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ISSN : 1225-5009(Print)
ISSN : 2287-772X(Online)
Flower Research Journal Vol.29 No.4 pp.213-222
DOI : https://doi.org/10.11623/frj.2021.29.4.01

Application of Plant Metabolomics Approach for Analysis of Volatile Compounds Synthesized in Plants

Seung Won Kang*
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8577, Japan


* Corresponding author: Seung won Kang Tel: +81-29-853-4807 E-mail:
kang.seungwon.ga@u.tsukuba.ac.jp
20/08/2021 23/08/2021 24/08/2021

Abstract


Volatile organic compounds (VOCs) in plants are various organic compounds with small molecular weight and high vapor pressure. The metabolomics approach was recently introduced to analyze VOCs involved in biological processes, such as abiotic and biotic stresses, spatial and temporal distribution, and genotypic differences. In addition, this approach is widely used in combination with identification of VOCs analysis and statistical analysis using multivariate analysis, such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA), etc. First, in this review, the current condition of the metabolomics approach to analyze VOCs synthesized in plants using head space-solid phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS) is discussed. In addition, metabolomics approach, such as extraction and analysis of VOCs using HS-SPME-GC-MS, conversion, and processing of mass spectral (MS) data, a database for VOCs identification, useful statistical methods, and statistical tools and applications, are explained. Finally, multi-omics in combination with other omics techniques, such as genomics, transcriptomics, etc. are suggested as prospects of a metabolomics approach for VOC analysis in floricultural plants using HS-SPMEGC- MS. Therefore, the metabolomics approach of HS-SPMEGC- MS will facilitate our understanding of VOCs synthesized in plants. Furthermore, the multi-omics approach will help understand gene functions involved in the biosynthesis of VOCs and help develop new development cultivars with nicer floral scents by contributing to the development of the floricultural industry.



gas chromatography-mass spectrometry (GC-MS) , head space (HS) , multiomics , multivariate analysis (MVA) , solid phase microextraction (SPME)

초록

    Introduction

    Volatile organic compounds (VOCs) of plants represent a mixture of various organic compounds with small molecular weight and high vapor pressure or high volatility. The majority of fragrance components are aliphatics, terpenoids, phenylpropanoids/benzenoids, etc. (Pichersky et al. 2006). By 1992, from 441 in 174 genera and about 60 families, more than 700 VOCs were identified using the head space technology (Knudsen et al. 1993). After about a decade, the number of additionally identified VOCs increased up to 1,700 VOCs (Knudsen et al. 2006). A huge number of VOCs were identified from diverse plant taxa such as 26 taxa from three genera in Magnoliaceae, 24 taxa from eight genera in Rosaceae, 21 taxa from twelve genera in Cactaceae, 21 taxa from three genera in Rutaceae, 21 taxa from eight genera in Solanaceae, Caryophyllaceae, Nyctaginaceae, etc. Overall, 991 taxa in 90 families have been used identify VOCs in plants (Knudsen et al. 2006).

    Plant metabolomics is the field of science to understand biological processes of a variety of organisms by analyzing metabolites with small molecular weight and has been widely used to develop biomarkers. To analyze lots of metabolites synthesized in plants, various types of analytical instruments are used such as capillary electrophoresis coupled to mass spectrometry (CE-MS), gas chromatography coupled to mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC–MS), nuclear magnetic resonance (NMR), etc. (Fukusaki and Kobayashi 2005;Kumar et al. 2017). Metabolites synthesized in plants under various type of environmental stresses (heat stress, drought stress, temperature, light, flooding, etc.) have been identified from many important agricultural crops (Das et al. 2017;Ma et al. 2016;Paupière et al. 2017;Saito 2018;Shelden et al. 2016;Sun et al. 2016;Thomason et al. 2018;Ueno and Sawaya 2019). In addition, biomarkers as a selective tool in breeding have been elucidated from Arabidopsis, barley, grape, maize, oats, tomato, etc. (Frenandez et al. 2016;Pretorius et al. 2021;Rubio et al. 2021;Steinfath et al. 2010). In recent, metabolomics approach has been introduced to identify and analyze VOCs involved in disease resistance, plant development and growth, resistance to biotic stresses (Iijima 2014;López-Gresa et al. 2017;Qualley and Dudareva 2009).

    During the last decade, advances in analytical instruments and application of statistical methods enabled deep understating of spatial and temporal change of VOCs in plants using metabolomics approach. In addition, high-throughput omics techniques have evolved and enabled to understand biological process of agronomically important crops in combination with genomics, transcriptomics, proteomics, metabolomics, phenomics, etc. (Saito 2018) (Fig. 1).

    Therefore, the aim of this review is to describe current condition of metabolomics approach to analyze VOCs synthesized in plants using HS-SPME-GC-MS, then to explain briefly the process from sampling to analysis of VOCs in plants, and finally to suggest future prospects of VOCs studies to improve quality of floricultural plants in floricultural industry.

    Extraction and analysis of VOCs using HS-SPME-GC-MS

    VOCs synthesized in plants are extracted by head space (HS) sampling technique and this can be largely categorized into two ways, static and dynamic HS sampling techniques. In static HS sampling, a solid-phase microextraction (SPME) technique is used, but closed-loop stripping, pull systems, push-pull systems, and online volatile collection systems belong to dynamic HS sampling technique (Tholl and Röse 2006). VOCs synthesized in plants are extracted by the HS-SPME technique. SPME was first reported and commercialized in 1990s and now one of the most popular techniques to extract VOCs from many ornamental flowers (Arthur and Pawliszyn 1990;Qualley and Dudareva 2009).

    GC-MS is used to detect and analyze VOCs because of low molecular weight range of VOCs. To analyze VOCs in plants, the head-space solid phase micro extraction (HS-SPME) technique coupled with GC-MS has been widely used because of HS-SPME is easy to handle and also known to provide high-throughput acquisition of biological data with very high accuracy and reproducibility (Arthur and Pawliszyn 1990). SPME does not require any complicated pre-treatment process unlike solvent extraction method which uses organic chemicals such as benzene, dichloromethane, ether, ethyl acetate, and hexane. In addition, HS-SPME can extract VOCs in a non-destructive way by allowing repetitive sampling of VOCs from one individual plant. Furthermore, extraction process is simple resulting in reduction of sample contamination and high reproducibility with a very high detection limit ranging from nano- to pico-gram.

    To analyze VOCs using HS-SPME-GC-MS technique, VOCs are absorbed by a long and thin fiber equipped in SPME. The fiber is made of silica or metals and is coated with the extraction phase. There are several type of extraction phases such as polydimethylsiloxane (PDMS), polyacrylate, poly ethylene glycol (PEG), divinylbenzene (DVB), carboxen (CAR), etc. PDMS is non-polar and one of the widely used as a stationary phase for extracting VOCs, non-polar semi-volatile compounds (SVOCs), etc. A mixed phase of fiber can be made by combining an individual extraction phase. PDMS/CAR/DVB is made of PDMS, CAR, and DVB and is used to extract VOCs or SVOCs between C3 and C20. PDMS/DVB is made of PDMS and DVB which is widely used fiber to absorb VOCs, amines, and aromatic nitro compounds. VOCs extracted by HS-SPME is, in general, desorbed at high temperature in the GC injector, but fibers can be immersed in a solvent for desorption of VOCs (Płotka et al. 2015).

    Conversion and processing of mass spectral data

    VOCs collected by HS-SPME are separated and fragmented through GC-MS and huge amount of mass spectral (MS) data are generated. VOCs are shown as a total ion chromatogram (TIC) in MS data and MS data also contain two important components, retention time and corresponding mass spectrum of a distinct compound. Then, data processing of TIC in MS data is needed for qualitative and quantitative analysis of VOCs. Data processing includes peak detection, ion extraction by deconvolution from a complex TIC, retention time (RI) calculation/calibration, compound identification, and alignment (Du and Zeisel 2013). Before MS data processing, conversion of data file format may necessary because a file format depends on the MS vendors (Table 1). Each vendor has a distinct data file format such as .D (Agilent), PEG (LECO), WIFF (Sciex), qgd (Shimadzu), RAW (Thermo and Waters), etc. and provides analytical software equipped to GC-MS for interpretation of MS data of their own file but it is not compatible to other data file formats from different vendors. In addition, those applications are too expensive for individuals to purchase so it is difficult to analyze MS data using a personal computer (Table 2).

    However, there are a couple of open-source software that allows processing MS data of different file formats. AMDIS (automated mass spectral deconvolution and identification system) is provided by the National Institute of Standards and Technology (NIST, USA, https://chemdata.nist.gov/dokuwiki/ doku.php?id=chemdata:amdis) and compatible with various data file formats suchas MSF, D, MS, acq, lrp, CDF, etc. OpenChrom® (Lablicate Gmbh, current version is OpenChrom Lablicate Edition ver. 1.4.x, https://lablicate.com/platform/ openchrom) is also an open-source software with high compatibility with various data file formats and those formats can be converted and exported to other file formats such as CDF, mzData, msp, msl, csv, mzML, mzxML (Pedrioli et al. 2004). In addition, identification of VOCs using OpenChrom® is possible by integrating library database (NIST MS library, Wiley database library, Massbank library, etc.) that will be discussed in the following section. MS-DIAL (http://prime.psc.riken.jp/) is one of the powerful open-source software which supports various file formats from different MS vendors. First, a vendor’s file format is converted to Analysis Base File (ABF) format using Reifycs Abf Converter (https://www.reifycs.com/AbfConverter/) for processing and analysis or mzML format using msconvert integrated in ProteoWizard (https://proteowizard.sourceforge.io/index.html, Chambers et al. 2012). Those converting software are also open-source software and easily downloadable. Other processing tools were summarized by Sugimoto et al. (2012). Some of web-based tools had stopped operation, but other web-based or local tools are still available; MetaboliteDetector (https://md.tu-bs.de/node/3, Hiller et al. 2009) and Metabolome Express (https://www.metabolome-express.org/, Carroll et al. 2010) are web-based application while MetAlign (https://www. wur.nl/en/show/MetAlign-1.htm, Lommen 2009) can be used as a stand-alone application. Mass++ (Mass plus plus), like Metaboanalyst, is an open-source software to visualize and analyze MS data (Tanaka et al. 2014).

    Mass spectral libraries to identify VOCs in plants

    An individual name of VOCs based on MS data can be identified using commercially available or open-source MS database (Table 3). NIST/EPA/NIH Mass Spectral Library (2020 edition) provided by NIST and Wiley Registry of Mass Spectral Data (12th edition) by Wiley are representative and commercially available libraries to identify the name of VOCs by comparing the corresponding ion chromatogram in MS data. NIST library provides 350,000 of EI spectral data and 1,320,000 of MS/MS data. Wiley library provides 785,061 of searchable Chemical Structures. The platform for RIKEN metabolomics (PRIMe, http://prime.psc.riken.jp/) is a web-based platform for metabolomics and transcriptomics research which is provided by Institute of Physical and Chemical Research (RIKEN, Japan). PRIMe provides 28,220 records of EI-MS database containing 9,062 unique compounds. Additional library can be downloadable, such as Fiehn BinBase DB, RIKEN DB, Kazusa DB, GL-Science DB, Osaka Univ. DB, or MoNA (MassBank or North America) volatile. MassBank is also open source platform which provides databases and tools for analyzing MS data. In addition, MassBank (http://www.massbank.jp/Index) also provides online based searching tools using basic compound information (name, mass, and/or chemical formula of compound), peak list, peaks, peak differences, InChIKey (international chemical identifier), or SPLASH (spectral hash) (Horai et al. 2010). Two MS data libraries are downloadable as a NIST msp format (.msp), the MassBank_NIST.msp and MassBank_RIKEN.msp. These libraries can be used by integrating MS-DIAL or OpenChrom® to identify VOCs in MS data (Fig. 2).

    A couple of online-based searching tool can be used as a supplementary identification of VOCs (Table 3). NIST Chemistry WebBook provides lots of information for a particular compound, such as chemical formula, molecular weight, InChlKey, CAS registry number, chemical structure, name of isomers, synonyms, mass spectrum obtained from electron ionization, GC data (Kovats` retention index, column type, and references), structure in 2d Mol (.mol) and 3d SD files (sdf). SpectraBaseTM is an online based free spectral database provided by WILEY. Finally, The Pherobase (https://www.pherobase.com/) is the database of pheromones and semiochemicals and this database provide chemical name and structure, mass spectrum, behavioral function to insects, list of plants in which a particular compound was reported, Kovats` retention index, column, references, etc. (El-Sayed 2021).

    Statistical analysis

    Multivariate analysis (MVA) is a statistical method which is mostly used in metabolomics (Worley and Powers 2013). In order to analyze MS data, peak intensity or concentration of targeted VOCs, selection of appropriate algorithm is a key in data analysis using MVA for precise determination of the experimental result. Various statistical methods are used in MVA, such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), hierarchical cluster analysis (HCA), and self-organizing mapping (SOM).

    Principal component analysis (PCA)

    To analyze MS data, principal component analysis (PCA) is performed by transforming the original data to principal components (principal axes) and is widely used to reduce the dimensionality of large datasets such as VOCs in plants. Data matrix is prepared using VOCs identified by GC-MS and the amount in peak intensity or concentrations of corresponding VOCs. The first and second principal components are determined by eigen values in eigenvector in linear algebra (Bro and Smilde 2014). PCA can identify and indicate useful information from the metabolome using a few principal components (van den Berg et al. 2006). Two information, the score and the loading, can be obtained by applying PCA to MD dataset. The loading represents the component which mostly contributed to the whole information in VOCs and provides the difference in each VOC among samples. On the other hand, the score represents the coordination of data vectors in PCA. The 2D score plot can be derived using the most significant components (the first and second principal component for 2D score plot) and this plot intuitively visualize the differences among samples. Therefore, the principal component is considered the most important constituent to distinguish differences between samples (Yamamoto et al. 2014).

    Partial least squares-discriminant analysis (PLS-DA)

    PLS-DA is one of the mostly used statistical tools in MVA to analyze datasets and is performed for classification and regression of different sample groups. PCA analyzes relationship of sample groups only using information of explanatory variable (independent variable) while PLS-DA deals with explanatory and dependent variables together. PLS generates new explanatory variables that clearly show the difference of dependent variables of the data (Gromski et al. 2015). In MVA using PLS-DA method, variable importance in projection (VIP) score is frequently used as a score representing the importance of variables and as an index of VOCs that affect differences between sample groups. Since the VIP score fluctuates greatly depending on the processing method of variables, an important variable in PLS-DA, in general, is considered whose VIP score is greater than 1.

    Hierarchical cluster analysis (HCA)

    HCA is widely used a statistical tool for chemotaxonomic analysis to group and visualize samples. Dendrogram is a two-dimensional diagram and represents clustering analysis based on the distance between a pair of data point and allows intuitive comparison between sample groups. There are a couple of linkage methods, such as single linkage clustering, complete linkage clustering, average linkage, and average group linkage. In addition, heatmap can be drawn from HCA and it enables intuitive comparison by visualizing a data table using quantitative and qualitative features of VOCs acquired from HS-SPME-GC-MS.

    Statistical tools and software

    In order to apply metabolomics approach to analyze VOCs in plants, as discussed above, a statistical method widely used is MVA including PCA, PLS-DA, orthogonal PLS-DA (OPLS-DA), heatmap or dendrogram in HCA, etc. There are commercially available applications, open-source software, web-based tools, or a statistical software using a spreadsheet software (Microsoft EXCEL) (Table 4). SIMCA® (Sartorius, German) and Aspen Unscrambler™ (Aspetech, USA) are commercial applications which are widely used in analysis of metabolomes in metabolomics. Metaboanalyst (version 5.0, https://www.metaboanalyst.ca/) is a web-based tool for metabolomics data processing and provides not only comprehensive functional and meta- analysis of mass spectral peaks but also exploratory statistical analysis (Pang et al. 2021). In addition, Metaboanalyst has an intuitive and user-friendly interface so it is easy to handle MS data obtained from GC-MS. This software provides thirteen modules depending on the input data type, such as raw specter (mzML, mzXML, or mzData), peaks of mass spectrometry, annotated features, and generic format (csv or txt). Statistics in Microsoft Excel (http://prime.psc.riken.jp/) is an excel based platform that enables statistical analysis such as PCA, PLS regression (PLS-R), and PLS-DA (Matsuo et al. 2017;Tsugawa et al. 2015). This platform is easy to handle and can generate some visualized information such as bar graph, line chart, PCA and PLS-R plots, etc. R (ver 4.1.1., https://www.r-project.org/) is the free software for statistical analysis and graphical representation of the results by integrating packages related to MVA, but it requires to learn and understand R language to analyze your data (R Development Core Team 2005). Rstudio (https://www.rstudio.com/) is the open source software and provides the integrated development environment for R to facilitate operation of R even if users do not know R language (RStudio Team 2020). Mass++ (Mass plus plus, https://www.mspp.ninja/?lang=en_us) is an open-source software basically to visualize and analyze MS data and also provides statistical analysis by integrating commercial plug-in provided from SIMCA® (Tanaka et al. 2014).

    Conclusion

    In this review, metabolomics approach of VOCs synthesized in plants using HS-SPME-GC-MS is discussed by introducing extraction and analysis of VOCs, conversion and processing of MS data, data libraries for identification of VOCs, statistical methods, and applications for statistical analysis. About two decades have passed since the term metabolome was first used and metabolomics has been considered one of the important scientific field to understand complicated biological processes in agronomically important crops (Alseekh and Fernie 2018;Dixon et al. 2006). Recent advances in metabolomics study, such as improved accuracy of analytical instruments with automated systems, easy to handle free applications for data processing and statistical analysis based on graphic user interface, open-source and web-based libraries has provided better and increased accessibility to researchers.

    Analysis of VOCs using HS-SPME-GC-MS with MVA has been tried to identify genotypic differences, temporal and spatial variations from Antirrhinum, Aquilegia, wild Chrysanthemums, Cymbidium, gentians, Luculia, salvia species, Tillandsia, tulips (Baek et al. 2019;Kim et al. 2014;Lee et al. 2010;Li et al. 2016;Lo et al. 2021;Oyama-Okubo and Tsuji 2013;Tulukcu et al. 2019;Wang et al. 2021;Weiss et al. 2016). In addition, recent advances in other omics studies enabled multi-omoics approach to understand relationship between VOCs and deferentially expressed genes in evening primrose (Oenothera harringronii) (Bechen et al. 2021;Verdonk et al. 2003). Furthermore, analysis of single-cell transcriptome by RNA sequencing is now also available (Xu et al. 2010;Zhang et al. 2021). For example, glandular trichomes (GTs) play an important key role to secrete large amounts of VOCs and are located on the surfaces of many organs, such as leaves, stems, flower petals, etc. GTs can be selectively collected from plant organs (Tissier 2012;Tissier et al. 2017). Therefore, if multiomics approach in combination with single-cell RNA sequencing technique and analysis of VOCs is introduced, precise relationship between expressed genes and VOCs synthesized in GTs and furthermore, deeper understanding between GTs and other cells by comparative analysis through differentially expressed genes and VOCs in one individual plant or among plants too (Zhou et al. 2021).

    Therefore, metabolomics approach of HS-SPME-GC-MS will facilitate the study of VOCs synthesized in plants and also multiomics approach will accelerate our understanding of the relationship between VOCs and other omes, such as genomes, transcriptomes, proteomes, etc. Finally, it will not only help specify genes involved in biosynthesis of VOCs but also help developing new cultivars with nicer floral scents by contributing development of floricultural industry.

    Acknowledgement

    This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (Ministry of Education, Science and Technology). [NRF-2010- 355-F00009]

    Figure

    FRJ-29-4-213_F1.gif

    Potential approach of various type of omics and multiomics in VOCs research for floricultural plants.

    FRJ-29-4-213_F2.gif

    Example of total ion chromatogram and identification of VOCs using OpenChrom®, an open-source free software and All records with Kovats RI (GCMS DB-Public-KovatsRI-VS3.msp), a free database library provided by RIKEN (http://prime.psc.riken.jp/compms/ index.html). A green colored box on the left is an example to show the name of VOCs and is magnified as shown the box framed by green on the right-side.

    Table

    Example of MS data file formats from different vendors.

    Some of conversion and processing software for analysis of mass spectral data acquired using HS-SPME-GC-MS.

    MS data libraries and useful website widely used to identify VOCs in plants using HS-SPME-GC-MS.

    Useful statistical software for multivariate analysis.

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    1. SEARCH

    2. Journal Abbreviation : 'Flower Res. J.'
      Frequency : Quarterly
      Doi Prefix : 10.11623/frj.
      ISSN : 1225-5009 (Print) / 2287-772X (Online)
      Year of Launching : 1991
      Publisher : The Korean Society for Floricultural Science
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