Optimization of serum samples derivatization process by I-Optimal Split-plot design of experiments for GC–MS metabolomics of inflammatory neuropathies

[Display omitted] •I-Optimal split plot design was firstly used for GC-MS untargeted metabolomics of serum samples.•Interactions of the derivatization conditions were observed.•Biosynthesis of unsaturated fatty acids are altered in Inflammatory Neuropathies. The derivatization process is critical in...

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Bibliographic Details
Published in:Microchemical journal 2024-12, Vol.207, p.111822, Article 111822
Main Authors: Zamora-Obando, Hans Rolando, de Paula Andrade, Victória, Camelo, André Luiz Melo, do Santos, Flávia Bernardo, Dias, Aline Cristina, França Junior, Marcondes Cavalcante, Simionato, Ana Valéria Colnaghi
Format: Article
Language:English
Subjects:
GC–MS derivatization
Inflammatory neuropathies
Metabolomics
Optimization
Split-plot design
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Summary:[Display omitted] •I-Optimal split plot design was firstly used for GC-MS untargeted metabolomics of serum samples.•Interactions of the derivatization conditions were observed.•Biosynthesis of unsaturated fatty acids are altered in Inflammatory Neuropathies. The derivatization process is critical in gas chromatography coupled to mass spectrometry-based untargeted metabolomics. This process aims to increase common non-volatile compounds’ vapor pressure and thermal stability by two sequential steps: methoximation and silylation. Up to now, there are no standardized conditions for performing these reactions. In this study, we simultaneously optimized these procedures using an I-Optimal Split-plot design in serum samples of healthy individuals. The resultant two-factor interaction model performed well with R2 > 0.90 and R2-adjusted > 0.70 in the statistical analysis. The results revealed interactions between methoximation and silylation conditions. The optimized conditions were applied for the GC–MS untargeted metabolomics analysis of serum samples of patients with inflammatory neuropathies and healthy controls. The orthogonal partial least square discriminant analysis with satisfactory cross-validation results (R2 = 0.744, Q2 = 0.557) determined 32 metabolites with VIP score > 1. Short- and long-chain organic acids, fatty acids, and carbohydrates were annotated as potential discriminative molecular markers.
ISSN:0026-265X
DOI:10.1016/j.microc.2024.111822