Two-dimensional multiplexed assay for rapid and deep SARS-CoV-2 serology profiling and for machine learning prediction of neutralization capacity

  1. Akiko Koide
  2. Tatyana Panchenko
  3. Chan Wang
  4. Sara A. Thannickal
  5. Larizbeth A. Romero
  6. Kai Wen Teng
  7. Francesca-Zhoufan Li
  8. Padma Akkappedi
  9. Alexis D. Corrado
  10. Jessica Caro
  11. Catherine Diefenbach
  12. Marie I. Samanovic
  13. Mark J. Mulligan
  14. Takamitsu Hattori
  15. Kenneth A. Stapleford
  16. Huilin Li
  17. Shohei Koide

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Abstract

Antibody responses serve as the primary protection against SARS-CoV-2 infection through neutralization of viral entry into cells. We have developed a two-dimensional multiplex bead binding assay (2D-MBBA) that quantifies multiple antibody isotypes against multiple antigens from a single measurement. Here, we applied our assay to profile IgG, IgM and IgA levels against the spike antigen, its receptor-binding domain and natural and designed mutants. Machine learning algorithms trained on the 2D-MBBA data substantially improve the prediction of neutralization capacity against the authentic SARS-CoV-2 virus of serum samples of convalescent patients. The algorithms also helped identify a set of antibody isotype–antigen datasets that contributed to the prediction, which included those targeting regions outside the receptor-binding interface of the spike protein. We applied the assay to profile samples from vaccinated, immune-compromised patients, which revealed differences in the antibody profiles between convalescent and vaccinated samples. Our approach can rapidly provide deep antibody profiles and neutralization prediction from essentially a drop of blood without the need of BSL-3 access and provides insights into the nature of neutralizing antibodies. It may be further developed for evaluating neutralizing capacity for new variants and future pathogens.

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  1. ScreenIT

    SciScore for 10.1101/2021.08.03.454782: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: All patients gave written informed consent and all samples were deidentified for this study under IRB #i20-00595 (SARS-CoV-2 infected), IRB #s18-02037 (healthy pre-SARS-CoV-2 controls), and IRB #S20-02069 (vaccinated lymphoma patients).
    Sex as a biological variablenot detected.
    RandomizationSpecifically, the sample set was randomly split into a training set with 90% observations and a test set with 10% observations.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    For reference standards, a commercially available anti-COVID-19 and SARS-CoV S glycoprotein antibody clone CR3022 in the IgG, IgA and IgM formats (Absolute Antibody, Human IgG1, Kappa, catalog number Ab01680-10.0, Human IgA, Kappa, catalog number Ab01680-16.0, Human IgM, Kappa, catalog number Ab01680-15.0) were included in triplicates in each measurement.
    anti-COVID-19
    suggested: None
    SARS-CoV S glycoprotein
    suggested: None
    Human IgG1
    suggested: None
    Human IgA
    suggested: None
    Human IgM
    suggested: None
    d 1:800), anti-human IgA PE (Jackson 109-115-011 diluted 1:100) and anti-human IgM DyLight405 (Jackson 709-475-073 diluted 1:200) were used as secondary antibodies.
    anti-human IgA PE
    suggested: None
    In order to standardize MBBA data across different measurements, they were referenced to the MFI values of the control antibodies, CR3022, as described above.
    CR3022
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    For neutralization of cancer patient serum, 20,000 Vero E6 (ATCC CRL-1586) cells/well were seeded in 96 well plates the day before infection.
    Vero E6
    suggested: None
    Software and Algorithms
    SentencesResources
    Data were analyzed using FlowJo (BD, version 10.7.1).
    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.

  2. Version published to 10.1101/2021.08.03.454782v1 on bioRxiv