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Detection of Amyloid-β Fibrils Using Track-Etched Nanopores: Effect of Geometry and Crowding.

Nathan MeyerNicolas ArroyoJean-Marc JanotMathilde LepoitevinAnna StevensonImad Abrao NemeirVeronique PerrierDaisy BougardMaxime BelondradeDidier CotJérémy BentinFabien PicaudJoan TorrentSebastien Balme
Published in: ACS sensors (2021)
Several neurodegenerative diseases have been linked to proteins or peptides that are prone to aggregate in different brain regions. Aggregation of amyloid-β (Aβ) peptides is recognized as the main cause of Alzheimer's disease (AD) progression, leading to the formation of toxic Aβ oligomers and amyloid fibrils. The molecular mechanism of Aβ aggregation is complex and still not fully understood. Nanopore technology provides a new way to obtain kinetic and morphological aspects of Aβ aggregation at a single-molecule scale without labeling by detecting the electrochemical signal of the peptides when they pass through the hole. Here, we investigate the influence of nanoscale geometry (conical and bullet-like shape) of a track-etched nanopore pore and the effect of molecular crowding (polyethylene glycol-functionalized pores) on Aβ fibril sensing and analysis. Various Aβ fibril samples that differed by their length were produced by sonication of fibrils obtained in the presence of epigallocatechin gallate. The conical nanopore functionalized with polyethylene glycol (PEG) 5 kDa is suitable for discrimination of the fibril size from relative current blockade. The bullet-like-shaped nanopore enhances the amplitude of the current and increases the dwell time, allowing us to well discern the fibrils. Finally, the nanopore crowded with PEG 20 kDa enhances the relative current blockade and increases the dwell time; however, the discrimination is not improved compared to the "bullet-shaped" nanopore.
Keyphrases
  • single molecule
  • atomic force microscopy
  • living cells
  • heat shock protein
  • gold nanoparticles
  • molecularly imprinted
  • amino acid
  • multiple sclerosis
  • cognitive decline
  • brain injury
  • data analysis