Recently, it has been hypothesized that alpha-synuclein protein strain morphology may be associated with clinical subtypes of alpha-synucleinopathies, like Parkinson's disease and multiple system atrophy. However, direct evidence is lacking due to the caveat of conformation-specific characterization of protein strain morphology. Here we present a new cell model based in vitro method to explore various alpha-synuclein (αsyn) aggregate morphotypes.
View Article and Find Full Text PDFAssays for quantifying aggregated and phosphorylated (S129) human α-synuclein protein are widely used to evaluate pathological burden in patients suffering from synucleinopathy disorders. Many of these assays, however, do not cross-react with mouse α-synuclein or exhibit poor sensitivity for this target, which is problematic considering the preponderance of mouse models at the forefront of pre-clinical α-synuclein research. In this project, we addressed this unmet need by reformulating two existing AlphaLISA SureFire Ultra™ total and pS129 α-synuclein assay kits to yield robust and ultrasensitive (LLoQ ≤ 0.
View Article and Find Full Text PDFAlpha-synuclein (α-syn) inclusions in the brain are hallmarks of so-called Lewy body diseases. Lewy bodies contain mainly aggregated α-syn together with some other proteins. Monomeric α-syn lacks a well-defined three-dimensional structure, but it can aggregate into oligomeric and fibrillar amyloid species, which can be detected using specific antibodies.
View Article and Find Full Text PDFPrevious studies have shown that aggregated alpha-synuclein (α-s) protein, a key pathological marker of Parkinson's disease (PD), can propagate between cells, thus participating in disease progression. This prion-like propagation has been widely studied using in vivo and in vitro models, including rodent and human cell cultures. In this study, our focus was on temporal assessment of functional changes during α-s aggregation and propagation in human induced pluripotent stem cell (hiPSC)-derived neuronal cultures and in engineered networks.
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