TY  - JOUR
AU  - Coles, Nathan P
AU  - Elsheikh, Suzan
AU  - Gouda, Alaa
AU  - Quesnel, Agathe
AU  - Butler, Lucy
AU  - Achadu, Ojodomo J
AU  - Islam, Meez
AU  - Kalesh, Karunakaran
AU  - Occhipinti, Annalisa
AU  - Angione, Claudio
AU  - Marles-Wright, Jon
AU  - Koss, David J
AU  - Thomas, Alan J
AU  - Outeiro, Tiago F
AU  - Filippou, Panagiota S
AU  - Khundakar, Ahmad A
TI  - A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.
JO  - Journal of neuroscience methods
VL  - 425
SN  - 0165-0270
CY  - Amsterdam [u.a.]
PB  - Elsevier Science
M1  - DZNE-2025-01311
SP  - 110617
PY  - 2026
AB  - 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.
KW  - Humans
KW  - Lewy Body Disease: cerebrospinal fluid
KW  - Lewy Body Disease: diagnosis
KW  - alpha-Synuclein: cerebrospinal fluid
KW  - Spectrum Analysis, Raman: methods
KW  - Machine Learning
KW  - Male
KW  - Female
KW  - Aged
KW  - Aged, 80 and over
KW  - Proof of Concept Study
KW  - Parkinson Disease: cerebrospinal fluid
KW  - Parkinson Disease: diagnosis
KW  - Principal Component Analysis
KW  - Biomarkers: cerebrospinal fluid
KW  - Diagnostics (Other)
KW  - Lewy body dementia (Other)
KW  - Machine learning analysis (Other)
KW  - Raman spectroscopy (Other)
KW  - α-synuclein aggregation (Other)
KW  - alpha-Synuclein (NLM Chemicals)
KW  - Biomarkers (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:41238048
DO  - DOI:10.1016/j.jneumeth.2025.110617
UR  - https://pub.dzne.de/record/282548
ER  -