Assay Harmonization and Use of Biological Standards To Improve the Reproducibility of the Hemagglutination Inhibition Assay: a FLUCOP Collaborative Study

Abstract

The hemagglutination inhibition (HAI) assay is an established technique for assessing influenza immunity, through measurement of antihemagglutinin antibodies. Improved reproducibility of this assay is required to provide meaningful data across different testing laboratories. This study assessed the impact of harmonizing the HAI assay protocol/reagents and using standards on interlaboratory variability. Human pre- and postvaccination sera from individuals (n = 30) vaccinated against influenza were tested across six laboratories. We used a design of experiment (DOE) method to evaluate the impact of assay parameters on interlaboratory HAI assay variability. Statistical and mathematical approaches were used for data analysis. We developed a consensus protocol and assessed its performance against in-house HAI testing. We additionally tested the performance of several potential biological standards. In-house testing with four reassortant viruses showed considerable interlaboratory variation (geometric coefficient of variation [GCV] range of 50% to 117%). The age, concentration of turkey red blood cells, incubation duration, and temperature were key assay parameters affecting variability. Use of a consensus protocol with common reagents, including viruses, significantly reduced GCV between laboratories to 22% to 54%. Pooled postvaccination human sera from different vaccination campaigns were effective as biological standards. Our results demonstrate that the harmonization of protocols and critical reagents is effective in reducing interlaboratory variability in HAI assay results and that pools of postvaccination human sera have potential as biological standards that can be used over multiple vaccination campaigns. Moreover, the use of standards together with in-house protocols is as potent as the use of common protocols and reagents in reducing interlaboratory variability. IMPORTANCE The hemagglutination inhibition (HAI) assay is the most commonly used serology assay to detect antibodies from influenza vaccination or influenza virus infection. This assay has been used for decades but requires improved standardization of procedures to provide meaningful data. We designed a large study to assess selected parameters for their contribution to assay variability and developed a standard protocol to promote consistent HAI testing methods across laboratories. The use of this protocol and common reagents resulted in lower levels of variability in results between participating laboratories than achieved using in-house HAI testing. Human sera sourced from vaccination campaigns over several years, and thus including antibody to different influenza vaccine strains, served as effective assay standards. Based on our findings, we recommend the use of a common protocol and/or human serum standards, if available, for testing human sera for the presence of antibodies against seasonal influenza using turkey red blood cells.

Keywords: hemagglutination inhibition assay; influenza; serology; standardization.

FIG 1

Overview of variation in RBCs used in HAI protocols. (A) Use of RBCs in existing HAI protocols across 11 labs. Turkey RBCs (TRBCs), chicken RBCs (CRBCs), and guinea pig RBCs (GPRBCs) are used by labs for testing different influenza virus strains (above and below pie charts) at different concentrations (% in pie charts). The number of labs is shown in parentheses. (B) Different methods for storing (RBC expiration age in days), counting, and preparing RBCs are currently used across the 11 surveyed labs (only 10 of 11 labs returned data for RBC counting protocols).

FIG 2

The impact of selected parameters on HAI assay geometric mean titers (GMTs). Ten sets of conditions were tested using a common source of antigen. GMTs of 30 samples (combined replicates) per condition and strain are shown (Table 1 shows design of experiment). Condition 9 is the consensus protocol, and 10 is each laboratory’s in-house protocol. GMTs are depicted with 95% confidence intervals (CI). The red dashed line indicates the GMT per condition across all testing laboratories.

FIG 3

The impact of selected parameters on HAI assay geometric coefficient of variation (%GCV). Ten sets of conditions were tested using a common source of antigen. %GCVs of 30 samples (combined replicates) per condition and strain are shown across all 6 testing laboratories (Table 1 shows design of experiment). Condition 9 is the consensus protocol, and 10 is each laboratory’s in-house protocol. Boxes indicate median and quartile ranges; green points indicate mean %GCV with 95% confidence intervals.

FIG 4

The effect of the parameters tested on variation between laboratories (difference in SD of log₂ HAI = difference in wells added due to high setting) (A) and fold change in overall GMT (B). The fold difference using the “high setting” of each tested parameter compared to the “low setting” is plotted for each virus strain (see Table S1). TRBC, turkey red blood cells; Inc1, incubation step involving virus and serum sample; Inc2, incubation step involving virus, serum, and TRBCs.

FIG 5

Mathematical analysis of interlaboratory variation across 10 sets of conditions tested. Each sample was scored for high (red or 0) or low (green or 1) variability following a mathematical approach to analyzing the data. The total score across all samples is shaded from high-med-low as green-yellow-red (All samples). The best three sets of conditions are highlighted in gray in the “Conditions” column. Table 1 details the parameters used in conditions 1 to 10.

FIG 6

The impact of using a common SOP and/or a common source antigen (Ag) on HAI titer GMTs. GMTs of 30 samples (combined replicates) per condition and strain are shown. Titers plotted are raw calculated titers. GMTs are depicted with 95% confidence intervals. The black dashed line indicates the GMT per condition across all testing laboratories. The gray-shaded area shows the range of GMTs across all testing laboratories. Conditions are as follows: in-house, in-house assay protocol; SOP, FLUCOP consensus protocol; Ag, common source antigen/virus.

FIG 7

The impact of using a consensus SOP and/or a common source of antigen on HAI titer geometric coefficient of variation (%GCV). %GCVs of 30 samples per condition (replicates combined) across all testing laboratories are shown for four seasonal influenza viruses. Plots show raw calculated GCVs. Boxes indicate median and quartile ranges; green points indicate mean %GCV with 95% confidence intervals. Median HAI titers of individual samples are shown by a color gradient.

FIG 8

Performance of biological standards. Interlaboratory variation across all testing laboratories was calculated as the mean standard deviation for the log₂ HAI titer (of the combined replicates) per sample and condition across the labs. The resulting summary statistics were then converted to %GCV. %GCV is plotted for the following: in-house testing (red) and in-house testing when HAI titers are expressed relative to a homologous ferret serum (olive) and when HAI titers are expressed relative to the three pools of postvaccination human sera (2010–2011 season, green; 2012–2013 season, blue; 2015–2016 season, pink) (composition of the vaccines is given in Table 2).