Plant Metabolomics: From Holistic Data to Relevant Biomarkers

Jean-Luc      Wolfender; Serge      Rudaz; Young      Hae Choi; Hye      Kyong Kim

doi:10.2174/0929867311320080009

Abstract

Metabolomics is playing an increasingly important role in plant science. It aims at the comprehensive analysis of the plant metabolome which consists both of primary and secondary metabolites. The goal of metabolomics is ultimately to identify and quantify this wide array of small molecules in biological samples. This new science is included in several systems biology approaches and is based primarily on the unbiased acquisition of mass spectrometric (MS) or nuclear magnetic resonance (NMR) data from carefully selected samples. This approach provides the most ‘‘functional’’ information of the ‘omics’ technologies of a given organism since metabolites are the end products of the cellular regulatory processes. The application of state-of-the-art data mining, that includes various untargeted and targeted multivariate data analysis methods, to the vast amount of data generated by this data-driven approach leads to sample classification and the identification of relevant biomarkers. The biological areas that have been successfully studied by this holistic approach include global metabolite composition assessment, mutant and phenotype characterisation, taxonomy, developmental processes, stress response, interaction with the environment, quality control assessment, lead finding and mode of action of botanicals.

This review summarises the main MS- and NMR-based approaches that are used to perform these studies and discusses the potential and current limitations of the various methods. The intent is not to provide an exhaustive overview of the field, which has grown considerably over the past decade, but to summarise the main strategies that are used and to discuss the potential and limitations of the different approaches as well as future trends.

Keywords: Metabolomics, profiling, plants, natural products, lead finding, stress response, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), multivariate data analysis