Metabolic profiling of body fluids and multivariate data analysis.

MethodsX

Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg; Braunschweig Integrated Centre of Systems Biology, University of Braunschweig, Rebenring 56, Braunschweig, Germany; Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany.

Published: February 2017

AI Article Synopsis

  • Metabolome analyses of body fluids face challenges from pre-analytical variations and dynamic biochemical changes, making sample handling essential for quality and reproducibility.
  • The metabolomics analysis process consists of four main steps: sample collection, metabolite extraction, data acquisition, and data analysis, with a specific protocol for gas chromatography-mass spectrometry (GC-MS) applied to various body fluids.
  • Advantages of this GC-MS protocol include robust results despite sample variations, minimal sample volume requirements, rapid and cost-effective processing, and the use of logistic regression for detailed biomarker analysis.

Article Abstract

Metabolome analyses of body fluids are challenging due pre-analytical variations, such as pre-processing delay and temperature, and constant dynamical changes of biochemical processes within the samples. Therefore, proper sample handling starting from the time of collection up to the analysis is crucial to obtain high quality samples and reproducible results. A metabolomics analysis is divided into 4 main steps: 1) Sample collection, 2) Metabolite extraction, 3) Data acquisition and 4) Data analysis. Here, we describe a protocol for gas chromatography coupled to mass spectrometry (GC-MS) based metabolic analysis for biological matrices, especially body fluids. This protocol can be applied on blood serum/plasma, saliva and cerebrospinal fluid (CSF) samples of humans and other vertebrates. It covers sample collection, sample pre-processing, metabolite extraction, GC-MS measurement and guidelines for the subsequent data analysis. Advantages of this protocol include: •Robust and reproducible metabolomics results, taking into account pre-analytical variations that may occur during the sampling process•Small sample volume required•Rapid and cost-effective processing of biological samples•Logistic regression based determination of biomarker signatures for in-depth data analysis.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5329063PMC
http://dx.doi.org/10.1016/j.mex.2017.02.004DOI Listing

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