4.8 Article

Quantifying Oligomer Populations in Real Time during Protein Aggregation Using Single-Molecule Mass Photometry

Journal

ACS NANO
Volume 16, Issue 10, Pages 16462-16470

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsnano.2c05739

Keywords

protein aggregation; misfolded oligomers; tau; kinetic mechanism; mass photometry

Funding

  1. Alberta Innovates [G2018000860]
  2. Natural Sciences and Engineering Research Council of Canada [RTI-2020-00301]
  3. Alberta Innovates Graduate Fellowship

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Protein aggregation is a characteristic of many neurodegenerative diseases. This study used single-molecule mass photometry to track the populations of different oligomers during aggregation of tau protein. The results revealed an equilibrium between monomers, dimers, and trimers before aggregation, and a continuous decrease in monomer population accompanied by an increase in smaller oligomers and subsequent incorporation into larger oligomers and fibrils during aggregation.
Protein aggregation is a hallmark of many neurodegenerative diseases. The early stages of the aggregation cascade are crucial because small oligomers are thought to be key neurotoxic species, but they are difficult to study because they feature heterogeneous mixtures of transient states. We show how the populations of different oligomers can be tracked as they evolve over time during aggregation using single-molecule mass photometry to measure individually the masses of the oligomers present in solution. By applying the approach to tau protein, whose aggregates are linked to diseases including Alzheimer's and frontotemporal dementia, we found that tau existed in an equilibrium between monomers, dimers, and trimers before aggregation was triggered. Once aggregation commenced, the monomer population dropped continuously, paired first with a rise in the population of the smallest oligomers and then a steep drop as the protein was incorporated into larger oligomers and fibrils. Fitting these populations to kinetic models allowed different models of aggregation to be tested, identifying the most likely mechanism and quantifying the microscopic rates for each step in the mechanism. This work demonstrates a powerful approach for the characterization of previously inaccessible regimes in protein aggregation and building quantitative mechanistic models.

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