Monitoring amyloid-β 42 conformational change using a spray-printed graphene electrode

Up to now, the reproducibility and stability of graphene-based electrochemical sensors have represented an obstacle to the development of practical biosensing techniques. In this paper we report a cost-effective and highly reproducible graphene-based electrochemical sensing platform to monitor the kinetic conformational change of amyloidogenic proteins. The sensor surface is spray-printed with a graphene oxide layer and then electrochemically reduced to achieve excellent sensitivity to the redox current. The reproducibility of these sensors in terms of redox peak position, intensity and electroactive area has been proved to be high. These sensors are used to monitor the conformational changes of amyloid-β 42 via the change in the oxidation current of tyrosine, which is caused by different electrochemical accessibility during the aggregation process. The aggregation process detected at these graphene electrochemical sensors shows a good correlation with the fluorescence assay. The proposed platform provides a complementary technique to aid understanding of the detailed process of amyloidogenic protein aggregation and the mechanism of neurodegenerative diseases as well as helping to promote the development of disease-prevention strategies

Towards high throughput oligomer detection and classification for early-stage aggregation of amyloidogenic protein

The aggregation kinetics of proteins and peptides have been studied extensively due to their significance in many human diseases, including neurodegenerative disorders, and the roles they play in some key physiological processes. However, most of these studies have been performed as bulk measurements using Thioflavin T or other fluorescence turn-on reagents as indicators of fibrillization. Such techniques are highly successful in making inferences about the nucleation and growth mechanism of fibrils yet cannot directly measure assembly reactions at low protein concentrations which is the case for amyloid-β (Aβ) peptide under physiological conditions. In particular, the evolution from monomer to low order oligomer in the early stages of aggregation cannot be detected. Single molecule methods allow directly access to such fundamental information. We developed a high throughput protocol for single molecule photobleaching experiments using an automated fluorescence microscope. Stepwise photobleaching analysis of the time profiles of individual foci allowed us to determine the stoichiometry of the protein oligomers and probe protein aggregation kinetics. Furthermore, we investigated the potential application of supervised machine learning with support vector machines (SVMs) as well as multilayer perceptron (MLP) artificial neural networks to classify bleaching traces into stoichiometric categories based on an ensemble of measurable quantities derivable from individual traces. Both SVM and MLP models achieved comparable accuracy of more than 80% against simulated traces up to 19-mer, although MLP offered considerable speed advantages thus making it suitable for application to high throughput experimental data. We used our high throughput method to study the aggregation of Aβ40 in the presence of metal ions and the aggregation of α-synuclein in the presence of gold nanoparticles

Quantitative comparison of different iron forms in the temporal cortex of Alzheimer patients and control subjects

Quantum Matter and Optic