Scalable, semi-automated fluorescence reduction neutralization assay for qualitative assessment of Ebola virus-neutralizing antibodies in human clinical samples

Abstract

Antibody titers against a viral pathogen are typically measured using an antigen binding assay, such as an enzyme-linked immunosorbent assay (ELISA), which only measures the ability of antibodies to identify a viral antigen of interest. Neutralization assays measure the presence of virus-neutralizing antibodies in a sample. Traditional neutralization assays, such as the plaque reduction neutralization test (PRNT), are often difficult to use on a large scale due to being both labor and resource intensive. Here we describe an Ebola virus fluorescence reduction neutralization assay (FRNA), which tests for neutralizing antibodies, that requires only a small volume of sample in a 96-well format and is easy to automate. The readout of the FRNA is the percentage of Ebola virus-infected cells measured with an optical reader or overall chemiluminescence that can be generated by multiple reading platforms. Using blinded human clinical samples (EVD survivors or contacts) obtained in Liberia during the 2013-2016 Ebola virus disease outbreak, we demonstrate there was a high degree of agreement between the FRNA-measured antibody titers and the Filovirus Animal Non-clinical Group (FANG) ELISA titers with the FRNA providing information on the neutralizing capabilities of the antibodies.

Trial registration: ClinicalTrials.gov NCT02431923.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Assay sensitivity.

The sensitivity of the assay as it depends on the number of cells per filed (n) and the probability that a cell is infected at the lowest dilution (π1) given that the probability a cell is infected in the virus only wells is 50% and c = 1.

Fig 2. Total cell count distribution.

Distribution of total cell counts across all samples, plate columns, fields, and replicates.

Fig 3. FRNA50 titer estimate in fields with varying cell count.

Comparison of FRNA50 titer estimates calculated using low cell count and high cell count fields. For each plate and column replicate, the field corresponding to either the minimum or maximum total cell count was used to estimate functional titers.

Fig 4. Assay sensitivity comparisons.

The improvement in sensitivity that is possible by using multiple fields.

Fig 5. Coefficient of variation.

Distribution of the coefficient of variation by the number of fields used to calculate FRNA50 titers.

Fig 6. Comparison of cell count by field location.

Comparison of total cell count, positive cell count and positive cell percentage between boundary fields and non-boundary fields by plate column. Cell counts and percentages are averaged across samples, replicates and fields. Statistical significance was assessed via GEE linear regression models that controlled for the effects of plate and replicate and adjusted for repeated measures among samples.