%0 Journal Article
%A Coles, Nathan P
%A Elsheikh, Suzan
%A Gouda, Alaa
%A Quesnel, Agathe
%A Butler, Lucy
%A Achadu, Ojodomo J
%A Islam, Meez
%A Kalesh, Karunakaran
%A Occhipinti, Annalisa
%A Angione, Claudio
%A Marles-Wright, Jon
%A Koss, David J
%A Thomas, Alan J
%A Outeiro, Tiago F
%A Filippou, Panagiota S
%A Khundakar, Ahmad A
%T A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.
%J Journal of neuroscience methods
%V 425
%@ 0165-0270
%C Amsterdam [u.a.]
%I Elsevier Science
%M DZNE-2025-01311
%P 110617
%D 2026
%X 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.
%K Humans
%K Lewy Body Disease: cerebrospinal fluid
%K Lewy Body Disease: diagnosis
%K alpha-Synuclein: cerebrospinal fluid
%K Spectrum Analysis, Raman: methods
%K Machine Learning
%K Male
%K Female
%K Aged
%K Aged, 80 and over
%K Proof of Concept Study
%K Parkinson Disease: cerebrospinal fluid
%K Parkinson Disease: diagnosis
%K Principal Component Analysis
%K Biomarkers: cerebrospinal fluid
%K Diagnostics (Other)
%K Lewy body dementia (Other)
%K Machine learning analysis (Other)
%K Raman spectroscopy (Other)
%K α-synuclein aggregation (Other)
%K alpha-Synuclein (NLM Chemicals)
%K Biomarkers (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:41238048
%R 10.1016/j.jneumeth.2025.110617
%U https://pub.dzne.de/record/282548