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Seed Amplification Assays for Parkinson's Disease: A Review of α-Synuclein Assays in Body Fluids and Tissues
Weijie Kong1, Katsuya Satoh1, Mika Inada Shimamura1
1Department of Health Sciences, Unit of Medical and Dental Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
Abstract:
The pathological core of Parkinson's disease and related synucleinopathies involves the misfolding and aggregation of α-synuclein. Seed amplification assays (SAAs) have revolutionized the detection of pathological α-synuclein by enabling highly sensitive and specific identification of seeding activity. Cerebrospinal fluid (CSF)-based real-time quaking-induced conversion (RT-QuIC) demonstrates exceptional diagnostic accuracy for sporadic Parkinson's disease (PD) and dementia with Lewy bodies (DLB), with sensitivity reaching 93.3%-94.6% and pooled specificity of 94% (95% CI: 0.92-0.96), consistent with the meta-analysis of 21 core CSF studies. Alternative samples such as skin and intestinal tissues offer diagnostic accuracy up to 94.1%, providing less invasive options. These assays can distinguish conformational differences between Parkinson's disease/Lewy body dementia and multiple system atrophy (MSA), revealing potential for differential diagnosis through strain typing. In prodromal screening, SAAs show remarkable utility, with positivity rates exceeding 80% in idiopathic rapid eye movement sleep behavior disorder (iRBD) cohorts, indicating detection years before clinical diagnosis. Despite these advances, current limitations include small-sample sizes in many studies, insufficient multicenter validation, and lack of standardized protocols affecting interlaboratory consistency. Future efforts should focus on establishing standardized procedures, integrating digital biomarkers, and validating these technologies across diverse populations and disease stages to facilitate widespread clinical implementation. This study systematically evaluates the diagnostic performance of α-synuclein SAAs across biological samples, their role in differential diagnosis, and their potential in prodromal prediction and disease monitoring.
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