A novel Python program for implementation of quality control in the ELISA
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...
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| Published in: | Journal of immunological methods Vol. 448; pp. 80 - 84 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.09.2017
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| ISSN: | 0022-1759, 1872-7905, 1872-7905 |
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| 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.
•A python program was developed to rapidly screen ELISA data for quality control•This method was applied to the quantification of a Fab fragment of anti-cocaine mAb in blood |
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| AbstractList | 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. 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.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. 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 133 days 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. 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. •A python program was developed to rapidly screen ELISA data for quality control•This method was applied to the quantification of a Fab fragment of anti-cocaine mAb in blood |
| Author | Webster, Rose P. Norman, Andrew B. Wetzel, Hanna N. Cohen, Cinder |
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| Cites_doi | 10.4161/21645515.2014.990856 10.1093/clinchem/23.10.1857 10.1021/jm030351z 10.1371/journal.pcbi.0030199 |
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| Keywords | Levey-Jennings control charts Quality control ELISA Python |
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| References | Paula, Tabet, Farr, Norman, Ball (bb0025) 2004; 47 Bassi (bb0005) 2007; 3 Westgard, Barry (bb0035) 1986 Institute for Laboratory Animal Research (bb0010) 2011 Kirley, Norman (bb0015) 2015; 11 Shewhart (bb0030) 1931 Westgard, Groth, Aronsson, Falk, de Verdier (bb0040) 1977; 23 Naylor, Porter, Wilson, Herring, Lofthouse, Hannemann, Piccolo, Rockwood, Price (bb0020) 2017 Westgard (10.1016/j.jim.2017.05.012_bb0035) 1986 Institute for Laboratory Animal Research (10.1016/j.jim.2017.05.012_bb0010) 2011 Bassi (10.1016/j.jim.2017.05.012_bb0005) 2007; 3 Shewhart (10.1016/j.jim.2017.05.012_bb0030) 1931 Kirley (10.1016/j.jim.2017.05.012_bb0015) 2015; 11 Naylor (10.1016/j.jim.2017.05.012_bb0020) 2017 Paula (10.1016/j.jim.2017.05.012_bb0025) 2004; 47 Westgard (10.1016/j.jim.2017.05.012_bb0040) 1977; 23 |
| References_xml | – year: 2011 ident: bb0010 article-title: Guide for the Care and use of Laboratory Animals – volume: 47 start-page: 133 year: 2004 end-page: 142 ident: bb0025 article-title: Three-dimensional quantitative structure-activity relationship modeling of cocaine binding by a novel human monoclonal antibody publication-title: J. Med. Chem. – volume: 23 start-page: 1857 year: 1977 end-page: 1867 ident: bb0040 article-title: Performance characteristics of rules for internal quality control: Probabilities for false rejection and error detection publication-title: Clin. Chem. – volume: 3 year: 2007 ident: bb0005 article-title: A primer on python for life science researchers publication-title: PLoS Comput. Biol. – year: 1986 ident: bb0035 article-title: Cost-Effective Quality Control: Managing the Quality and Productivity of Analytical Processes – volume: 11 start-page: 458 year: 2015 end-page: 467 ident: bb0015 article-title: Characterization of a recombinant humanized anti-cocaine monoclonal antibody and its fab fragment publication-title: Human vaccines & immunotherapeutics – year: 1931 ident: bb0030 article-title: Economic Control of Quality of Manufactured Products – year: 2017 ident: bb0020 article-title: DeuteRater: A Tool for Quantifying Peptide Isotope Precision and Kinetic Proteomics – volume: 11 start-page: 458 year: 2015 ident: 10.1016/j.jim.2017.05.012_bb0015 article-title: Characterization of a recombinant humanized anti-cocaine monoclonal antibody and its fab fragment publication-title: Human vaccines & immunotherapeutics doi: 10.4161/21645515.2014.990856 – year: 2017 ident: 10.1016/j.jim.2017.05.012_bb0020 – year: 1986 ident: 10.1016/j.jim.2017.05.012_bb0035 – volume: 23 start-page: 1857 year: 1977 ident: 10.1016/j.jim.2017.05.012_bb0040 article-title: Performance characteristics of rules for internal quality control: Probabilities for false rejection and error detection publication-title: Clin. Chem. doi: 10.1093/clinchem/23.10.1857 – volume: 47 start-page: 133 year: 2004 ident: 10.1016/j.jim.2017.05.012_bb0025 article-title: Three-dimensional quantitative structure-activity relationship modeling of cocaine binding by a novel human monoclonal antibody publication-title: J. Med. Chem. doi: 10.1021/jm030351z – volume: 3 year: 2007 ident: 10.1016/j.jim.2017.05.012_bb0005 article-title: A primer on python for life science researchers publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.0030199 – year: 2011 ident: 10.1016/j.jim.2017.05.012_bb0010 – year: 1931 ident: 10.1016/j.jim.2017.05.012_bb0030 |
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| SubjectTerms | Analysis of Variance Animals Antibodies, Monoclonal - blood Automation, Laboratory batch systems blood blood sampling Calibration Cocaine - immunology Cocaine-Related Disorders - blood Cocaine-Related Disorders - diagnosis Cocaine-Related Disorders - immunology colorimetry computer software data collection ELISA enzyme-linked immunosorbent assay Enzyme-Linked Immunosorbent Assay - standards goats Immunoglobulin Fab Fragments - blood Levey-Jennings control charts Mice monoclonal antibodies Predictive Value of Tests Python Quality Control Reference Standards Reproducibility of Results Software Design Software Validation Substance Abuse Detection - methods Substance Abuse Detection - standards |
| Title | A novel Python program for implementation of quality control in the ELISA |
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