Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel

  1. National COVID Testing Scientific Advisory Panel
  2. Emily R Adams
  3. Mark Ainsworth
  4. Rekha Anand
  5. Monique I Andersson
  6. Kathryn Auckland
  7. J Kenneth Baillie
  8. Eleanor Barnes
  9. Sally Beer
  10. John Bell
  11. Tamsin Berry
  12. Sagida Bibi
  13. Miles Carroll
  14. Senthil Chinnakannan
  15. Elizabeth Clutterbuck
  16. Richard J Cornall
  17. Derrick W Crook
  18. Thushan De Silva
  19. Wanwisa Dejnirattisai
  20. Kate E Dingle
  21. Christina Dold
  22. Alexis Espinosa
  23. David W Eyre
  24. Helen Farmer
  25. Maria Fernandez Mendoza
  26. Dominique Georgiou
  27. Sarah J Hoosdally
  28. Alistair Hunter
  29. Katie Jeffrey
  30. Paul Klenerman
  31. Julian Knight
  32. Clarice Knowles
  33. Andrew J Kwok
  34. Ullrich Leuschner
  35. Robert Levin
  36. Chang Liu
  37. Cesar Lopez-Camacho
  38. Jose Carlos Martinez Garrido
  39. Philippa C Matthews
  40. Hannah McGivern
  41. Alexander J Mentzer
  42. Jonathan Milton
  43. Juthathip Mongkolsapaya
  44. Shona C Moore
  45. Marta S Oliveira
  46. Fiona Pereira
  47. Elena Perez Lopez
  48. Timothy Peto
  49. Rutger J Ploeg
  50. Andrew Pollard
  51. Tessa Prince
  52. David J Roberts
  53. Justine K Rudkin
  54. Veronica Sanchez
  55. Gavin R Screaton
  56. Malcolm G Semple
  57. Donal T Skelly
  58. Jose Slon-Campos
  59. Elliot Nathan Smith
  60. Alberto Jose Sobrino Diaz
  61. Julie Staves
  62. David Stuart
  63. Piyada Supasa
  64. Tomas Surik
  65. Hannah Thraves
  66. Pat Tsang
  67. Lance Turtle
  68. A Sarah Walker
  69. Beibei Wang
  70. Charlotte Washington
  71. Nicholas Watkins
  72. James Whitehouse

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Abstract

Background

The COVID-19 pandemic caused >1 million infections during January-March 2020. There is an urgent need for reliable antibody detection approaches to support diagnosis, vaccine development, safe release of individuals from quarantine, and population lock-down exit strategies. We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices.

Methods

We tested plasma for COVID (SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices. We used a panel of plasma samples from individuals who have had confirmed COVID infection based on a PCR result (n=40), and pre-pandemic negative control samples banked in the UK prior to December-2019 (n=142).

Results

ELISA detected IgM or IgG in 34/40 individuals with a confirmed history of COVID infection (sensitivity 85%, 95%CI 70-94%), vs. 0/50 pre-pandemic controls (specificity 100% [95%CI 93-100%]). IgG levels were detected in 31/31 COVID-positive individuals tested ≥10 days after symptom onset (sensitivity 100%, 95%CI 89-100%). IgG titres rose during the 3 weeks post symptom onset and began to fall by 8 weeks, but remained above the detection threshold. Point estimates for the sensitivity of LFIA devices ranged from 55-70% versus RT-PCR and 65-85% versus ELISA, with specificity 95-100% and 93-100% respectively. Within the limits of the study size, the performance of most LFIA devices was similar.

Conclusions

Currently available commercial LFIA devices do not perform sufficiently well for individual patient applications. However, ELISA can be calibrated to be specific for detecting and quantifying SARS-CoV-2 IgM and IgG and is highly sensitive for IgG from 10 days following first symptoms.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.15.20066407: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: ) Ethical approval and role of the funding source: Our work was undertaken with ethical approval from the National Health Service Blood and Transplant (NHSBT) ethics, providing donor consent for plasma use; NIHR Biobank REC agreement (REC 13/NW/0017; IRAS 87824); International Severe Acute Respiratory and Emerging Infection Consortium (‘ ISARIC’) approval by the South Central (Oxford C) Research Ethics Committee in England (Ref: 13/SC/0149), and Scotland A Research Ethics Committee in Scotland (Ref: 20/SS/0028).
    IRB: ) Ethical approval and role of the funding source: Our work was undertaken with ethical approval from the National Health Service Blood and Transplant (NHSBT) ethics, providing donor consent for plasma use; NIHR Biobank REC agreement (REC 13/NW/0017; IRAS 87824); International Severe Acute Respiratory and Emerging Infection Consortium (‘ ISARIC’) approval by the South Central (Oxford C) Research Ethics Committee in England (Ref: 13/SC/0149), and Scotland A Research Ethics Committee in Scotland (Ref: 20/SS/0028).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Testing protocol: We tested 90 samples using ELISA to quantify IgM and IgG antibody in plasma designated SARS-CoV-2 negative (n=50) and positive (n=40).
    IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The gene was cloned into a pHLsec and expressed in 293T cells.
    293T
    suggested: NCBI_Iran Cat# C498, RRID:CVCL_0063)
    Software and Algorithms
    SentencesResources
    Statistical analysis: Analyses were conducted using R (version 3.6.3) and Stata (version 15.1), with additional plots generated using GraphPad Prism (version 8.3.1).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The main study limitation is that numbers tested were too small to provide tight confidence intervals around performance estimates for any specific LFIA device. Expanding testing across diverse populations would increase certainty, but given the broadly comparable performance of different assays, the cost and manpower to test large numbers may not be justifiable. Demonstrating high specificity is particularly challenging; for example, if the true underlying value was 98%, 1000 negative controls would be required to estimate the specificity of an assay to +/-1% with approximately 90% power. Full assessment should also include a range of geographical locations and ethnic groups, children, and those with immunological disease including autoimmune conditions and immunosuppression. In summary, antibody testing is a crucial component of measures that may be required to inform release from lockdown. Our findings suggest that while current LFIA devices may provide some information for population-level surveys, their performance is inadequate for most individual patient applications. The ELISA we describe is currently being optimised and adapted to run on a high-throughput platform and provides promise for the development of reliable approaches to antibody detection that can support decision making for clinicians, the public health community, policy-makers and industry.

    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.

  2. Version published to 10.1101/2020.04.15.20066407v3 on medRxiv
  3. Version published to 10.12688/wellcomeopenres.15927.1
  4. ScreenIT

    SciScore for 10.1101/2020.04.15.20066407: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementEthical approval Our work was undertaken with ethical approval from the National Health Service Blood and Transplant ( NHSBT ) ethics , providing donor consent for plasma use; NIHR Biobank REC agreement ( REC 13/NW/0017; IRAS 87824); International Severe Acute Respiratory and Emerging Infection Consortium ( ‘ISARIC’ ) approval by the South Central ( Oxford C ) Research Ethics Committee in England ( Ref: 13/SC/0149) , and Scotland A Research Ethics Committee in Scotland ( Ref: 20/SS/0028) .Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variablenot detected.Cell Line Authenticationnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices. Design: We tested plasma for COVID (SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices.
    IgG antibodies
    suggested: None
    20 Our study , and another undertaken independently in parallel,11 demonstrates accurate performance of ELISA targeting anti-spike protein antibodies .
    anti-spike protein
    suggested: None
    Methods to enhance sensitivity , especially shortly after symptom onset , could consider different sample types ( e.g. saliva) , different antibody classes ( e.g. IgA)22 , T-cell assays or antigen detection.
    antigen detection.
    suggested: None
    Cartoon to illustrate the generation of IgM and IgG antibodies to SARS nCoV-2 and detection of antibodies by a lateral flow device.
    IgG
    suggested: None
    Experimental Models: Cell Lines
    SentencesResources
    The gene was cloned into a pHLsec and expressed in 293T cells .
    293T
    suggested: KCB Cat# KCB 200744YJ, CVCL_0063
    Software and Algorithms
    SentencesResources
    Statistical analysis Analyses were conducted using R ( version 3.6.3 ) and Stata ( version 15.1) , with additional plots generated using GraphPad Prism ( version 8.3.1) .
    GraphPad Prism
    suggested: (GraphPad Prism, SCR_002798)
    Image created with BioRender.com; exported under a paid subscription.
    BioRender
    suggested: (Biorender, SCR_018361)

    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).

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  5. Version published to 10.1101/2020.04.15.20066407v2 on medRxiv
  6. Version published to 10.1101/2020.04.15.20066407v1 on medRxiv