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Related Concept Videos

Amyloid Fibrils03:03

Amyloid Fibrils

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Amyloid fibrils are aggregates of misfolded proteins.  Under most circumstances, misfolded proteins are either refolded by chaperone proteins or degraded by the proteasome. However, in the case of a mutation or a disease, these proteins can accumulate to form large clusters and often further assemble to form elongated fibers, called fibrils. 
Amyloid deposits were observed as early as 1639 in the liver and the spleen.   In 1854, Rudolph Virchow performed iodine staining,...
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Related Experiment Video

Updated: Nov 10, 2025

Biochemical Purification and Proteomic Characterization of Amyloid Fibril Cores from the Brain
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Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing Between Two Centres.

Diana Canetti1, Francesca Brambilla2, Nigel B Rendell1

  • 1Wolfson Drug Discovery Unit and National Amyloidosis Centre, Centre for Amyloidosis and Acute Phase Proteins, Division of Medicine, University College London, London WC1E6BT, UK.

Molecules (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Comparing amyloid proteomics data between centers showed high concordance, validating bioinformatics tools for accurate clinical diagnosis of this rare protein-folding disease.

Keywords:
LC-MS/MS raw data exchangeamyloid proteomicsproteomics platformsproteomics results validation

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Area of Science:

  • Biochemistry
  • Proteomics
  • Medical Diagnostics

Background:

  • Amyloidosis is a rare disease caused by abnormal protein fiber deposition, impacting tissue function.
  • Proteomic analysis of patient biopsies is vital for diagnosing amyloidosis and identifying specific types.
  • The European Proteomics Amyloid Network (EPAN) facilitates best practice sharing in amyloid proteomics.

Purpose of the Study:

  • To compare proteomics results between the National Amyloidosis Centre (NAC) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR).
  • To evaluate the quality and confidence of protein identification results using different software and algorithms.
  • To validate the performance of bioinformatics tools used in clinical amyloidosis diagnosis.

Main Methods:

  • Sharing of mass spectrometric raw data between NAC and ITB-CNR.
  • Analysis of data using multiple software platforms: Mascot, Scaffold, Proteome Discoverer, Sequest, and bespoke algorithms.
  • Comparison of protein identification results obtained from different bioinformatics tools.

Main Results:

  • A high concordance was observed between the proteomics results from the two centers.
  • The study suggests good accuracy of the various bioinformatics tools employed.
  • Inter-center data sharing proved effective for validating software performance.

Conclusions:

  • Inter-center data exchange is a valuable method for testing and validating proteomics software.
  • Accurate bioinformatics tools are crucial for reliable clinical diagnosis in amyloidosis.
  • Collaborative efforts enhance the reliability of proteomics data in diagnosing rare diseases.