Evaluation of methods to reduce background using the Python-based ELISA_QC program.

Almost all immunological approaches [immunohistochemistry, enzyme-linked immunosorbent assay (ELISA), Western blot], that are used to quantitate specific proteins have had to address high backgrounds due to non-specific reactivity. We report here for the first time a quantitative comparison of methods for reduction of the background of commercial biotinylated antibodies using the Python-based ELISA_QC program. This is demonstrated using a recombinant humanized anti-cocaine monoclonal antibody. Several approaches, such as adjustment of the incubation time and the concentration of blocking agent, as well as the dilution of secondary antibodies, have been explored to address this issue. In this report, systematic comparisons of two different methods, contrasted with other more traditional methods to address this problem are provided. Addition of heparin (HP) at 1 μg/ml to the wash buffer prior to addition of the secondary biotinylated antibody reduced the elevated background absorbance values (from a mean of 0.313 ± 0.015 to 0.137 ± 0.002). A novel immunodepletion (ID) method also reduced the background (from a mean of 0.331 ± 0.010 to 0.146 ± 0.013). Overall, the ID method generated more similar results at each concentration of the ELISA standard curve to that using the standard lot 1 than the HP method, as analyzed by the Python-based ELISA_QC program. We conclude that the ID method, while more laborious, provides the best solution to resolve the high background seen with specific lots of biotinylated secondary antibody.