Plasma LC-HRMS metabolomics in parkinsonian syndromes: comparative evaluation of sample preparation protocols for exploratory metabolic signatures.
Plasma LC-HRMS metabolomics in parkinsonian syndromes: comparative evaluation of sample preparation protocols for exploratory metabolic signatures.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Bologna, IT · Author affiliation
IRCCS, Istituto Delle Scienze Neurologiche di Bologna, Laboratorio di Proteomica Metabolomica e Chimica Bioanalitica, Bologna, Italy.Location evidence
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Original abstract
Untargeted plasma metabolomics by LC-HRMS is increasingly used to explore candidate biomarkers in neurodegenerative disease, yet the analytical output and downstream biological interpretability remain highly dependent on end-to-end workflow choices. This limits reproducibility and cross-study comparability in parkinsonian syndromes where objective fluid biomarkers for differential diagnosis are still lacking. Here, we implemented a QC-anchored, untargeted LC-HRMS plasma platform to compare sample preparation strategies and to explore disease-associated metabolic patterns across Parkinson's disease (PD), multiple system atrophy-parkinsonian subtype (MSA-P), and progressive supranuclear palsy-parkinsonism (PSP-P) in a prospective single-center cohort (n = 102; 57 PD, 19 MSA-P, 26 PSP-P). Three protein precipitation-based workflows were compared within a harmonized analytical framework: tube precipitation, 96-well precipitation-filtration, and 96-well phospholipid removal. Integrated preparation QCs, system QCs, and blanks supported performance monitoring, while MS2 annotation enabled interpretation of disease-associated signals. Phospholipid removal provided the most favorable trade-off for discovery and quantitation, delivering reduced phospholipid-related chromatographic background, improved quantitative behavior, and a higher density of MS2-supported annotations despite a modest reduction in raw feature counts. Differential abundance was assessed using empirical Bayes moderated linear models implemented in limma. Pairwise contrasts were defined for PD vs. MSA-P, PD vs. PSP-P, and MSA-P vs. PSP-P, and P values were adjusted using the Benjamini-Hochberg false-discovery rate (FDR). Across workflows, the most robust exploratory disease-associated pattern was observed for PD versus PSP-P, whereas MSA-P comparisons yielded limited FDR-significant features. Because of the absence of MSI Level 1 annotation, the hypothesis-generating pathway-level was based on exploratory metabolomics interpretation, requiring targeted confirmation.