SARS-CoV-2 seroprevalence and neutralizing activity in donor and patient blood

  1. Dianna L. Ng
  2. Gregory M. Goldgof
  3. Brian R. Shy
  4. Andrew G. Levine
  5. Joanna Balcerek
  6. Sagar P. Bapat
  7. John Prostko
  8. Mary Rodgers
  9. Kelly Coller
  10. Sandra Pearce
  11. Sergej Franz
  12. Li Du
  13. Mars Stone
  14. Satish K. Pillai
  15. Alicia Sotomayor-Gonzalez
  16. Venice Servellita
  17. Claudia Sanchez San Martin
  18. Andrea Granados
  19. Dustin R. Glasner
  20. Lucy M. Han
  21. Kent Truong
  22. Naomi Akagi
  23. David N. Nguyen
  24. Neil M. Neumann
  25. Daniel Qazi
  26. Elaine Hsu
  27. Wei Gu
  28. Yale A. Santos
  29. Brian Custer
  30. Valerie Green
  31. Phillip Williamson
  32. Nancy K. Hills
  33. Chuanyi M. Lu
  34. Jeffrey D. Whitman
  35. Susan L. Stramer
  36. Candace Wang
  37. Kevin Reyes
  38. Jill M. C. Hakim
  39. Kirk Sujishi
  40. Fariba Alazzeh
  41. Lori Pham
  42. Edward Thornborrow
  43. Ching-Ying Oon
  44. Steve Miller
  45. Theodore Kurtz
  46. Graham Simmons
  47. John Hackett
  48. Michael P. Busch
  49. Charles Y. Chiu

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Abstract

Given the limited availability of serological testing to date, the seroprevalence of SARS-CoV-2-specific antibodies in different populations has remained unclear. Here, we report very low SARS-CoV-2 seroprevalence in two San Francisco Bay Area populations. Seroreactivity was 0.26% in 387 hospitalized patients admitted for non-respiratory indications and 0.1% in 1,000 blood donors in early April 2020. We additionally describe the longitudinal dynamics of immunoglobulin-G (IgG), immunoglobulin-M (IgM), and in vitro neutralizing antibody titers in COVID-19 patients. The median time to seroconversion ranged from 10.3–11.0 days for these 3 assays. Neutralizing antibodies rose in tandem with immunoglobulin titers following symptom onset, and positive percent agreement between detection of IgG and neutralizing titers was >93%. These findings emphasize the importance of using highly accurate tests for surveillance studies in low-prevalence populations, and provide evidence that seroreactivity using SARS-CoV-2 anti-nucleocapsid protein IgG and anti-spike IgM assays are generally predictive of in vitro neutralizing capacity.

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  1. Version published to 10.1038/s41467-020-18468-8
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.20107482: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the institutional review board (IRB) at UCSF (UCSF IRB #10-02598) as a no-subject contact study with waiver of consent.
    Consent: This study was approved by the institutional review board (IRB) at UCSF (UCSF IRB #10-02598) as a no-subject contact study with waiver of consent.
    RandomizationAdditional samples were collected from randomly selected cohorts of outpatients and hospitalized patients at UCSF during the same time period seen for indications other than COVID-19 respiratory disease (non-COVID).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The VITROS anti-SARS-CoV-2 total antibody assay approved under FDA Emergency Use Authorization was performed using either serum or plasma samples at Vitalant Research Institute according to the manufacturer instructions15.
    anti-SARS-CoV-2
    suggested: None
    We then calculated PPA, NPA, and OPA between the neutralizing antibody result and IgM, assuming IgM to be the gold standard.
    IgM
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    Production of pseudoviruses for the SARS-CoV-2 neutralization assay: VSVΔG-luciferase-based viruses, in which the glycoprotein (G) gene has been replaced with luciferase, were produced by transient transfection of viral glycoprotein expression plasmids (pCG SARS-CoV-2 Spike, provided courtesy of Stefan Pölhmann16, as well as pCAGGS VSV-G or pCAGGS EboGP as controls) or no glycoprotein controls into HEK293T cells by TranslT-2020.
    HEK293T
    suggested: None
    Software and Algorithms
    SentencesResources
    Clinical data for UCSF patients were extracted from electronic health records and entered in a HIPAA (Health Insurance Portability and Accountability Act)-secure REDCap research database.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Serologic testing: The Abbott Architect SARS-CoV-2 IgG assay (FDA Emergency Use Authorization (EUA)) and SARS-CoV-2 IgM (prototype) testing was performed using either serum or plasma samples on the Architect instrument according to the manufacturer instructions4.
    Abbott Architect
    suggested: (Abbott ARCHITECT i1000sr System, RRID:SCR_019328)
    Non-linear regression curves and 80% neutralization titers (NT80) were calculated in GraphPad Prism.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Statistical calculations were performed using python libraries scipy.stats, sklearn.metrics.auc and statsmodels.stats as well as Stata v15.1 (College Station, TX).
    python
    suggested: (IPython, RRID:SCR_001658)
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    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 10.1101/2020.05.19.20107482 on medRxiv