Prediction of S-glutathionylated Proteins Progression in Alzheimer's Transgenic Mouse Model Using Principle Component Analysis

To date, prediction of Alzheimer's disease (AD) is mainly based on clinical criteria because no well-established biochemical biomarkers for routine clinical diagnosis of AD currently exist. We developed an approach to aid in the early diagnosis of AD by using principal component analysis (PCA)-based spectral analysis of oxidized protein electrophoretic profiling. We found that the combination of capillary electrophoresis and PCA analysis of S-glutathionylation distribution characterization can be used in the sample classification and molecular weight (Mw) prediction. The comparison of leave-one-out AD versus non-AD gives the sensitivity of 100% and 93.33% in brain tissues and blood samples, respectively, while the specificity of 100% in brain and 90.0% in blood samples. Our findings demonstrate that PCA of S-glutathionylation electrophoretic profiling detects AD pathology features, and that the molecular weight based electrophoretic profiling of blood and brain S-glutathionylated proteins are sensitive to change, even at the early stage of the disease. Our results offer a previously unexplored diagnostic approach by using electrophoretic characteristics of oxidized proteins to serve as a predictor of AD progression and early stage screening.