Taylor Dispersion Analysis and Atomic Force Microscopy Provide a Quantitative Insight into the Aggregation Kinetics of Aβ (1–40)/Aβ (1–42) Amyloid Peptide Mixtures - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue ACS Chemical Neuroscience Année : 2022

Taylor Dispersion Analysis and Atomic Force Microscopy Provide a Quantitative Insight into the Aggregation Kinetics of Aβ (1–40)/Aβ (1–42) Amyloid Peptide Mixtures

Résumé

Aggregation of amyloid β peptides is known to be one of the main processes responsible for Alzheimer's disease. The resulting dementia is believed to be due in part to the formation of potentially toxic oligomers. However, the study of such intermediates and the understanding of how they form is very challenging because they are heterogeneous and transient in nature. Unfortunately, few techniques can quantify, in real time, the proportion and the size of the different soluble species during the aggregation process. In a previous work (Deleanu et al. Anal. Chem. 2021), we showed the potential of Taylor dispersion analysis (TDA) in amyloid speciation during the aggregation process of Aβ(1-40) and Aβ(1-42). The current work aims at exploring in detail the aggregation of amyloid Aβ(1-40):Aβ(1-42) peptide mixtures with different proportions of each peptide (1:0, 3:1, 1:1, 1:3 and 0:1), using TDA and atomic force microscopy (AFM). TDA allowed for monitoring the kinetics of the amyloid assembly and quantifying the transient intermediates. Complementarily, AFM allowed the formation of insoluble fibrils to be visualized. Together, the two techniques enabled to study the influence of the peptide ratios on the kinetics and the formation of potentially toxic oligomeric species.
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Dates et versions

hal-03801189 , version 1 (06-10-2022)

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Mihai Deleanu, Olivier Deschaume, Luca Cipelletti, Jean-François Hernandez, Carmen Bartic, et al.. Taylor Dispersion Analysis and Atomic Force Microscopy Provide a Quantitative Insight into the Aggregation Kinetics of Aβ (1–40)/Aβ (1–42) Amyloid Peptide Mixtures. ACS Chemical Neuroscience, 2022, 13 (6), pp.786-795. ⟨10.1021/acschemneuro.1c00784⟩. ⟨hal-03801189⟩
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