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Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics.

Antonia Fecke1,2, Nay Min Min Thaw Saw1, Dipali Kale1

  • 1Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany.

Metabolites
|July 29, 2023
PubMed
Summary

This study introduces a new workflow for accurately quantifying numerous metabolites in biological samples using liquid chromatography-mass spectrometry (LC-MS). The method enhances large-scale metabolomics by correcting for sample matrix effects, improving data reliability.

Keywords:
LC-MSmetabolite quantificationmetabolomicsquantitative spectral libraryrelative response factor

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Area of Science:

  • Biomedical Science
  • Analytical Chemistry
  • Metabolomics

Background:

  • Accurate metabolite quantification is crucial for clinical and biomedical research.
  • Biological sample matrices and LC-MS analysis complexities pose challenges for large-scale metabolomics.
  • Compound response in LC-MS is affected by various factors including physicochemical properties and analytical conditions.

Purpose of the Study:

  • To develop and validate an integrated analytical and computational workflow for large-scale metabolite quantification.
  • To evaluate the relative response factor (RRF) approach for correcting matrix effects in LC-MS.
  • To create a shareable quantitative LC-MS library for metabolomics data processing.

Main Methods:

  • Utilized the relative response factor (RRF) approach to correct for matrix effects in LC-MS analysis.
  • Developed a quantitative LC-MS library using the Skyline/Panorama web platform.
  • Adapted the Skyline software environment for metabolomics data processing.

Main Results:

  • Developed and validated a metabolomics method quantifying over 90 metabolites from a panel of 280+ standards.
  • The RRF quantification approach was validated against external calibration and literature data.
  • The workflow demonstrated suitability for large-scale metabolite quantification in human plasma samples.

Conclusions:

  • A novel quantitative chromatogram library and targeted data analysis workflow were developed for biomedical metabolomics.
  • The integrated workflow facilitates large-scale metabolite quantification by addressing matrix effects.
  • This approach enhances the clinical and biomedical translation of metabolomics research.