SARS-CoV-2 spike-specific memory B cells express higher levels of T-bet and FcRL5 after non-severe COVID-19 as compared to severe disease

  1. Raphael A. Reyes
  2. Kathleen Clarke
  3. S. Jake Gonzales
  4. Angelene M. Cantwell
  5. Rolando Garza
  6. Gabriel Catano
  7. Robin E. Tragus
  8. Thomas F. Patterson
  9. Sebastiaan Bol
  10. Evelien M. Bunnik

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Abstract

SARS-CoV-2 infection elicits a robust B cell response, resulting in the generation of long-lived plasma cells and memory B cells. Here, we aimed to determine the effect of COVID-19 severity on the memory B cell response and characterize changes in the memory B cell compartment between recovery and five months post-symptom onset. Using high-parameter spectral flow cytometry, we analyzed the phenotype of memory B cells with reactivity against the SARS-CoV-2 spike protein or the spike receptor binding domain (RBD) in recovered individuals who had been hospitalized with non-severe (n = 8) or severe (n = 5) COVID-19. One month after symptom onset, a substantial proportion of spike-specific IgG + B cells showed an activated phenotype. In individuals who experienced non-severe disease, spike-specific IgG + B cells showed increased expression of markers associated with durable B cell memory, including T-bet and FcRL5, as compared to individuals who experienced severe disease. While the frequency of T-bet + spike-specific IgG + B cells differed between the two groups, these cells predominantly showed an activated switched memory B cell phenotype in both groups. Five months post-symptom onset, the majority of spike-specific memory B cells had a resting phenotype and the percentage of spike-specific T-bet + IgG + memory B cells decreased to baseline levels. Collectively, our results highlight subtle differences in the B cells response after non-severe and severe COVID-19 and suggest that the memory B cell response elicited during non-severe COVID-19 may be of higher quality than the response after severe disease.

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  1. Version published to 10.1371/journal.pone.0261656
  2. ScreenIT

    SciScore for 10.1101/2021.09.24.461732: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This repository was reviewed and approved by the University of Texas Health Science Center at San Antonio Institutional Review Board.
    Consent: All study participants provided written informed consent prior to specimen collection for the repository to include collection of associated clinical information and use of left-over clinical specimens for research.
    Sex as a biological variablenot detected.
    RandomizationFeatures used for UMAP projection included mean fluorescence intensity of staining markers, excluding live/dead, RBD, spike1, and spike2 with default parameters (neighbors = 15, metric = Euclidean, random seed = 9889) and included all COVID-19 samples used in this study for initial projection.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The modified plasmids have been deposited to Addgene: #166856 and #166857 for spike and RBD, respectively.
    Addgene
    suggested: (Addgene, RRID:SCR_002037)
    FlowJo was used for gating and quantifying cell frequencies.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    All plasma samples were measured in duplicate and the average absorbance reading was used to calculate the area under the curve using GraphPad 9.
    GraphPad
    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.
    • 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.

  3. Version published to 10.1101/2021.09.24.461732v1 on bioRxiv