SARS-CoV-2 antibody profile of naturally infected and vaccinated individuals detected using qualitative, semi-quantitative and multiplex immunoassays

  1. Jamie Meyers
  2. Anne Windau
  3. Christine Schmotzer
  4. Elie Saade
  5. Jaime Noguez
  6. Lisa Stempak
  7. Xiaochun Zhang

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Abstract

No abstract available

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  1. Version published to 10.1016/j.diagmicrobio.2022.115803
  2. ScreenIT

    SciScore for 10.1101/2022.01.24.22269699: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: All samples were utilized according to University Hospitals Institutional Review Board policies governing the use of residual material for assay validation.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Bioplex 2200 semi-quantitative SARS-CoV-2 IgG multiplex panel (Bio-Rad, CA) detects and differentiates antibodies against the RBD, S1, S2, and nucleocapsid protein.
    S2
    suggested: None
    The Atellica SARS-CoV-2 semi-quantitative IgG assay and qualitative total antibody assay measure anti-RBD IgG and total antibodies, respectively (Siemens, NY) with a 1.0 index value as assay cutoff for positivity.
    anti-RBD IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    The recoveredgroup included 61 healthcare workers who participated in a serosurveillance research study (IRB number: STUDY20200608) and were identified as past SARS-CoV-2 infection by either a positive result (n=52) of the IgG anti-N protein assay (Abbott, IL) or negative serology, but with a self-reported positive PCR result.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    STATISTICAL METHODS: Statistical analyses were performed using SigmaPlot software.
    SigmaPlot
    suggested: (SigmaPlot, RRID:SCR_003210)

    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: We detected the following sentences addressing limitations in the study:
    A potential limitation of the study is that antibody activity does not encompass the entirety of the immune responses. T-cell immunity also plays an important role. Both COVID infection and vaccination can induce CD4 and CD8 T cell responses. Additionally, viral variants included in this study are predominantly the original SARS-CoV-2 strain and the strain with D614G mutation. Humoral response to infection with Delta or Omicron variant may be different. In conclusion, individuals fully vaccinated with mRNA vaccine mounted strong humoral immunity with much higher anti-RBD1, anti-S1, and anti-S2 antibody levels compared to the naturally infected individuals. The results strongly support that all eligible individuals should be vaccinated, regardless of history of COVID-19 infection. Additionally, semi-quantitative anti-RBD/S assays that can quantify antibody levels with a broad reportable range are potentially useful in evaluating post-vaccination and/or post-infection immunity if a linear correlation with neutralizing antibody titers is achievable. Standardization and harmonization in quantitation and reporting are necessary for these assays to be useful in clinical practice.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2022.01.24.22269699v1 on medRxiv