Mapping Systemic Inflammation and Antibody Responses in Multisystem Inflammatory Syndrome in Children (MIS-C)

  1. Conor N. Gruber
  2. Roosheel S. Patel
  3. Rebecca Trachtman
  4. Lauren Lepow
  5. Fatima Amanat
  6. Florian Krammer
  7. Karen M. Wilson
  8. Kenan Onel
  9. Daniel Geanon
  10. Kevin Tuballes
  11. Manishkumar Patel
  12. Konstantinos Mouskas
  13. Timothy O’Donnell
  14. Elliot Merritt
  15. Nicole W. Simons
  16. Vanessa Barcessat
  17. Diane M. Del Valle
  18. Samantha Udondem
  19. Gurpawan Kang
  20. Charuta Agashe
  21. Neha Karekar
  22. Joanna Grabowska
  23. Kai Nie
  24. Jessica Le Berichel
  25. Hui Xie
  26. Noam Beckmann
  27. Sandeep Gangadharan
  28. George Ofori-Amanfo
  29. Uri Laserson
  30. Adeeb Rahman
  31. Seunghee Kim-Schulze
  32. Alexander W. Charney
  33. Sacha Gnjatic
  34. Bruce D. Gelb
  35. Miriam Merad
  36. Dusan Bogunovic

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Abstract

No abstract available

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  1. Version published to 10.1016/j.cell.2020.09.034
  2. ScreenIT

    SciScore for 10.1101/2020.07.04.20142752: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Sample collection: Written informed consent for all individuals in this study was provided in compliance with an institutional review board protocol.
    IRB: Sample collection: Written informed consent for all individuals in this study was provided in compliance with an institutional review board protocol.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableHealthy volunteers were age-matched to the extent possible, including a 3 year-old female, 6 year-old female, 7 year old male, 12 year-old male, and 19 year-old female.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    To assess the distribution of different immunoglobulin isotypes, assays were performed separately with anti-human IgA-AP antibody, anti-human IgM-AP antibody, anti-human IgG1 Fc-AP, anti-human IgG2 Fc-AP, anti-human IgG3 hinge-AP and anti-human IgG4 Fc-AP.
    anti-human IgG4
    suggested: None
    Following fixation, the cells were washed, permeabilized with 0.1% Triton X-100, blocked in a 3% milk solution (American Bio) and stained with a monoclonal antibody to anti-SARS nucleoprotein (Amanat 2020) and subsequently a goat anti-mouse IgG–HRP (Rockland Immunochemicals).
    anti-SARS nucleoprotein
    suggested: None
    a goat anti-mouse IgG–HRP (Rockland Immunochemicals)
    suggested: None
    anti-mouse IgG–HRP
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    This virus–serum mixture was then added to Vero E6 cells (ATCC) plated in a 96-well cell culture plate and incubated at 37 °C for 1 hour.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    FCS files were analyzed using Cytobank.
    Cytobank
    suggested: (Cytobank, RRID:SCR_014043)
    Quantile normalized matrices were read into R statistical environment (4.0.1) and analyzed using the DESeq2 and limma R packages.
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    limma
    suggested: (LIMMA, RRID:SCR_010943)
    Overlap of enriched samples were visualized by the UpSetR package and tile plot and heat map visualizations were drawn by the ggplot2 software.
    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.

  3. Version published to 10.1101/2020.07.04.20142752v1 on medRxiv