Longitudinal analysis of T cell receptor repertoires reveals shared patterns of antigen-specific response to SARS-CoV-2 infection

  1. Rachel M. Gittelman
  2. Enrico Lavezzo
  3. Thomas M. Snyder
  4. H. Jabran Zahid
  5. Cara L. Carty
  6. Rebecca Elyanow
  7. Sudeb Dalai
  8. Ilan Kirsch
  9. Lance Baldo
  10. Laura Manuto
  11. Elisa Franchin
  12. Claudia Del Vecchio
  13. Monia Pacenti
  14. Caterina Boldrin
  15. Margherita Cattai
  16. Francesca Saluzzo
  17. Andrea Padoan
  18. Mario Plebani
  19. Fabio Simeoni
  20. Jessica Bordini
  21. Nicola I. Lorè
  22. Dejan Lazarević
  23. Daniela M. Cirillo
  24. Paolo Ghia
  25. Stefano Toppo
  26. Jonathan M. Carlson
  27. Harlan S. Robins
  28. Andrea Crisanti
  29. Giovanni Tonon

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Abstract

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  1. Version published to 10.1172/jci.insight.151849
  2. ScreenIT

    SciScore for 10.1101/2020.11.09.20228023: (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
    Antibody response was measured using three commercial serology testing kits: LIAISON SARS-CoV-2 S1/S2 IgG (DiaSorin), Elecsys Anti-SARS-CoV-2 assay (Roche Diagnostics) and ARCHITECT SARS-CoV-2 IgG (Abbott).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    We calculate the Spearman rank correlations between antibody titers and CD4+ and CD8+ T-cell response using the Pingouin package in Python (Vallat 2018) and report the two-sided significance.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    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 10.1101/2020.11.09.20228023v2 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.11.09.20228023: (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: Thank you for sharing your code and data.

    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.

    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.

  5. ScreenIT

    SciScore for 10.1101/2020.11.09.20228023: (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: Thank you for sharing your data.

    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.

    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.

  6. Version published to 10.1101/2020.11.09.20228023v1 on medRxiv