Leveraging Single Particle Fluorescence Spectroscopy For The Diagnosis Of Parkinson’s Disease And Other Synucleinopathies
Speaker: Associate Professor Emma Sierecki
University of New South Wales
Sydney
Leveraging Single Particle Fluorescence Spectroscopy For The Diagnosis Of Parkinson’s Disease And Other Synucleinopathies
Alpha-synuclein seed amplification assays (SAA) are emerging as sensitive and specific tools for detecting pathological α-synuclein (αSyn) aggregates in Parkinson’s disease and related synucleinopathies and offer early diagnosis. However, conventional SAAs provide limited quantitative information and do not inform on the molecular properties of the αSyn seeds. Here, we describe the development of a single-molecule SAA platform that combines the amplification principle of SAA with single-particle fluorescence spectroscopy to quantify and characterize αSyn assemblies in biological samples.
We first wanted to exploit the high analytical sensitivity of single-particle counting to establish a diagnostic method. Individual αSyn assemblies are detected as individual fluorescence bursts as they diffuse through a confocal observation volume, allowing their concentration to be measured. We analytically validated the assay and applied it to cerebrospinal fluid, but also blood-derived samples.
In parallel, we explore the potential of single-particle fingerprinting for differentiating between αSyn strains. Our previous work had shown that structurally distinct αSyn polymorphs display different amplification mechanisms and generate distinct single-molecule signatures. Extending this concept to a disease context, we find that αSyn assemblies secreted by patients-derived cellular models show differences in their amplification response. Analysis of changes in single-particle properties enables discrimination between molecularly distinct αSyn populations.
Overall, this work establishes single-molecule SAA as a platform for both quantitative detection and molecular characterization of αSyn seeds, relevant to differential diagnosis and patient stratification.
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