Home > Publications Database > A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis. > print

001     282548
005     20251218103441.0
024 7 _ |a 10.1016/j.jneumeth.2025.110617
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024 7 _ |a 0165-0270
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024 7 _ |a 1872-678X
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037 _ _ |a DZNE-2025-01311
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Coles, Nathan P
|b 0
245 _ _ |a A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis.
260 _ _ |a Amsterdam [u.a.]
|c 2026
|b Elsevier Science
336 7 _ |a article
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336 7 _ |a ARTICLE
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520 _ _ |a 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.
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650 _ 7 |a Diagnostics
|2 Other
650 _ 7 |a Lewy body dementia
|2 Other
650 _ 7 |a Machine learning analysis
|2 Other
650 _ 7 |a Raman spectroscopy
|2 Other
650 _ 7 |a α-synuclein aggregation
|2 Other
650 _ 7 |a alpha-Synuclein
|2 NLM Chemicals
650 _ 7 |a Biomarkers
|2 NLM Chemicals
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Lewy Body Disease: cerebrospinal fluid
|2 MeSH
650 _ 2 |a Lewy Body Disease: diagnosis
|2 MeSH
650 _ 2 |a alpha-Synuclein: cerebrospinal fluid
|2 MeSH
650 _ 2 |a Spectrum Analysis, Raman: methods
|2 MeSH
650 _ 2 |a Machine Learning
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Aged, 80 and over
|2 MeSH
650 _ 2 |a Proof of Concept Study
|2 MeSH
650 _ 2 |a Parkinson Disease: cerebrospinal fluid
|2 MeSH
650 _ 2 |a Parkinson Disease: diagnosis
|2 MeSH
650 _ 2 |a Principal Component Analysis
|2 MeSH
650 _ 2 |a Biomarkers: cerebrospinal fluid
|2 MeSH
700 1 _ |a Elsheikh, Suzan
|b 1
700 1 _ |a Gouda, Alaa
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700 1 _ |a Quesnel, Agathe
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700 1 _ |a Butler, Lucy
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700 1 _ |a Achadu, Ojodomo J
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700 1 _ |a Islam, Meez
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700 1 _ |a Kalesh, Karunakaran
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700 1 _ |a Occhipinti, Annalisa
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700 1 _ |a Angione, Claudio
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700 1 _ |a Marles-Wright, Jon
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700 1 _ |a Koss, David J
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700 1 _ |a Thomas, Alan J
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700 1 _ |a Outeiro, Tiago F
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700 1 _ |a Filippou, Panagiota S
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700 1 _ |a Khundakar, Ahmad A
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773 _ _ |a 10.1016/j.jneumeth.2025.110617
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
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