Drought has serious effects on the physiology of cereal crops. At the cellular and specifically the metabolite level, many individual compounds are increased to provide osmoprotective functions, prevent the dissociation of enzymes, and to decrease the number of reactive oxygen species present in the cell. We have used a targeted GC-MS approach to identify compounds that differ in three different cultivars of bread wheat characterized by different levels of tolerance to drought under drought stress (Kukri, intolerant; Excalibur and RAC875, tolerant). Levels of amino acids, most notably proline, tryptophan, and the branched chain amino acids leucine, isoleucine, and valine were increased under drought stress in all cultivars. In the two tolerant cultivars, a small decrease in a large number of organic acids was also evident. Excalibur, a cultivar genotypically related to Kukri, showed a pattern of response that was more similar to Kukri under well-watered conditions. Under drought stress, Excalibur and RAC875 had a similar response; however, Excalibur did not have the same magnitude of response as RAC875. Here, the results are discussed in the context of previous work in physiological and proteomic analyses of these cultivars under drought stress.

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Primary and secondary amines, including amino acids, biogenic amines, hormones, neurotransmitters, and plant siderophores, are readily derivatized with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate using easily performed experimental methodology. Complex mixtures of these amine derivatives can be fractionated and quantified using liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS). Upon collision induced dissociation (CID) in a quadrupole collision cell, all derivatized compounds lose the aminoquinoline tag. With the use of untargeted fragmentation scan functions, such as precursor ion scanning, the loss of the aminoquinoline tag (Amq) can be monitored to identify derivatized species; and the use of targeted fragmentation scans, such as multiple reaction monitoring, can be exploited to quantitate amine-containing molecules. Further, with the use of accurate mass, charge state, and retention time, identification of unknown amines is facilitated. The stability of derivatized amines was found to be variable with oxidatively labile derivatives rapidly degrading. With the inclusion of antioxidant and reducing agents, tris(2-carboxyethyl)-phosphine (TCEP) and ascorbic acid, into both extraction solvents and reaction buffers, degradation was significantly decreased, allowing reproducible identification and quantification of amine compounds in large sample sets.

Liquid chromatography-mass spectrometry (LC-MS) methods using either aqueous normal phase (ANP) or reversed phase (RP) columns are routinely used in small molecule or metabolomic analyses. These stationary phases enable chromatographic fractionation of polar and non-polar compounds, respectively. The application of a single chromatographic stationary phase to a complex biological extract results in a significant proportion of compounds which elute in the non-retained fraction, where they are poorly detected because of a combination of ion suppression and the co-elution of isomeric compounds. Thus coverage of both polar and nonpolar components of the metabolome generally involves multiple analyses of the same sample, increasing the analysis time and complexity. In this study we describe a novel tandem in-line LC-MS method, in which compounds from one injection are sequentially separated in a single run on both ANP and RP LC-columns. This method is simple, robust, and enables the use of independent gradients customized for both RP and ANP columns. The MS signal is acquired in a single chromatogram which reduces instrument time and operator and data analysis errors. This method has been used to analyze a range of biological extracts, from plant and animal tissues, human serum and urine, microbial cell and culture supernatants. Optimized sample preparation protocols are described for this method as well as a library containing the retention times and accurate masses of 127 compounds.

Abiotic stress has major impacts on crop yields worldwide. In the field, multiple stresses often occur simultaneously, and interactions between these stresses will modify the physiological response of plants. We briefly review known tolerance mechanisms and describe approaches that led to their identification, and how new tolerance mechanisms may be identified using 'omics technologies. We then focus on metabolomics technologies including data analysis, and the results of their application in abiotic stress research. Integration of multiple 'omics datasets and their interpretation with existing genetic, physiological and morphological data is a challenge that is currently being addressed to enable further exploration and interpretation of metabolomic data. We conclude by discussing new developments in metabolomics, such as single cell measurements of stress responses and tissue-based mass spectrometry imaging that will offer greater spatial resolution of sub-cellular distribution of metabolites.

Abiotic stress has major impacts on crop yields worldwide. In the field, multiple stresses often occur simultaneously, and interactions between these stresses will modify the physiological response of plants. We briefly review known tolerance mechanisms and describe approaches that led to their identification, and how new tolerance mechanisms may be identified using ‘omics technologies. We then focus on metabolomics technologies including data analysis, and the results of their application in abiotic stress research. Integration of multiple ‘omics datasets and their interpretation with existing genetic, physiological and morphological data is a challenge that is currently being addressed to enable further exploration and interpretation of metabolomic data. We conclude by discussing new developments in metabolomics, such as single cell measurements of stress responses and tissue‐based mass spectrometry imaging that will offer greater spatial resolution of sub‐cellular distribution of metabolites.

In order to make sense of the sheer volume of metabolomic data that can be generated using current technology, robust data analysis tools are essential. We propose the use of the Growing Self-Organizing Map (GSOM) algorithm and by doing so demonstrate that a deeper analysis of metabolomics data is possible in comparison to the widely used Batch-Learning Self-Organizing Map (BL-SOM), Hierarchical Cluster Analysis (HCA) and Partitioning Around Medoids (PAM) algorithms on simulated and real-world time-course metabolomic datasets. We then applied GSOM to a recently published dataset representing metabolome response patterns of three wheat cultivars subject to a field simulated cyclic drought stress. This novel and information rich analysis provided by the proposed GSOM framework can be easily extended to other high-throughput metabolomics studies.