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

Area of Science:

• Neuroscience
• Biochemistry
• Spectroscopy

Background:

• Lewy body dementias (LBD), including dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), are characterized by alpha-synuclein aggregation.
• Seed amplification assays (SAAs) detect alpha-synuclein but offer binary readouts and require labeling.
• Raman spectroscopy provides label-free biochemical analysis, enhanced by machine learning for diagnostics.

Purpose of the Study:

• To assess if Raman spectroscopy and machine learning can improve SAA-based discrimination of LBD from controls in CSF.
• To evaluate the potential for rapid, label-free detection of LBD-specific biochemical changes.

Main Methods:

• Analysis of post-mortem CSF samples from DLB, PDD, and control groups using a 7-day SAA.
• Collection of Raman spectra on days 1, 4, and 7.
• Application of principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) for spectral analysis.

Main Results:

• PCA and UMAP distinguished LBD from controls within 24 hours (Day 1) post-SAA.
• Spectral shifts indicated changes in alpha-synuclein structure (decreased alpha-helix, increased beta-sheet).
• No clear separation was observed between DLB and PDD groups.

Conclusions:

• Combining Raman spectroscopy with machine learning enables rapid, label-free detection of disease-specific changes in CSF.
• This novel workflow shows potential for LBD diagnosis.
• Further validation in larger cohorts is warranted.