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A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis

Lookup NU author(s): Dr Jon Marles-WrightORCiD, Professor Alan ThomasORCiD, Professor Tiago OuteiroORCiD, Dr Ahmad Khundakar

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2025 .Background 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. Objectives 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). Methods 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). Results 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. Conclusion 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.

Publication metadata

Author(s): Coles NP, Elsheikh S, Gouda A, Quesnel A, Butler L, Achadu OJ, Islam M, Kalesh K, Occhipinti A, Angione C, Marles-Wright J, Koss DJ, Thomas AJ, Outeiro TF, Filippou PS, Khundakar AA

Publication type: Article

Publication status: Published

Journal: Journal of Neuroscience Methods

Year: 2026

Volume: 425

Print publication date: 01/01/2026

Online publication date: 12/11/2025

Acceptance date: 02/11/2025

Date deposited: 01/12/2025

ISSN (print): 0165-0270

ISSN (electronic): 1872-678X

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.jneumeth.2025.110617

DOI: 10.1016/j.jneumeth.2025.110617

Data Access Statement: The codes generated and analyzed during the current study are openly available in the GitHub repository at https://github.com/NColes2812/RamanSpectralAnalysis

PubMed id: 41238048

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