α‐Synuclein Seeds Amplification Assays for Parkinson's Disease and Related Synucleinopathies

Authors:
Publisher website
Google Scholar
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the pathological misfolding and aggregation of α‐synuclein (α‐syn), which leads to dopaminergic neuronal loss and multisystem dysfunction. Conventional clinical diagnosis is often delayed until after substantial neuronal degeneration has occurred. Recent advances in α‐syn seed amplification assays (α‐syn SAAs), including real‐time quaking‐induced conversion (RT‐QuIC) and protein misfolding cyclic amplification (PMCA), have revolutionized PD biomarker research. These assays exploit the prion‐like seeding and propagation properties of pathological α‐syn to amplify trace aggregates in biological samples with exceptional sensitivity and specificity. Here, we summarize the mechanistic principles and biophysical underpinnings of α‐syn SAAs, compare their diagnostic performance across biospecimens such as cerebrospinal fluid, skin, blood, and saliva, and evaluate their potential for differential diagnosis among synucleinopathies. We further discuss the integration of SAAs with other biomarkers including α‐syn oligomer ELISAs, neurofilament light chain assays, and dopaminergic PET/SPECT imaging. Emerging developments, such as same‐day RT‐QuIC, quantitative kinetic readouts, and peripheral blood‐based assays, are accelerating clinical translation. Despite challenges in assay standardization, strain discrimination, and regulatory validation, α‐syn SAAs hold transformative potential for early diagnosis, patient stratification, and monitoring of therapeutic efficacy in PD. Their convergence with artificial intelligence, organoid modeling, and multimodal biomarker frameworks promises to redefine precision medicine in PD and other synucleinopathies.
Funding Information
  • National Medical Research Council
  • National Natural Science Foundation of China (82171243)

This publication has 57 references indexed in Scilit: