Development of a high‐sensitivity ELISA detecting IgG, IgA and IgM antibodies to the SARS‐CoV‐2 spike glycoprotein in serum and saliva

  1. Sian E. Faustini
  2. Sian E. Jossi
  3. Marisol Perez‐Toledo
  4. Adrian M. Shields
  5. Joel D. Allen
  6. Yasunori Watanabe
  7. Maddy L. Newby
  8. Alex Cook
  9. Carrie R. Willcox
  10. Mahboob Salim
  11. Margaret Goodall
  12. Jennifer L. Heaney
  13. Edith Marcial‐Juarez
  14. Gabriella L. Morley
  15. Barbara Torlinska
  16. David C. Wraith
  17. Tonny V. Veenith
  18. Stephen Harding
  19. Stephen Jolles
  20. Mark J. Ponsford
  21. Tim Plant
  22. Aarnoud Huissoon
  23. Matthew K. O'Shea
  24. Benjamin E. Willcox
  25. Mark T. Drayson
  26. Max Crispin
  27. Adam F. Cunningham
  28. Alex G. Richter

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Abstract

Detecting antibody responses during and after SARS‐CoV‐2 infection is essential in determining the seroepidemiology of the virus and the potential role of antibody in disease. Scalable, sensitive and specific serological assays are essential to this process. The detection of antibody in hospitalized patients with severe disease has proven relatively straightforward; detecting responses in subjects with mild disease and asymptomatic infections has proven less reliable. We hypothesized that the suboptimal sensitivity of antibody assays and the compartmentalization of the antibody response may contribute to this effect. We systematically developed an ELISA, optimizing different antigens and amplification steps, in serum and saliva from non‐hospitalized SARS‐CoV‐2‐infected subjects. Using trimeric spike glycoprotein, rather than nucleocapsid, enabled detection of responses in individuals with low antibody responses. IgG1 and IgG3 predominate to both antigens, but more anti‐spike IgG1 than IgG3 was detectable. All antigens were effective for detecting responses in hospitalized patients. Anti‐spike IgG, IgA and IgM antibody responses were readily detectable in saliva from a minority of RT‐PCR confirmed, non‐hospitalized symptomatic individuals, and these were mostly subjects who had the highest levels of anti‐spike serum antibodies. Therefore, detecting antibody responses in both saliva and serum can contribute to determining virus exposure and understanding immune responses after SARS‐CoV‐2 infection.

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  1. Version published to 10.1111/imm.13349
  2. ScreenIT

    SciScore for 10.1101/2020.06.16.20133025: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the London - Camden & Kings Cross Research Ethics Committee reference 20/HRA/1817.
    Consent: All participants in both studies provided written, informed consent prior to their enrolment.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Anti-human immunoglobulin antibodies (anti-IgG, clone R-10 1:8000; anti-IgA clones, 2D7 1:2000 and MG4.156 1:4000;
    Anti-human immunoglobulin
    suggested: None
    anti-IgG
    suggested: (Vector Laboratories Cat# BA-8000, RRID:AB_2336140)
    anti-IgA
    suggested: (LSBio (LifeSpan Cat# LS-C68423-2000, RRID:AB_10634906)
    The data was classed as positive and negative based on the cut-off established from the concentrations of pre2019 samples assuming that the values of the mean + 3 standard deviations were biologically plausible to be negative for anti-SARS-CoV-2 antibodies (Cutt-off values 0.349 in saliva and 0.629 in serum).
    anti-SARS-CoV-2
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    This construct was used to transiently transfect 293T cells cultured in Opti-MEM (ThermoFisher Scientific) in 2L roller bottles using Polyethylenimine (PEI) linear (Polysciences, Inc, USA).
    293T
    suggested: None
    SARS-CoV-2 spike glycoprotein expression and purification: Expression plasmid encoding SARS-CoV-2 S glycoprotein 9 was transiently transfected into Human Embryonic Kidney (HEK) 293F cells.
    HEK
    suggested: RRID:CVCL_6642)
    293F
    suggested: RRID:CVCL_D615)
    Software and Algorithms
    SentencesResources
    Patient cohorts and ethical review: Paired serum and saliva samples were collected from health care workers at University Hospitals Birmingham NHS Foundation Trust as part of the CoCo study.
    CoCo
    suggested: (CoCo, RRID:SCR_010947)
    The agreement between the classification of saliva and serum samples was assessed using kappa statistics with STATA 16.1
    STATA
    suggested: (Stata, RRID:SCR_012763)
    1 (StataCorp LLC, USA).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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.

    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 10.1101/2020.06.16.20133025v1 on medRxiv