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Updated: Jun 28, 2026

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Establishing substantial equivalence: metabolomics.

Michael H Beale¹, Jane L Ward, John M Baker

¹National Centre for Plant and Microbial Metabolomics, Rothamsted Research, Harpenden, Hertfordshire, UK.

Methods in Molecular Biology (Clifton, N.J.)

|November 15, 2008

Summary

This summary is machine-generated.

Modern metabolomic methods, using nuclear magnetic resonance (NMR) fingerprinting, assess the safety of genetically modified (GM) wheat. This approach effectively screens for unintended effects by comparing metabolite profiles against natural environmental variations.

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published on: May 20, 2013

Area of Science:

Plant science
Biochemistry
Genetics

Background:

Metabolomic methods enable automated comparison of diverse compounds across numerous samples.
These techniques are valuable for screening plant populations, particularly for detecting unintended effects of genetic modification (GM).
Several metabolomic approaches exist for determining substantial equivalence in GM crops.

Purpose of the Study:

To develop and apply a [1H]-NMR fingerprinting methodology for metabolomic screening of plants.
To study the substantial equivalence of field-grown genetically modified (GM) wheat using this developed protocol.
To analyze flour from field plots of GM wheat to assess metabolomic profiles.

Main Methods:

Utilized [1H]-NMR fingerprinting for metabolomic screening.
Applied the protocol to analyze flour from field-grown GM wheat.
Employed downstream data processing and multivariate analysis for spectral comparison.

Main Results:

The methodology was successfully applied to field-grown GM wheat.
Data processing and multivariate analysis allowed for the assessment of metabolome changes.
The study could evaluate GM-induced metabolome alterations against natural environmental variation.

Conclusions:

[1H]-NMR fingerprinting provides a robust method for metabolomic screening of GM plants.
The approach facilitates the determination of substantial equivalence by comparing metabolite profiles.
This technique allows for the assessment of unintended GM effects within the context of natural variability.