Special Issue: Plant Metabolomics

The Special Issue is designed to provide an insight on the variability of the possible applications of the metabolomic approach within the field of plant sciences. The possibility to relatively quantify and putatively annotate hundreds to thousands of compounds has enabled scientists to obtain data on plant functions in previously unimaginable detail. Though the field is still evolving at a rapid pace, it is now clear that metabolomics has already become an efficient tool in the toolkit of colleagues studying secondary metabolism, plant physiology, plant-microbe interactions and several other fields, opening up new possibilities in agricultural, phytochemical and other sciences. The current Special Issue presents papers using various measurement techniques, plant genera and metabolite classes.

The only way to obtain information on absolute concentration, and unequivocal identification (i.e., distinguishment of isomers) is the usage of authentic standards. A few studies have used this approach, including, a study on Trollius polyphenolic substances [12], and an excellent study utilizing a wide range of iridoid glycosides, xanthones and flavonoids and as authentic standards [4]. In a headspace GC-MS study of endophytic fungi-derived volatile organic compounds (VOCs) [7], identification of compounds is done in an unambiguous manner with authentic standards; though in this case, quantitation is not that straightforward as in liquid injection techniques.
Specialized metabolites have always been important compounds in phytochemistry, and several studies were centered around hypotheses in this field: flavonoid glycosides and tannic-like compounds from Alnus species [14], and tomato priming by growth promoting rhizobacteria [5].
Other studies used metabolomic analysis of mainly primary metabolites (intermediates of core metabolism: amino acids, sugars, etc.) followed by pathway analysis to investigate various problems in plant physiology. This set includes a publication examining tomato fruit metabolites during Mg oversupply [15] and a paper on primary metabolism of mutant Arabidopsis lines during ABA treatment [16]. Excellent studies on primary metabolism also include one on partially submerged stress in deepwater rice [17], and a study on the effects of phenological development on the leaf metabolome of Linum usitatissimum [8], as well as a paper on Nicotiana nectar chemical profiles [9]. The article by Pontarin et al. [8] is an excellent example showing the complexity of plant metabolome, several concentration kinetics along time were presented, covering several compound classes.
Some papers made an in-depth analysis of both specialized and primary metabolites. These include a study on the variability of rapeseed compounds [18], a paper on metabolite changes in the important medicinal plant Pelargonium sidoides as a response to irrigation and nitrogen [19], as well as an excellent study on metabolomic response in grapevine wood after infection with a fungal pathogen, Neofusicoccum parvum [6].
The field is not without challenges; it is a rapidly evolving one with many issues upfront which need to be resolved in the future. First, while we have widely accepted protocols for quality control of the measurements that quantify compounds after calibration with authentic standards, the quality control in metabolomics is still a developing field [20]. As there are drifts in the sensitivity of the instruments, one has to execute special measures such as randomized injection order, application of quality control (QC) samples, LOESS fitting, among others [21], otherwise, merging datasets measured years after one another in large cohort studies would be rendered impossible. A series of QC (quality control) samples are usually used to resolve this issue, though there is a debate about how an optimal QC sample set is to be prepared, and all solutions have their compromises [22].
Compound annotation and data interpretation of data is also not an easy task-while transcriptomics and proteomics yield sequences that can be subjected to at least similarity studies, the identification of small molecules is not that straightforward as the structural variability is much higher [23]. This step is likely the most critical bottleneck of the metabolomic workflow recently. Putative annotation uses database matches, and many times results, in compounds that are likely the result of over-fitting-results can be like erroneously "finding" a wide array of halogenated pharmaceuticals in field plants because of improper database matches. Several tools exist that offer partially or fully automated annotation or extraction of chemical information from the MS/MS spectra [24][25][26][27], but results are always to be handled with special care. As a consequence, most features found in a study remain unannotated [23]. Data pre-treatment and evaluation is not less sensitive [28].
I think we all look forward to the upcoming advancements of this excellent technique in the future. Hereby, I would like to thank the authors for their contribution, the peer reviewers who helped scientific evaluation of the submissions, as well as the staff members of the Metabolites Editorial Office.

Conflicts of Interest:
The author declares no conflict of interest.