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Amyloid structure and assembly: insights from scanning transmission electron microscopy

Claire Goldsbury1, Ulrich Baxa, Martha N Simon

  • 1The Brain and Mind Research Institute, University of Sydney, NSW 2006, Australia.

Journal of Structural Biology
|September 28, 2010
PubMed

Insights

Scanning transmission electron microscopy (STEM) measures amyloid fibril mass-per-length, revealing complex assembly pathways and polymorphisms. This technique aids structural modeling for diseases like Alzheimer's.

Area of Science:

  • Biophysics
  • Structural Biology
  • Biochemistry

Background:

  • Amyloid fibrils are protein aggregates linked to diseases like Alzheimer's and type II diabetes.
  • Similar structures, such as yeast prions, are involved in inherited traits.
  • Scanning transmission electron microscopy (STEM) is a key tool for studying amyloid structures.

Purpose of the Study:

  • To review the contributions and limitations of STEM in investigating amyloid assembly.
  • To explore STEM's role in understanding fibril polymorphisms and structural models.
  • To highlight STEM's unique ability to measure mass-per-length (MPL) of individual amyloid filaments.

Main Methods:

  • Utilizing scanning transmission electron microscopy (STEM) for mass-per-length (MPL) measurements.
  • Analyzing both in vitro assembled and ex vivo amyloid samples.
  • Integrating STEM data with high-resolution methods like solid-state nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR).

Main Results:

  • STEM mass measurements illuminate hierarchical relationships in amyloid fibril assembly.
  • Demonstrated that polymorphic fibrils and globular oligomers can co-assemble from a single polypeptide.
  • MPL data provides constraints for molecular model building of amyloid structures.

Conclusions:

  • STEM is crucial for understanding amyloid assembly pathways and structural diversity.
  • The technique enables direct mass measurements, offering unique insights into fibril formation.
  • STEM, combined with other methods, advances the molecular modeling of amyloid aggregates.

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