A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis
Abstract
Lewy body dementias (LBD), comprising dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), are defined by misfolded α-synuclein aggregation. Seed amplification assays (SAAs), such as RT-QuIC, enable sensitive detection of α-synuclein aggregates but typically provide binary readouts and require fluorescence labeling. Raman spectroscopy offers a label-free approach to detect subtle biochemical changes, and its diagnostic potential can be enhanced with machine learning. This proof-of-concept study aimed to evaluate whether Raman spectroscopy combined with machine learning can improve SAA-based discrimination of LBD from controls in cerebrospinal fluid (CSF). We analyzed a small number of post-mortem CSF samples from pathologically confirmed DLB (n = 2), PDD (n = 2), and controls (n = 2) using a 7-day SAA. Raman spectra were collected on Days 1, 4, and 7 and analyzed using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Following SAA, both PCA and UMAP distinguished combined LBD samples from controls within 24 h (Day 1), reflecting biochemical changes consistent with α-synuclein fibrillation. Spectral shifts indicated decreased α-helical content with increased β-sheet structures. No consistent separation between DLB and PDD was observed. This preliminary study demonstrates that combining Raman spectroscopy with machine learning can enable rapid, label-free detection of disease-specific changes. Despite the very limited sample size, these findings highlight the potential of this novel workflow and strongly warrant its validation in larger cohorts. Schematic overview of the modified SAA process. Monomeric recombinant α-synuclein was incubated with patient CSF and glass beads, before 7-day mechanical agitation. In disease patients, oligomeric α-synuclein recruits monomeric α-synuclein into growing fibrillar chains, which in turn are fragmented through mechanical agitation. These fragments form initial seeds, which elongate further, amplifying α-synuclein signals under Raman spectroscopy. • Raman spectroscopy detects α-synuclein aggregation after seed amplification. • Machine learning distinguishes Lewy body dementia from controls in 24 h. • Label-free assay shows early α-helix to β-sheet structural transitions. • Potential rapid diagnostic tool for synucleinopathies.
Authors: Nathan P. Coles, Suzan Elsheikh, Alaa Gouda, Agathe Quesnel, Lucy Butler, Ojodomo J. Achadu, Meez Islam, Karunakaran Kalesh, Annalisa Occhipinti, Claudio Angione, Jon Marles‐Wright, David J. Koss, Alan J. Thomas, Tiago F. Outeiro, Panagiota S. Filippou, Ahmad Adam Khundakar
Published in: Journal of Neuroscience Methods (2025)