Tahirbegi, B, Magness, AJ, Piersimoni, ME et al. (7 more authors) (2022) Toward high-throughput oligomer detection and classification for early-stage aggregation of amyloidogenic protein. Frontiers in Chemistry, 10. 967882. ISSN 2296-2646
Abstract
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 early stages of aggregation cannot be detected. Single-molecule methods allow direct 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 stoichiometry of 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 a 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Tahirbegi, Magness, Piersimoni, Teng, Hooper, Guo, Knöpfel, Willison, Klug and Ying. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
Keywords: | single-molecule photobleaching; fluorescence imaging; machine learning; artificial neural network; amyloid-β; α-synuclein; protein aggregation; neurodegenerative disease |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Physics and Astronomy (Leeds) > Molecular & Nanoscale Physics The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Chemistry and Biochemistry (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Jun 2023 11:14 |
Last Modified: | 14 Jun 2023 11:14 |
Status: | Published |
Publisher: | Frontiers Media |
Identification Number: | 10.3389/fchem.2022.967882 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200317 |