Reducing the risk of misdiagnosis of indirect ELISA by normalizing serum-specific background noise: The example of detecting anti-FGFR3 autoantibodies

Abstract

Indirect enzyme-linked immunosorbent assay (ELISA) is an important diagnostic method as it enables the quantification of the presence of autoantibodies in human blood sera. However, unspecific binding of antibodies to the solid phase causes considerable serum-specific background noise (SSBN), involving the risk of false positive diagnosis. Therefore, we present a simple and concise, yet obvious proof-of-principle of a recently suggested normalization method. The method is based on subtracting SSBN by using non-coated ELISA wells as a control for each serum-of-interest. We performed ELISA to quantify anti-fibroblast growth factor receptor 3 (FGFR3) antibody levels in three positive controls (two anti-FGFR3-positive patients and a rabbit antiserum against FGFR3) and 58 negative controls (healthy blood donors). In all subjects, we found considerable unspecific reactivity which strongly varied among subjects. The conventional normalization method was not able to balance this strong SSBN, as demonstrated by 2/58 false positive healthy controls and one FGFR3-positive patient that was hidden in the noise (false negative). SSBN normalization reduced the frequency of false-positives to 0/58. Further, all three anti-FGFR3-positive sera were successfully detected and even doubled their z-score used to determine positivity. Albeit occupying more space on the ELISA plate, we strongly recommend considering this normalization method when working with blood sera. To better put the idea across to the community, we depict the SSBN issue and its solution in a graphic scheme. We conclude that SSBN normalization increases the sensitivity and specificity of indirect ELISA and thereby reduces the risk of false positive and false negative diagnosis. © 2019. Licensed under the Creative Commons [CC BY-NC 4.0 licence, https://doi.org/10.1016/j.jim.2019.01.004].