The European Physical Journal E

, Volume 33, Issue 4, pp 273–282 | Cite as

Tracking the heterogeneous distribution of amyloid spherulites and their population balance with free fibrils

  • V. Foderà
  • A. M. Donald
Regular Article


The analysis of amyloidogenic systems reveals the appearance of distinct states of aggregation for amyloid fibrils. For different proteins and under specific experimental conditions, amyloid spherulites are recognized as a significant component occurring in several protein model systems used for in vitro fibrillation studies. In this work we have developed an approach to characterize solutions containing a mixture of amyloid spherulites and individual fibrils. Using bovine insulin as the model system, sedimentation kinetics for the amyloid aggregates were followed using a combination of UV-Vis spectroscopy and cross-polarized optical microscopy. Spherulites were identified as the species undergoing sedimentation. A simple mathematical approach allows the description of the kinetics in terms of decay time/rate distribution. Moreover, based on the sedimentation kinetics, a rough estimate of the balance between amyloid spherulites and individual fibrils can be provided. Fitting the experimental data with the proposed physico-chemical approach shows self-consistent results in reasonable agreement with quantitative imaging analysis previously reported. Our results provide new physical insights into the analysis of amyloidogenic systems, providing a method to characterize the heterogeneous distribution of amyloid spherulites and simultaneously distinguish spherulites and free fibril populations. Importantly, the method can be generally applied to the characterization of polydisperse solutions containing optically traceable spherical particles in the micrometric range.


Bovine Insulin Absorbance Signal Amyloid Aggregate Sedimentation Kinetic Small Spherulite 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • V. Foderà
    • 1
  • A. M. Donald
    • 1
  1. 1.Sector of Biological and Soft Systems, Department of Physics, Cavendish LaboratoryUniversity of CambridgeCambridgeUK

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