 10.1101/2020.08.06.239574: (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: 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 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.

    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.

  3. Version published to 10.1101/2020.08.06.239574v1 on bioRxiv