Imprinted SARS-CoV-2-specific memory lymphocytes define hybrid immunity

  1. Lauren B. Rodda
  2. Peter A. Morawski
  3. Kurt B. Pruner
  4. Mitchell L. Fahning
  5. Christian A. Howard
  6. Nicholas Franko
  7. Jennifer Logue
  8. Julie Eggenberger
  9. Caleb Stokes
  10. Inah Golez
  11. Malika Hale
  12. Michael Gale
  13. Helen Y. Chu
  14. Daniel J. Campbell
  15. Marion Pepper

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Abstract

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  1. Version published to 10.1016/j.cell.2022.03.018
  2. ScreenIT

    SciScore for 10.1101/2022.01.12.22269192: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: All studies are approved by the University of Washington Human Subjects Division Institutional Review Board.
    Consent: Informed consent was obtained from all enrolled participants.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    (HAARVI) study (STUDY00000959), Healthy Adult Specimen Repository study (STUDY00002929) or COVID-19/SARS-CoV-2 Prevalence and Antibody Therapy Development study (Gale Lab, STUDY00009810).
    STUDY00000959
    suggested: None
    STUDY00002929
    suggested: None
    STUDY00009810
    suggested: None
    Secondary antibodies were diluted in dilution buffer as follows: anti-human IgG-HRP (Jackson ImmunoResearch) at 1:3000, anti-human IgM-HRP (Southern Biotech) at 1:3000, or anti-human IgA-HRP (Southern Biotech) at 1:1500.
    anti-human IgG-HRP
    suggested: None
    anti-human IgM-HRP
    suggested: (MyBioSource Cat# MBS673990, RRID:AB_10891687)
    anti-human IgA-HRP
    suggested: (SouthernBiotech Cat# 2050-05, RRID:AB_2687526)
    For surface phenotyping, cells were washed and barcoded using four different fluorescently labeled CD45 antibodies to create eight unique barcodes as previously described (Becht et al., 2021).
    CD45
    suggested: None
    For intracellular cytokine staining, cells were first incubated with anti-CXCR5 antibody at room temperature for 40 minutes.
    anti-CXCR5
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    After 1 hour incubation at 37°C, the plasma/virus mixtures were added to 96-well poly-L-lysine-coated plates seeded with human ACE2-expressing 293T cells (BEI Resources: NR-52511) 20 hours prior.
    293T
    suggested: None
    After 30 min of incubation at 37°C, the plasma/virus mixtures were added to 12 well plates of Vero cells and incubated for 1 h at 37°C, rocking every 15 min.
    Vero
    suggested: None
    Experimental Models: Organisms/Strains
    SentencesResources
    Percent neutralization was calculated as (1 – (sample+293T-ACE2+virus RLU - 293T+virus RLU)/(293T-ACE2+virus RLU - 293T+virus RLU) x 100.
    RLU - 293T+virus RLU)/ ( 293T-ACE2+virus RLU - 293T+virus RLU
    suggested: None
    Recombinant DNA
    SentencesResources
    GFP vector plasmid (BEI Resources; NR52516), a second generation helper plasmid pMD2.g (gift from Didier Trono; Addgene #12260), and either: the SARS-CoV-2(o) pseudotyping plasmid above, or a plasmid encoding the SARS-CoV-2(Δ) variant spike (Invivogen, San Diego, CA,USA; pLV-Spike-V8, B.1.617.2, delta).
    GFP
    suggested: RRID:Addgene_126657)
    pMD2
    suggested: None
    Software and Algorithms
    SentencesResources
    Data was analyzed in Prism (GraphPad).
    Prism
    suggested: (PRISM, RRID:SCR_005375)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    High-dimensional analysis of cytometry data: AIM-positive (CD154+CD69+) cells from all data files were concatenated with keywords and subjected to Phenograph clustering algorithm using k=40 nearest neighbors (Levine et al., 2015) and UMAP dimensionality reduction plugins using parameters IL-2, IFN-γ, IL-10, IL-4, IL-21, CD127, CD25, and CXCR5 in FlowJo 10 (Becton Dickinson).
    Phenograph
    suggested: (Phenograph, RRID:SCR_016919)
    FlowJo
    suggested: (FlowJo, RRID:SCR_008520)

    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.

  3. Version published to 10.1101/2022.01.12.22269192v1 on medRxiv