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Updated: Feb 11, 2026

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Tissue Multiplatform-Based Metabolomics/Metabonomics for Enhanced Metabolome Coverage.

Panagiotis A Vorkas1, M R Abellona U2, Jia V Li2,3

  • 1Section of Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK. p.vorkas@imperial.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|April 15, 2018
PubMed
Summary

This study presents a new workflow for analyzing tissue metabolism, improving metabolite detection and data integration for various tissue types. This method overcomes challenges in tissue analysis, enhancing disease pathology research.

Keywords:
CoverageExtractionHILICLipidomeLipidomicsMSEMetabolic phenotypingMetabolic profilingMetabolomeMetabolomicsMetabonomicsMultiplatformNMRTissueUPLC-MS

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Area of Science:

  • Metabolomics and Systems Biology
  • Biochemical Analysis
  • Disease Pathology Research

Background:

  • Tissue analysis offers direct insights into disease pathology and intervention targets.
  • Challenges in tissue metabolomics include heterogeneity and molecule detection limitations compared to biofluids.

Purpose of the Study:

  • To develop a multiplatform, multi-method workflow for comprehensive metabolic profiling of tissue samples.
  • To enhance metabolome coverage and enable robust metabolite detection in diverse tissue types.

Main Methods:

  • A dual consecutive extraction (organic followed by aqueous) was employed.
  • Tissue extracts were analyzed using reversed-phase (RP-) and hydrophilic interaction liquid chromatography (HILIC-) ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS).
  • Nuclear magnetic resonance (NMR) spectroscopy was utilized for metabolic profiling.

Main Results:

  • The workflow demonstrated enhanced metabolome coverage and expanded the range of robustly detected metabolites.
  • Quality control features were incorporated, enhancing versatility and supporting data integration.
  • The pipeline was successfully applied to a wide range of tissue types.

Conclusions:

  • The developed workflow provides a robust and versatile approach for tissue metabolic profiling.
  • This method enhances the ability to investigate disease pathology and potential interventions directly within tissue samples.
  • The pipeline supports provisional aliquoting for future analyses and facilitates data integration.