Gas Chromatography-Mass Spectrometry (GC-MS) in the Plant Metabolomics Toolbox: GC-MS in Multi-Platform Metabolomics...

Innovative developments of GC-MS over the last two decades made this methodology a powerful tool for profiling a broad range of volatile metabolites and non-volatile ones of non-polar, semi-polar and even polar nature after appropriate derivatization. Indeed, the high potential of GC-MS in the analysis of low molecular weight metabolites involved in essential cellular functions (energy production, metabolic adjustment, signaling) made it the method of choice for the life and plant scientists. However, despite these advances, due to their intrinsic thermal lability, multiple classes of hydrophilic low-molecule weight metabolites (like nucleotides, sugar phosphates, cofactors, CoA esters) are unsuitable under the high-temperature conditions of the split-splitless (SSL) injection and GC separation, which makes the analysis of such compounds by GC-MS challenging. Therefore, to ensure comprehensive coverage of the plant metabolome, the GC-MS-based metabolomics platform needs to be efficiently combined with other metabolomics techniques and instrumental strategies. Moreover, to get a deeper insight into dynamics of plant cell metabolism in response to endogenic and exogenic clues, integration of the metabolomics data with the output obtained from other post-genomics techniques is desired. Therefore, here, we overview different strategies for the integration of the GC-MS-based metabolite profiling output with the data, acquired by other metabolomics techniques in terms of the multi-platform metabolomics approach. Further, we comprehensively discuss the implementation of the GC-MS-based metabolomics in multi-omics strategies and the data integration strategies behind this. This approach is the promising strategy, as it gives deep and multi-level insight into physiological processes in plants in the systems biology context, with consideration of all levels of gene expression. However, multiple challenges may arise in the way of integrating data from different omics technologies, which are comprehensively discussed in this review.