Maturation and persistence of the anti-SARS-CoV-2 memory B cell response

  1. Aurélien Sokal
  2. Pascal Chappert
  3. Anais Roeser
  4. Giovanna Barba-Spaeth
  5. Slim Fourati
  6. Imane Azzaoui
  7. Alexis Vandenberghe
  8. Ignacio Fernandez
  9. Magali Bouvier-Alias
  10. Etienne Crickx
  11. Asma Beldi Ferchiou
  12. Sophie Hue
  13. Laetitia Languille
  14. Samia Baloul
  15. France Noizat-Pirenne
  16. Marine Luka
  17. Jérôme Megret
  18. Mickaël Ménager
  19. Jean-Michel Pawlotsky
  20. Simon Fillatreau
  21. Felix A Rey
  22. Jean-Claude Weill
  23. Claude-Agnès Reynaud
  24. Matthieu Mahévas

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Memory B cells play a fundamental role in host defenses against viruses, but to date, their role have been relatively unsettled in the context of SARS-CoV-2. We report here a longitudinal single-cell and repertoire profiling of the B cell response up to 6 months in mild and severe COVID-19 patients. Distinct SARS-CoV-2 Spike-specific activated B cell clones fueled an early antibody-secreting cell burst as well as a durable synchronous germinal center response. While highly mutated memory B cells, including preexisting cross-reactive seasonal Betacoronavirus-specific clones, were recruited early in the response, neutralizing SARS-CoV-2 RBD-specific clones accumulated with time and largely contributed to the late remarkably stable memory B-cell pool. Highlighting germinal center maturation, these cells displayed clear accumulation of somatic mutations in their variable region genes over time. Overall, these findings demonstrate that an antigen-driven activation persisted and matured up to 6 months after SARS-CoV-2 infection and may provide long-term protection.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.17.385252: (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.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Anti-S and anti-N commercial assays: Serological assays were performed for IgG anti-N, IgG anti-S and total antibodies (ab) anti-S detection
    Anti-S
    suggested: None
    anti-N
    suggested: None
    anti-N , IgG
    suggested: None
    Oligonucleotide conjugation of anti-His antibody: Unconjugated anti-His antibodies were purchased at Biolegend and custom oligonucleotides were ordered at Integrated DNA Technologies, following the 10Xgenomics protocol available at https://support.10xgenomics.com/single-cell-gene-expression/overview/doc/demonstratedprotocol-cell-surface-protein-labeling-for-single-cell-rna-sequencing-protocols and the barcode whitelist.
    anti-His
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Vero E6 cells were seeded at 2×104cells/well in a 96-well plate 24h before the assay.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Serum samples were processed for anti-Nucloprotein (N) detection on Abbott SARS-CoV-2 IgG chemiluminescent microparticle immunoassay following the manufacturer’s instructions.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Data were analyzed with FlowJo or Kaluza softwares.
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)
    Kaluza
    suggested: (Kaluza, RRID:SCR_016182)
    FlowSOM plugin was used in parallel on the same downsampled dataset to create a self-organizing map (using n = 9 clusters as default parameter) that was then applied to the initial FCS files from all 83 samples to calculate total and spike-specific memory B cell repartition in identified clusters.
    FlowSOM
    suggested: (FlowSOM, RRID:SCR_016899)
    Sequence quality was verified with the CodonCode Aligner software (CodonCode Corporation) and data were analyzed with the IMGT/HighV-QUEST web portal (from The International Immunogenetics Information System) or in parallel with the VDJ sequences generated as part of our scRNA-seq dataset (see below)
    CodonCode Aligner
    suggested: None
    Single-cell gene expression analysis: Paired-end FASTQ reads for all three libraries were demultiplexed and aligned against the GRCh38 human reference genome (GENCODE v32/Ensembl 98; July 2020) using 10x Genomics’ Cell Ranger v4.0.0 pipeline.
    GENCODE
    suggested: (GENCODE, RRID:SCR_014966)
    Outputs of Cell Ranger were directly loaded into Seurat v3.2.2 (Stuart et al., 2019) for further QC steps and analysis.
    Seurat
    suggested: (SEURAT, RRID:SCR_007322)
    Computational analyses of VDJ sequences: Processed FASTA sequences from cultured single-cell heavy chain sequencing, 10x single-cell RNA sequencing and 874 published SARS-CoV-2 RBD and/or S-specific antibodies (Brouwer et al., 2020; Kreer et al., 2020; Liu et al., 2020; Robbiani et al., 2020; Seydoux et al., 2020; Shi et al., 2020; Wec et al., 2020; Zost et al., 2020) were annotated using Igblast v1.16.0 against the human IMGT reference database.
    Igblast
    suggested: (IgBLAST, RRID:SCR_002873)
    Mutation frequencies in V genes were then calculated using the calcObservedMutations() function from Immcantation/SHazaM v1.0.2 R package.
    Immcantation/SHazaM
    suggested: None
    Graphics were obtained using the ggplot2 v3.3.2 and circlize v0.4.10 packages.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    circlize
    suggested: (circlize, RRID:SCR_002141)
    v4.0.0 docker image) and further visualize in R using the Alakazam v1.0.2 and igraph v1.2.6 packages.
    igraph
    suggested: (igraph, RRID:SCR_019225)
    Neutralization curves and 50% FRNT values were calculated by nonlinear regression analysis using Prism 6, GraphPad software.
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    Statistical analyses involved use of GraphPad Prism 8.0 (La Jolla, CA, USA).
    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: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04402892Not yet recruitingCOVID-19: SARS-CoV-2 Specific Memory B and T-CD4+ Cells

    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.

  2. Version published to 10.1101/2020.11.17.385252 on bioRxiv