Distinguishing COVID-19 infection and vaccination history by T cell reactivity

  1. Esther Dawen Yu
  2. Eric Wang
  3. Emily Garrigan
  4. Benjamin Goodwin
  5. Aaron Sutherland
  6. James Chang
  7. Rosa Isela Gálvez
  8. Jose Mateus
  9. Stephen A. Rawlings
  10. Davey M. Smith
  11. April Frazier
  12. Daniela Weiskopf
  13. Jennifer M. Dan
  14. Shane Crotty
  15. Alba Grifoni
  16. Ricardo da Silva Antunes
  17. Alessandro Sette

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Abstract

SARS-CoV-2 infection and COVID-19 vaccines elicit memory T cell responses. Here, we report the development of two new pools of E xperimentally-defined T cell epitopes derived from the non-spike R emainder of the SARS-CoV-2 proteome (CD4RE and CD8RE). The combination of T cell responses to these new pools and Spike (S) were used to discriminate four groups of subjects with different SARS-CoV-2 infection and COVID-19 vaccine status: non-infected, non-vaccinated (I−V−); infected and non-vaccinated (I+V−); infected and then vaccinated (I+V+); and non-infected and vaccinated (I−V+). The overall classification accuracy based on 30 subjects/group was 89.2% in the original cohort and 88.5% in a validation cohort of 96 subjects. The T cell classification scheme was applicable to different mRNA vaccines, and different lengths of time post-infection/post-vaccination. T cell responses from breakthrough infections (infected vaccinees, V+I+) were also effectively segregated from the responses of vaccinated subjects using the same classification tool system. When all five groups where combined, for a total of 239 different subjects, the classification scheme performance was 86.6%. We anticipate that a T cell-based immunodiagnostic scheme able to classify subjects based on their vaccination and natural infection history will be an important tool for longitudinal monitoring of vaccination and aid in establishing SARS-CoV−2 correlates of protection.

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  1. ScreenIT

    SciScore for 10.1101/2021.12.15.472874: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Human Subjects and PBMC isolation: The Institutional Review Boards of the University of California, San Diego (UCSD; 200236X) and the La Jolla Institute for Immunology (LJI; VD-214) approved the protocols used for blood collection for all the subjects who donated at all sites.
    Consent: Each participant provided informed consent and was assigned a study identification number with clinical information recorded.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    AntI−human IgG peroxidase antibody produced in goat (Sigma A6029) was used at a 1:5,000 dilution.
    AntI−human IgG peroxidase antibody
    suggested: None
    AntI−human IgG
    suggested: None
    The cells were stimulated with the different MPs analyzed (1ug/mL), PHA (10mg/mL), and DMSO (0.1%) in 96-well plates previously coated with antI−cytokine antibodies for IFNγ, (mAbs 1-D1K; Mabtech, Stockholm, Sweden) at a concentration of 10ug/mL.
    antI−cytokine
    suggested: None
    Subsequently, plates were washed again with PBS/0.05% Tween20 and incubated for 1 hour with fluorophore-conjugated antibodies (AntI−BAM-490).
    AntI−BAM-490
    suggested: None
    Software and Algorithms
    SentencesResources
    All samples were acquired on a ZE5 cell analyzer (Biorad laboratories, Hercules, CA) and analyzed with FlowJo software (Tree Star, Ashland, OR).
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Statistical analysis: Experimental data were analyzed by GraphPad Prism Version 9 (La Jolla, CA) and Microsoft Excel Version 16.16.27 (Microsoft, Redmond, WA).
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.12.15.472874v1 on bioRxiv