By Magali Fischer
With more than 50% of preclinical results estimated to be irreproducible, the reliability of methods, assays, and protocols is a major concern in all areas of research. Many critical assay workflows, such as those for ELISA tests, are prone to error, even when using a high quality kit. While ELISA kits provide a solid basis to generate reliable data, troubleshooting the complete assay workflow is the first step toward pinpointing additional sources of variability and error that must be addressed in order to increase reproducibility and confident decision making.
Is poor data reproducibility compromising the reliability of your ELISA assays?
Data reproducibility is crucial in regulated work such as pharma R&D and manufacturing, as well as clinical and academic research applications. Data rigor and reproducibility in clinical research is a systemic problem and a priority issue for many organizations, including the National Institutes of Health (NIH).¹ Errors in preclinical research can be very costly. The cumulative prevalence of irreproducible preclinical research has been estimated to exceed 50%, resulting in approximately US$28,000,000,000 (US$28B)/year spent on preclinical research that is not reproducible – in the United States alone.²
The data reproducibility problem is not limited to clinical research and the pharma industry. A report published in 2016 on reproducibility across many scientific fields shows that more than 70% of the over 1500 researchers contacted have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments.³ Such insights have led to a drive to implement quality control procedures in academic research.⁴
High performance ELISA has become an invaluable method in clinical diagnostics and research and isregarded as a simple and low-cost way to measure analytes in a sample. Running ELISA has, however, traditionally been a time-consuming and laborious manual process that is relatively inefficient and error-prone. The need to quickly generate reliable data consistently, day-to-day is challenged by problems with variability within assays, between labs, and with low throughput.
Problems with manual ELISA can include:
Causes can include:
As an example of the complexity of manual tasks, we can consider some of the details needed to ensure correct manual pipetting:
1. Use the correct pipette and tipThese requirements are stringent, time consuming and may lead to operator fatigue. Furthermore, multiple rounds of accurate pipetting and thorough aspiration are required during the process.
There are also a number of points to remember in general to achieve reproducible assay results:
In the next article in this series we will look at a case study that demonstrates how automation can address many of these issues, and help you improve the reproducibility of assays for biomarkers used to diagnose Alzheimer’s disease.
1. Prinz F, Schlange T, Asadullah K. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov. 2011; 10(9):712.
2. Freedman, L.P., Cockburn, I.M., and Simcoe, T.S. (2015) The Economics of Reproducibility in Preclinical Research. PLoS Biol. 13(6): e1002165.
3. Baker, M. (2016) 1500 scientists lift the lid on reproducibility. Nature 533: 452–454.
4. Baker, M. (2016) How quality control could save your science. Nature 529: 456–458.