Machine learning-assisted fluorescence/fluorescence colorimetric sensor array for discriminating amyloid fibrils

Misfolding and aggregation of proteins often lead to the development of diseases, and amyloid has gained widespread attention as a biomarker for a variety of diseases. In this study, we developed a fluorescence/fluorescence colorimetric dual-mode sensor array for the detection of amyloid fibrils using several commercially available organic small molecular dyes and alkaloids, including Thioflavin T, Congo Red, 8-anilino-1-naphthalenesulfonic acid, Safranine T, berberine and coptisine, as the elements. Herein, the array could not only use the fluorescence intensities change before and after protein interaction as a pattern recognition signal, but also read the Delta R/Delta G/Delta B values of the photos taken in the UV dark box on a smartphone-based platform, which converted the chromaticity information into intuitive data. Five studied amyloid fibrils, i.e. insulin, lysozyme, bovine serum albumin, amyloid-8 42 and alpha-synuclein fibrils, were properly distinguished with data processing assisted by machine learning algorithms, i.e. linear discriminant analysis, principal component analysis and hierarchical cluster analysis. After reducing the number of elements by principal component analysis, a simplified array quantified individual amyloid fibrils at 0.05-5 mu M and 0.5-10 mu M with fluorescence and fluorescence colorimetric signals, respectively, and successfully identified 25 unknown samples with high accuracy in diluted human plasma matrix and artificial cerebrospinal fluid. The array had good selectivity and sensitivity, providing a simple and inexpensive method for amyloid discrimination.