4.7 Article

Direct observation of heterogeneous formation of amyloid spherulites in real-time by super-resolution microscopy

Journal

COMMUNICATIONS BIOLOGY
Volume 5, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s42003-022-03810-1

Keywords

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Funding

  1. Lundbeck foundation [R250-2017-1293, R3462020-1759, R155-2013-14113]
  2. Carlsberg foundation [CF21-0659]
  3. Carlsberg foundation Distinguished Associate professor program [CF16-0797]
  4. NovoNordisk Center for Biopharmaceuticals and Biobarriers in Drug Delivery [NNF16OC0021948]
  5. Novo Nordisk foundation [NNF16OC0021948, NNF14CC0001]
  6. Villum foundation [10099, 19175]
  7. China Scholarship Council [201709110108]

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Real-time super-resolution microscopy analysis was used to study the growth kinetics, morphology, and abundance of human insulin amyloid spherulites with different growth pathways. The study revealed that even microscopically identical aggregates can follow distinct growth pathways.
Real-time super-resolution microscopy analysis reveals the growth kinetics, morphology, and abundance of human insulin amyloid spherulites with different growth pathways. Protein misfolding in the form of fibrils or spherulites is involved in a spectrum of pathological abnormalities. Our current understanding of protein aggregation mechanisms has primarily relied on the use of spectrometric methods to determine the average growth rates and diffraction-limited microscopes with low temporal resolution to observe the large-scale morphologies of intermediates. We developed a REal-time kinetics via binding and Photobleaching LOcalization Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies of human insulin, below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of microscopically identical aggregates, e.g., amyloid spherulites, may follow distinct pathways. Specifically, spherulites do not exclusively grow isotropically but, surprisingly, may also grow anisotropically, following similar pathways as reported for minerals and polymers. Combining our technique with machine learning approaches, we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape at the level of single aggregate morphology. Our unifying framework for the detection and analysis of spherulite growth can be extended to other self-assembled systems characterized by a high degree of heterogeneity, disentangling the broad spectrum of diverse morphologies at the single-molecule level.

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