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Enzyme-Linked Immunosorbent Assay

Enzyme-Linked Immunosorbent Assay

In 1971, Peter Perlman and Eva Engvall developed an Enzyme-linked immunosorbent assay (ELISA or EIA). ELISA differs from western blot in that the assays are conducted in microtiter plates or in vivo rather than on an absorbent membrane.
There are many different types of ELISAs, but they all involve an antibody molecule whose constant region binds an enzyme, leaving the variable region free to bind its specific antigen. Enzyme-substrate reaction allows the antigen to be visualized or...

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

Hanna N Wetzel¹, Cinder Cohen¹, Andrew B Norman¹

¹Department of Pharmacology and Cell Biophysics, University of Cincinnati College of Medicine, Cincinnati, OH 45267-0575, USA.

Journal of Immunological Methods

|June 6, 2017

Summary

This summary is machine-generated.

Researchers developed a free Python program to easily implement Levey-Jennings quality control for enzyme-linked immunosorbent assays (ELISA). This tool enhances data reliability and reproducibility in academic labs by identifying outliers in experimental results.

Keywords:

ELISA Levey-Jennings control charts Python Quality control

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Area of Science:

Biochemistry
Analytical Chemistry
Immunology

Background:

Enzyme-linked immunosorbent assays (ELISA) are widely used semi-quantitative methods.
Stringent quality control is essential for ELISA data accuracy but often lacking in academic settings due to software and knowledge limitations.
Reproducibility in scientific research is hampered by inadequate quality control measures.

Purpose of the Study:

To develop an accessible method for implementing Levey-Jennings quality control in ELISA.
To create a freely available software tool to aid academic laboratories in data quality control.
To improve the reliability and reproducibility of ELISA data generated in research settings.

Main Methods:

Developed a Python program for automated data processing and Levey-Jennings chart generation.
Established a colorimetric ELISA using a goat anti-human anti-Fab capture method.
Tested the program with ELISA standard curves quantifying an anti-cocaine monoclonal antibody Fab fragment in mouse blood over 133 days.

Main Results:

The Python program successfully generated Levey-Jennings control charts and relevant statistics.
The program accurately identified data points outside two standard deviations, consistent with two-way ANOVA results.
The developed ELISA method allowed for quantification of the Fab fragment in mouse blood samples.

Conclusions:

The developed Python program provides an easy-to-use solution for implementing Levey-Jennings quality control in ELISA.
The free availability of this software will empower academic laboratories to enhance data quality and reproducibility.
This work contributes to more reliable and consistent results in immunological and biochemical assays.