Journal Article

DZNE-2025-01311

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.

Coles, N. P. ; Elsheikh, S. ; Gouda, A. ; Quesnel, A. ; Butler, L. ; Achadu, O. J. ; Islam, M. ; Kalesh, K. ; Occhipinti, A. ; Angione, C. ; Marles-Wright, J. ; Koss, D. J. ; Thomas, A. J. ; Outeiro, T. F.DZNE* ; Filippou, P. S. ; Khundakar, A. A.

2026
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.jneumeth.2025.110617

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.

Keyword(s): Humans (MeSH) ; Lewy Body Disease: cerebrospinal fluid (MeSH) ; Lewy Body Disease: diagnosis (MeSH) ; alpha-Synuclein: cerebrospinal fluid (MeSH) ; Spectrum Analysis, Raman: methods (MeSH) ; Machine Learning (MeSH) ; Male (MeSH) ; Female (MeSH) ; Aged (MeSH) ; Aged, 80 and over (MeSH) ; Proof of Concept Study (MeSH) ; Parkinson Disease: cerebrospinal fluid (MeSH) ; Parkinson Disease: diagnosis (MeSH) ; Principal Component Analysis (MeSH) ; Biomarkers: cerebrospinal fluid (MeSH) ; Diagnostics ; Lewy body dementia ; Machine learning analysis ; Raman spectroscopy ; α-synuclein aggregation ; alpha-Synuclein ; Biomarkers

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Contributing Institute(s):

1. Epigenetics and Systems Medicine in Neurodegenerative Diseases (AG Fischer)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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