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

Aug 4, 2026

Lewy body dementias, including DLB and PDD, are driven by misfolded α-synuclein aggregation. Seed amplification assays can detect α-synuclein seeds but often yield binary readouts and usually need fluorescence labeling. This proof-of-concept evaluates whether Raman spectroscopy combined with machine learning improves seed amplification assay discrimination of Lewy body dementia from controls in cerebrospinal fluid.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Background

Seed amplification assays and limitations

Objectives

Methods

Sample design and SAA timeline

Raman spectral analysis

Results

Discrimination from controls

DLB vs PDD separation

Conclusion

Feasibility and validation needs

Journal of Neuroscience Methods 425 (2026) 110617 Contents lists available at ScienceDirect Journal of Neuroscience Methods journal [homepage: www.elsevier.com/locate/jneumeth](homepage: www.elsevier.com/locate/jneumeth) | Short communication
A modified α-synuclein seed amplification assay in Lewy body dementia using Raman spectroscopy and machine learning analysis
Nathan P. Colesa,b, Suzan Elsheikh a,b, Alaa Gouda a,b, Agathe Quesnel c, Lucy Butler a,b, Ojodomo J. Achadua,b, Meez Islam a,b, Karunakaran Kalesha,b, Annalisa Occhipintib,d,e, Claudio Angioneb,d,e, Jon Marles-Wright f, David J. Kossg, Alan J. Thomas h,
Tiago F. Outeiro i,j,k,l, Panagiota S. Filippou a,b,m, Ahmad A. Khundakara,b,i,*
a School of Health & Life Sciences, Teesside University, Middlesbrough TS1 3BX, United Kingdom b National Horizons Centre, Teesside University, Darlington DL1 1HG, United Kingdom c Research Center In Cancer De Toulouse, 2 Av. Hubert Curien, Toulouse 31100, France
d School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, United Kingdome Centre for Digital Innovation, Teesside University, Middlesbrough TS1 3BX, United Kingdom
f Biosciences Institute, Cookson Building, Framlington Place, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom g Division of Neuroscience, School of Medicine, University of Dundee, Nethergate, Dundee, Scotland DD1 4HN, United Kingdom h Newcastle Biomedical Research Centre, Newcastle University, Newcastle upon Tyne NE2 4HH, United Kingdom
i Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne United Kingdom
j University Medical Center G¨ottingen, Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, G¨ottingen, Germany k Max Planck Institute for Multidisciplinary Sciences, G¨ottingen, Germany
l Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), G¨ottingen, Germany
m Laboratory of Biological Chemistry, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece | | | | | --- | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Lewy body dementia Raman spectroscopy α-synuclein aggregation Machine learning analysis Diagnostics | | 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 RTQuIC, 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 poten

FAQs

What problem does the study address in Lewy body dementias diagnosis?

Lewy body dementias are defined by misfolded α-synuclein aggregation, and existing seed amplification assays can detect aggregates but often provide binary outputs and require fluorescence labeling. The study explores whether Raman spectroscopy with machine learning can enhance discrimination using a label-free approach.

How was Raman spectroscopy combined with seed amplification in the study?

A 7-day seed amplification assay was performed on post-mortem CSF samples, and Raman spectra were collected on Days 1, 4, and 7. Principal component analysis (PCA) and UMAP were then used to analyze the spectral data.

What were the main findings regarding distinguishing Lewy body dementia from controls?

After the seed amplification step, PCA and UMAP distinguished combined Lewy body dementia samples from controls within 24 hours (Day 1). Spectral shifts suggested decreased α-helical content and increased β-sheet structures, consistent with α-synuclein fibrillation.

Aug 4, 2026

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