Abstract
Metabolomics approaches provide a vast array of analytical datasets, which require a comprehensive analytical, statistical, and biochemical workflow to reveal changes in metabolic profiles. The biological interpretation of mass spectrometric metabolomics results is still obstructed by the reliable identification of the metabolites as well as annotation and/or classification. In this work, the whole Lemna minor (common duckweed) was extracted using various solvents and analyzed utilizing polarity-extended liquid chromatography (reversed-phase liquid chromatography (RPLC)-hydrophilic interaction liquid chromatography (HILIC)) connected to two time-of-flight (TOF) mass spectrometer types, individually. This study (introduces and) discusses three relevant topics for the untargeted workflow: (1) A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two different mass spectrometers using the same plant material type. (2) A statistical procedure was observed prioritizing significant detected features (dependent and independent of the mass spectrometer using the predictive methodology Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). (3) Relevant features were transferred to a prioritization tool (the FOR-IDENT platform (FI)) and were compared with the implemented compound database PLANT-IDENT (PI). This compound database is filled with relevant compounds of the Lemnaceae, Poaceae, Brassicaceae, and Nymphaceae families according to analytical criteria such as retention time (polarity and LogD (pH 7)) and accurate mass (empirical formula). Thus, an untargeted analysis was performed using the new tool as a prioritization and identification source for a hidden-target screening strategy. Consequently, forty-two compounds (amino acids, vitamins, flavonoids) could be recognized and subsequently validated in Lemna metabolic profile using reference standards. The class of flavonoids includes free aglycons and their glycosides. Further, according to our knowledge, the validated flavonoids robinetin and norwogonin were for the first time identified in the Lemna minor extracts.
Highlights
Metabolomics is the approach that deals with the investigation of a biological system by determining its overall metabolite profile at a given time point with the specified set of conditions
Several analytical techniques are available in the untargeted metabolomics analysis such as liquid chromatography-mass spectrometry (LCMS) and gas chromatography-mass spectrometry (GC-MS) [2] to analyze a large number of different chemical metabolites classes within one single analysis, respectively
A comparison study of metabolome samples was performed with an untargeted data handling workflow in two different labs with two mass spectrometers (TOF and quadrupole time-of-flight (QTOF)) using the same plant material type; Further, the metabolomics data from different mass spectrometers were analyzed and compared with predictive methodology orthogonal partial least squares—discriminant analysis (OPLS-DA)
Summary
Metabolomics is the approach that deals with the investigation of a biological system (cell, tissue, or organism) by determining its overall metabolite profile at a given time point with the specified set of conditions. The modern development of analytical techniques expanded the coverage of metabolomics in investigations of biological systems. This approach permits remarkable insights into regulation mechanisms as well as the responses to different perturbations. The identification of (unknown) metabolites is the fundamental step to transform analytical data into biological knowledge. This transformation is still considered the major bottleneck. Several analytical techniques are available in the untargeted metabolomics analysis such as liquid chromatography-mass spectrometry (LCMS) and gas chromatography-mass spectrometry (GC-MS) [2] to analyze a large number of different chemical metabolites classes within one single analysis, respectively. The usage of LC-MS has expanded rapidly over the past ten years in untargeted metabolomics analysis [3]
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