A novel Python program for implementation of quality control in the ELISA

Abstract

The use of semi-quantitative assays such as the enzyme-linked immunosorbent assay (ELISA) requires stringent quality control of the data. However, such quality control is often lacking in academic settings due to unavailability of software and knowledge. Therefore, our aim was to develop methods to easily implement Levey-Jennings quality control methods. For this purpose, we created a program written in Python (a programming language with an open-source license) and tested it using a training set of ELISA standard curves quantifying the Fab fragment of an anti-cocaine monoclonal antibody in mouse blood. A colorimetric ELISA was developed using a goat anti-human anti-Fab capture method. Mouse blood samples spiked with the Fab fragment were tested against a standard curve of known concentrations of Fab fragment in buffer over a period of 133days stored at 4°C to assess stability of the Fab fragment and to generate a test dataset to assess the program. All standard curves were analyzed using our program to batch process the data and to generate Levey-Jennings control charts and statistics regarding the datasets. The program was able to identify values outside of two standard deviations, and this identification of outliers was consistent with the results of a two-way ANOVA. This program is freely available, which will help laboratories implement quality control methods, thus improving reproducibility within and between labs. We report here successful testing of the program with our training set and development of a method for quantification of the Fab fragment in mouse blood.