Comprehensive LC-MS-Based Metabolite Fingerprinting Approach for Plant and Fungal-Derived Samples
Liquid chromatography-mass spectrometry (LC-MS)-based nontargeted metabolome approaches aim to detect chemotypes as markers for stress, disease, developmental, or genetic perturbation. Herein, we present a metabolite fingerprinting workflow, which is appl
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Introduction Metabolite fingerprinting is a comprehensive and comparative nontargeted metabolomics approach [1]. It aims to describe biological processes on the metabolite level and to identify metabolites or even pathways as biomarker of environmental, developmental, or genetic perturbation [2–4]. This requires the coverage of almost all metabolites of the biological material during metabolite extraction, separation by LC, and detection by high- resolution mass spectrometry (HR-MS) [5, 6] as well as interactive tools for exploratory data analysis [7–11]. The ultra performance liquid chromatography coupled with a time-of-flight mass spectrometer (UPLC ESI-TOF MS)-based metabolite fingerprinting approach, which we describe here, has been established for the comprehensive metabolome analysis of plant and fungal-derived samples (Fig. 1). Beside the common Angelo D’Alessandro (ed.), High-Throughput Metabolomics: Methods and Protocols, Methods in Molecular Biology, vol. 1978, https://doi.org/10.1007/978-1-4939-9236-2_11, © Springer Science+Business Media, LLC, part of Springer Nature 2019
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Kirstin Feussner and Ivo Feussner
Biological material Homogenizaon Two-phase extracon Polar phase
Non-polar phase
Ultra performance liquid chromatography (UPLC) Electrospray ionizaon/me-of-flight mass spectrometry (ESI-TOF-MS) Sample set 1: Pos. ESI/polar
Sample set 2: Neg. ESI/polar
Sample set 3: Pos. ESI/non-polar
Sample set 4: Neg. ESI/non-polar
Data matrix 1: Pos. ESI/polar
Data matrix 2: Neg. ESI/polar
Data matrix 3: Pos. ESI/non-polar
Data matrix 4: Neg. ESI/non-polar
Fig. 1 Workflow of the metabolite fingerprinting approach. The workflow starts with the homogenization of the plant or fungal-derived material and proceeds via extraction and LC-MS analyses up to the generation of data matrixes
rimary metabolites, the metabolome of plants and fungi is characp terized by a huge variety of so-called secondary or specialized metabolites [12]. These highly diverse and species-specific compound classes enable the respective organism to survive challenging environmental conditions by adaptation processes, like activating defense programs against pathogens, and they play therefore a central role as metabolite markers [1, 13]. Our approach deals with the great variability and species specificity of the metabolome of plants and fungi at the level of metabolite extraction and analysis as well as during data processing and data mining. It has been applied for the analysis of a huge variety of plant species, like dicotyledons [14–17], monocotyledons [18, 19], mosses, and algae [20] and the respective plant organs (leaves, flowers, roots, hypocotyls, seedlings, gametophores, etc.) as well as for the mycelia of different fungi [21–24]. Besides these also plant and fungi-derived fluids, like xylem sap, apoplastic wash fluid, root extracts, and supernatants of fungal cultures were analysis with the described approach [17, 21, 24]. The workflow includes a two- phase extraction, which facilitates the extraction of polar,
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