Affiliations: Department of Biomedical Engineering, Louisiana Tech University, Ruston, LA, USA
Correspondence:
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Correspondence to: June Feng, Department of Biomedical Engineering, Louisiana Tech University, 818 Nelson Avenue, Ruston, LA 71272, USA. Tel.: +1 318 257 5236; Fax: +1 318 257 4000; E-mail: junefeng@latech.edu.
Abstract: 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.
Keywords: Alzheimer's disease prediction, capillary gel electrophoresis with laser induced fluorescence detection (CGE-LIF), principle component analysis (PCA), S-glutathionylated proteins (Pr-SSG)
