FIU Robert Stempel College of Public Health & Social Work researcher Dr. Marcus Cooke and colleagues have published the study, Urinary 8-oxo-7,8-dihydro-2′-deoxyguanosine analysis by an improved ELISA: An inter-laboratory comparison study, which suggests that despite significant improvements, a common laboratory technique that is used to measure the concentration of a biomarker of oxidative stress in urine still should not be considered a robust alternative to mass spectrometric techniques, which remain the gold standard. The study appears in the June issue of the journal Free Radical Biology & Medicine, Volume 95.
The enzyme-linked immunosorbent assay known as ELISA is commonly used for the detection of urinary 8-oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG), a marker of whole body oxidative stress, due to its simplicity and low cost. However, the method has been criticized by the by the European Standards Committee on Urinary Lesion Analysis for high inter-laboratory variability and poor agreement with chromatographic techniques.
The study compares the 8-oxodG assessed in 30 urine samples as well as a urine spiked with four different concentrations of 8-oxodG by ELISA using standardized experimental conditions. The ELISA results were then compared with high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS), with researchers performing tentative identification of compounds that may contribute to the discrepancy between both methods. The modified ELISA reported on in the study substantially improved agreement of the method with HPLC–MS/MS analysis. But researchers still detected some samples with 8-oxodG values that differed from the HPLC–MS/MS data.
The research indicates that it is important for the scientific community to be aware of advantages of the modified ELISA, and to apply the recommended steps that are currently known to improve inter-laboratory and between-technique agreement. However, at the same time, the limitations of the assay should be considered when planning the experiments in order to avoid generation of misleading data.