SARS-CoV-2–specific T cells in unexposed adults display broad trafficking potential and cross-react with commensal antigens

  1. Laurent Bartolo
  2. Sumbul Afroz
  3. Yi-Gen Pan
  4. Ruozhang Xu
  5. Lea Williams
  6. Chin-Fang Lin
  7. Ceylan Tanes
  8. Kyle Bittinger
  9. Elliot S. Friedman
  10. Phyllis A. Gimotty
  11. Gary D. Wu
  12. Laura F. Su

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Abstract

The baseline composition of T cells directly affects later response to pathogens, but the complexity of precursor states remains poorly defined. Here, we examined the baseline state of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–specific T cells in unexposed individuals. SARS-CoV-2–specific CD4 + T cells were identified in prepandemic blood samples by major histocompatibility complex (MHC) class II tetramer staining and enrichment. Our data revealed a substantial number of SARS-CoV-2–specific T cells that expressed memory phenotype markers. Integrated phenotypic analyses demonstrated diverse preexisting memory states that included cells with distinct polarization features and trafficking potential to barrier tissues. T cell clones generated from tetramer-labeled cells cross-reacted with antigens from commensal bacteria in the skin and gastrointestinal tract. Direct ex vivo tetramer staining for one spike-specific population showed a similar level of cross-reactivity to sequences from endemic coronavirus and commensal bacteria. These data highlight the complexity of precursor T cell repertoire and implicate noninfectious exposures to common microbes as a key factor that shapes human preexisting immunity to SARS-CoV-2.

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  1. Version published to 10.1126/sciimmunol.abn3127
  2. ScreenIT

    SciScore for 10.1101/2021.11.29.470421: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For MHC blocking experiments, anti-MHC class II antibodies, L243 (0.37ug/ul, BioLegend) and TU39 (0.01ug/ul, BioLegend), were added to T cells before combining the cells with DCs.
    anti-MHC class II
    suggested: (Bio X Cell Cat# BE0306, RRID:AB_2736986)
    Software and Algorithms
    SentencesResources
    Non-zero populations were included in the analyses performed using FlowJo (BD)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    After stimulation, intracellular cytokine staining with anti-TNF-α (BioLegend) was performed using BD Cytofix/Cytoperm Fixation/Permeabilization Kit according to manufacturer protocol (BD)
    BD Cytofix/Cytoperm
    suggested: None
    Sequence similarity calculation: All spike and non-spike sequence of SARS-CoV2 and common corona viruses were derived from NCBI database.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    Pair-wise alignment using Clustal Omega was performed to identify aligned regions.
    Clustal Omega
    suggested: (Clustal Omega, RRID:SCR_001591)
    A total of 798 tetramer+ cells were read into R by flowCore and combined into one single dataset for the following data processing and high-dimensional analyses.
    flowCore
    suggested: (flowCore, RRID:SCR_002205)
    Clustering was performed using Phenograph with nearest neighbors set to 60 (k = 60) (44).
    Phenograph
    suggested: (Phenograph, RRID:SCR_016919)
    Statistical analyses were performed using GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

    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.

  3. Version published to 10.1101/2021.11.29.470421 on bioRxiv