Cargando…

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

[Image: see text] 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 s...

Descripción completa

Detalles Bibliográficos
Autores principales: Paul, Simanta Sarani, Lyons, Aaron, Kirchner, Russell, Woodside, Michael T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2022
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620981/
https://www.ncbi.nlm.nih.gov/pubmed/36126253
http://dx.doi.org/10.1021/acsnano.2c05739
Descripción
Sumario:[Image: see text] 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.