Fluorometric and Colorimetric Assessment of Thiobarbituric Acid-Reactive Lipid Aldehydes in Biological Matrices

Abstract

AbstractThe thiobarbituric acid (TBA) assay was developed to quantitatively determine lipid peroxidation for aldehydic compounds in biological matrices. Kohn and Liversedge introduced this methodology in 1944 (1,2). Since its introduction, the TBA assay has generated widespread interest in providing valuable information in the assessment of free radical-mediated damage owing to various disease pathologies as well as peroxidation of fatty acids, foods from plant and animal sources, cell membranes (2–4), and rat-liver microsomes (3–5). A biological marker that indicates oxidative stress with respect to lipid peroxidation in body fluids or cells is malondialdehyde (MDA). MDA is a byproduct of the arachidonate cycle, as well as lipid peroxidation (6–8) and is detectable in quantifiable amounts employing the TBA assay. TBA and MDA react to form a schiff base adduct (illustrated in Fig. 1) under high temperature/acidic conditions to produce a chromogenic/fluorescent product that can be easily measured employing various analytical techniques such as spectrophotometric (7,9–11) or fluorometric methods (6,12–14). Incorporating HPLC with ultraviolet (UV)/fluorometric detection or gas chromatography-mass spectrometry (GC-MS) have also been used previously to determine TBA-MDA adducts (2–3,5,15–17) but those methodologies are beyond the scope of this investigation. Our laboratory has developed a quick, simple, and reliable bioanalytical assay using a fluorescence microplate reader in the detection, as well as quantification of the TBA-MDA adduct (lipid peroxidation product) in rat liver microsomes, which we describe here. The chemical reaction between TBA and MDA to yield the TBA-MDA adduct as discussed in the text. KeywordsLipid PeroxidationLiver HomogenateBiological MatriceFluorescence Microplate ReaderFluorometric DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.