Reviewers: Holub P1, Klem K4, Peñuelas J2,3
Measurement unit: molecules/ions (m/z); Measurement scale: leaf, branch, tissue, exudates, soil and air samples; Equipment costs: €€€; Running costs: €€; Installation effort: high; Maintenance effort: medium (services of instruments); Knowledge need: high; Measurement mode: manual
Metabolomics is an “omics” approach that aims to analyse all low molecular-weight metabolites in biological samples. Metabolomic profiling represents a new tool to assess a plant’s response to biotic or abiotic stresses. In ecological studies, ecometabolomics aims to study the metabolome structure of organisms and media (water, soil, air, etc.) in field conditions (Peñuelas & Sardans, 2009; Sardans et al., 2011). Metabolome analyses provide the metabolism structure (the metabolites and their concentrations), thus informing on the functional state of the organisms, media, or overall ecosystem. The metabolome can be considered as the chemical phenotype of organisms (Fiehn, 2002). Because the chemical phenotypes are not fixed, metabolomics has been used to understand how organisms function under different environmental conditions (Yuan et al., 2009; Rivas-Ubach et al., 2012; Yoshikawa et al., 2013; Gargallo-Garriga et al., 2014). Target and non-target metabolomic profiling has been used to identify changes in plant metabolome and metabolic pathways enabling the investigation of plant-specific responses to biotic (e.g. pests) (Rivas-Ubach et al., 2016) or abiotic stress factors, such as increase of UV radiation (Oravec et al., 2015), drought (Rivas-Ubach et al., 2012; Gargallo-Garriga et al., 2014), temperature (Gargallo-Garriga et al., 2015), nutrient deficiency (Gargallo-Garriga et al., 2017), and/or air pollution (Večeřová et al., 2016). Metabolomics can thus substantially contribute to studies predicting acclimation of plants to environmental perturbances related to climate-change and other global-change drivers. Metabolomics has already shown great potential to provide in-depth understanding of biological systems, pathways, and their functionally dynamic interactions. Moreover, metabolomic profiling provides an understanding at the molecular level of how plants respond to changes in the growth environment and whether and how they acclimate and resist. Metabolomics can thus contribute to the understanding of the impact of climate and global change and also to answer basic ecological questions regarding the impacts of competition or trophic relationships on organisms.
5.11.1 What and how to measure?
Tandem analytical techniques and basic analytical techniques are used for the analysis of the metabolome. These are a combination of separation techniques: high performance liquid chromatography (HPLC) and gas chromatograph (GC) with mass spectrometer (MS) detection techniques. HPLC-MS systems are used in particular for target and non-target analyses of polar and semi-polar primary and secondary metabolites (polyphenols, amino acids, saccharides, phytohormones, etc.), while GC-MS is especially suitable for target analyses of non-polar metabolites or polar metabolites that can be easily derived (fatty acids, volatile compounds, etc.). A combination of HPLC and GC techniques may thus lead to a comprehensive description of plant metabolome and its changes. Proton-based nuclear magnetic resonance (1H NMR) coupled or not with 13C NMR is also a frequently used metabolomic platform that does not rely on previous separation methods for untargeted metabolomics profiling analysis (Rivas-Ubach et al., 2013). However, the low sensitivity of this method currently precludes its more extended use (Sardans et al., 2011). Basic analytical techniques such as Fourier transformed infrared spectroscopy, Raman spectroscopy, spectrofluorometry, and/or classical spectrophotometers can be used as complementary analytical techniques for investigation of other groups of metabolites including photosynthetic and photoprotective pigments (chlorophylls, carotenoids, xanthophylls). Determination of nitrogen and carbon contents in biological samples using an elemental analyser with a thermal conductivity detector yields important supplementary information about the stoichiometry of tissues, organs, and/or whole plants. This coupling of metabolomics with elemental analyses allows one to relate changes in plant function with changes in plant use of different elements, constituting a notable advantage for integrative ecological studies. The C/N/P stoichiometry plays an important role in the metabolism, as does the stoichiometry of other nutrients such as calcium, magnesium, and potassium. P, K, Ca, Mg, and many other elements are analysed with inductively coupled plasma – mass spectrometry (ICP-MS) techniques.
The measurement results are subsequently processed by statistical software (modified MatLab, R, or Sieve). For identification of metabolites, available commercial databases and mass libraries built by each lab are used. KEGG (Kyoto Encyclopaedia of Genes and Genomes) is thereafter generally used for description of affected metabolic pathways, i.e. which ones are up- and which ones are down-regulated in the different organs of the plants (Gargallo-Garriga et al., 2014). Information from non-target analyses is further used for target analysis. The overall results from metabolomic profiling are combined with results from physiological measurements. The main aim is to find relationships between the changes of metabolome and stress-induced changes in physiology.
Sampling, sample storage, and measuring protocols
The samples (ideally approximately 300 mg dry weight) have to be frozen in liquid nitrogen immediately after sampling and kept at −80 °C (max. 2 months), and stored dry-frozen at ≤ −20 °C until further processing. The effects of long-term storage and storage temperature have been investigated so that studies can be planned without possible degradation of chemical compounds. It is crucial to protect samples against contamination and water during all steps of the process, and therefore it is necessary to adhere to “good laboratory practices”. Since the metabolomics profiles may have substantial daily courses, it is recommended to respect such daily dynamics and to collect all samples within narrow time intervals depending on the experimental goal. Local microclimatic and radiation conditions also have to be considered. Accordingly, leaves/branches with the same cardinal orientation should be sampled within dense ecosystems to reduce the variability among samples. Moreover, plant age, geographical origin of plant species (Meijon et al., 2016), and/or elevation where the plants grow (Rajsnerová et al., 2015; Rivas-Ubach et al., 2017) have been shown to potentially influence the metabolome of plant tissues. Sampling campaigns thus have to respect these factors to reduce variability of plant metabolome, to minimise artefacts, and to increase reproducibility of metabolomic data.
The following protocols are based on the protocols for metabolite profiling in plants developed by Fiehn et al. (2000) and include further modifications with respect to analytical instruments used. Before chromatographic analyses, the frozen samples are homogenised using a pestle and mortar with the addition of liquid nitrogen. Leaf tissues, for example, can be ground under liquid nitrogen with a pestle and mortar, or using a ball mill with pre-chilled holders (summarised in Fiehn, 2002). Other plant organs such as roots and grains, however, may be sometimes too hard to use in ball mills. After homogenisation, different methods of metabolite extraction could be used but, again, no systematic study comparing extraction techniques is available. Some groups of metabolites, including fatty acids among others (Folch et al., 1957; Večeřová et al., 2016), require specific extraction procedures that have to be even further modified depending on the type of sample (soil, plant, thylakoid membrane, etc.). Most frequently, homogenised samples are extracted using a methanol:H2O solution (1:2) that has been demonstrated to be optimal to extract most polar and semi-polar metabolites. For a-polar metabolites a solution of methanol:chloroform:H2O (1:2:2) can be used. In this case, an aliquot of the upper (polar) phase is used to analyse saccharides, phenolic compounds, amino acids, and Krebs cycle acids employing an UltiMate 3000 HPLC coupled with an LTQ Orbitrap XL high resolution mass spectrometer (HRMS) (ThermoFisher Scientific).
For the polar and semi-polar extraction a Hypersil GOLD column (150 × 2.1 mm, 3 μm; ThermoFisher Scientific) is used for separation of metabolites. The flow rate of the mobile phase is 0.3 mL min−1 and column temperature is set to 30 °C. The mobile phase consists of (A) acetonitrile and (B) water containing 0.1% acetic acid. Both mobile phases (A) and (B) are filtrated and degassed for 10 min in an ultrasonic bath prior to use. Gradient elution chromatography is started with 10% acetonitrile (A) and 90% water (0.1% acetic acid) (B) and is held for 5 min. Within a time interval of 5–20 min, the mobile phase (A) composition is increased to 90 %. This composition is then maintained for 5 min, after which the system is equilibrated to initial conditions (10% acetonitrile (A) and 90% water (0.1% acetic acid)) over a period of 5 min. The 254, 272, 274, and 331 nm wavelengths are monitored.
The HRMS is equipped with a HESI II heated electrospray ionization source (ThermoFisher Scientific), operated in full scan mode with a mass resolution of 60,000, when the minimum peak separation is represented by a full width of the peak at half maximum (FWHM). Full scan spectra are acquired over the mass range 50–1000 m/z in positive polarity mode and 65–1000 m/z in negative polarity mode. The mass resolution and sensitivity of the HRMS are regularly controlled by injecting a mixture of phenolic compounds. As a control, phthalate is taken as an internal control mass. The compounds are assigned on the basis of public or private mass libraries created using standards measured in MS and MSn modes.
Gas chromatography coupled with mass spectrometry (GC-MS) is used to analyse a spectrum of fatty acids. Homogenised samples are transferred into vials and 1.5 mL of a chloroform:methanol solution (2:1) is added. The vials are placed in a thermoblock at 60 °C for 30 min. The lower phase (chloroform) is collected and the process is repeated. In case of insufficient phase separation, 1 mL of 0.88% potassium chloride should be added. After collection of lower phases and the addition of an internal standard (nonadecanoic acid), extracts are dried using nitrogen flow. The methyl esters derivatives of fatty acids are prepared using 1 mL of 3 N methanolic HCl, which is added to the dried extract and then heated for 90 min at 60 °C. Subsequently, the samples are extracted three times with 2.5 mL of n-hexane and dried using nitrogen flow. Finally, extracts are dissolved in 1 mL of n-hexane.
The analysis of fatty acids methyl esters is performed with a TSQ Quantum XLS triple Quadrupole (ThermoFischer Scientific) on a 30 m, 0.25 mm (inside diameter), 0.25 μm column (ZB-5MS; Phenomenex). Samples (1 μL) are injected in splitless mode. The inlet pressure of the carrier gas (helium) is 100 kPa at the initial oven temperature and its flow rate is 1.2 mL min−1. Meanwhile the injection temperature is 250 °C. The temperature gradient of the oven begins at 100 °C and is increased to 150 °C at the rate of 10 °C min−1, followed by a temperature increase to 260 °C at the rate of 2.5 °C min−1. The interface temperature is maintained at 250 °C. GC-MS (electrospray ionization 50 eV, ion source temperature 200 °C) is performed at full scan in the 50–450 m/z range (scan time 0.15 s). The fatty acids’ methyl esters are searched in the public and private mass libraries created from measurement of standards using GC-MS in full scan mode.
Where to start
Fiehn (2002), Fiehn et al. (2000), Folch et al. (1957), Gargallo-Garriga et al. (2015), Meijon et al. (2016), Peñuelas & Sardans (2009), Rivas-Ubach et al. (2017), Večeřová et al. (2016), Weckwerth & Kahl (2013)
5.11.2 Special cases, emerging issues, and challenges
Metabolomic profiling can be used, among others, to identify biomarkers of early-stress detection (Kaplan et al., 2004; Boudonck et al., 2009). Metabolomics could also represent, in conjunction with physiological and morphological traits, an important additive method of plant phenotyping, the breeding and selection of genotypes with the highest resistance to biotic and abiotic stresses. Metabolomics techniques could be also used to explore root exudates and thus to investigate plant-soil interactions (Sardans & Peñuelas, 2013). An overview of current developments and future challenges in ecological metabolomics can be found in Sardans et al. (2011).
A number of metabolomics applications could be extended by the application of other ionisation techniques such as DART (Direct Analysis in Real Time). This ambient ionisation technique does not require sample preparation, so solid and liquid materials can be analysed by mass spectrometry in their native state (Zhou et al., 2010; Lesiak et al., 2014; Armitage et al., 2015).
Desorption electrospray ionisation (DESI) is another ambient ionisation technique compliable with high-resolution mass spectrometry. DESI can be used for imaging metabolite distribution (localisation) in plant tissues or surfaces of biological systems. Visualisation of metabolite heterogeneity can be potentially used for the detection of pathogen infections on plant leaves (Takats et al., 2005; Chernetsova & Morlock, 2011).
Other ionisation techniques such as APCI (atmospheric pressure chemical ionisation) are suitable for investigating thermally stable samples with low to medium (less than 1500 Da) molecular weight, and medium to high polarity including non-polar lipids (Byrdwell, 2001), pesticides (Jansson et al., 2004), and/or various natural organic compounds (amino acids, saponins, phenyl propanoids, etc.). In contrast, electrospray ionisation (ESI) is especially useful in producing ions from macromolecules (Whitehouse et al., 1989).
Where to start
Jansson et al. (2004); Kaplan et al. (2004); Sardans et al. (2011); Takats et al. (2005); Whitehouse et al. (1989)
Theory, significance, and large datasets
Fiehn (2002); Gargallo-Garriga et al. (2014); Peñuelas & Sardans (2009); Sardans & Peñuelas (2013); Weckwerth & Kahl (2013)
More on methods and existing protocols
Fiehn et al. (2000); Jansson et al. (2004); Rivas-Ubach et al. (2012); Večeřová et al. (2016); Zhou et al. (2010)
Armitage, R. A., Jakes, K., & Day, C. (2015). Direct analysis in real time-mass spectroscopy for identification of red dye colourants in Paracas Necropolis Textiles. STAR: Science & Technology of Archaeological Research, 1(2), 60-69.
Boudonck, K. J., Mitchell, M. W., Német, L., Keresztes, L., Nyska, A., Shinar, D., & Rosenstock, M. (2009). Discovery of metabolomics biomarkers for early detection of nephrotoxicity. Toxicologic Pathology, 37(3), 280-292.
Byrdwell, W. C. (2001). Atmospheric pressure chemical ionization mass spectrometry for analysis of lipids. Lipids, 36(4), 327-346.
Chernetsova, E. S., & Morlock, G. E. (2011). Ambient desorption ionization mass spectrometry (DART, DESI) and its bioanalytical applications. Bioanalytical Reviews, 3(1), 1-9.
Fiehn, O. (2002). Metabolomics––the link between genotypes and phenotypes. Plant Molecular Biology, 48, 155-171.
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000). Metabolite profiling for plant functional genomics. Nature Biotechnology, 18(11), 1157-1161.
Folch, J., Lees, M., & Sloane-Stanley, G. H. (1957). A simple method for the isolation and purification of total lipids from animal tissues. Journal of Biological Chemistry, 226(1), 497-509.
Gargallo-Garriga, A., Sardans, J., Pérez-Trujillo, M., Rivas-Ubach, A., Oravec, M., Vecerova, K., … Parella, T. (2014). Opposite metabolic responses of shoots and roots to drought. Scientific Reports, 4, a6829.
Gargallo‐Garriga, A., Sardans, J., Pérez‐Trujillo, M., Oravec, M., Urban, O., Jentsch, A., … Peñuelas, J. (2015). Warming differentially influences the effects of drought on stoichiometry and metabolomics in shoots and roots. New Phytologist, 207(3), 591-603.
Gargallo-Garriga, A., Wright, S. J., Sardans, J., Pérez-Trujillo, M., Oravec, M., Večeřová, K., … Peñuelas, J. (2017). Long-term fertilization determines different metabolomic profiles and responses in saplings of three rainforest tree species with different adult canopy position. PLoS One, 12(5), e0177030.
Jansson, C., Pihlström, T., Österdahl, B. G., & Markides, K. E. (2004). A new multi-residue method for analysis of pesticide residues in fruit and vegetables using liquid chromatography with tandem mass spectrometric detection. Journal of Chromatography A, 1023(1), 93-104.
Kaplan, F., Kopka, J., Haskell, D. W., Zhao, W., Schiller, K. C., Gatzke, N., … Guy, C. L. (2004). Exploring the temperature-stress metabolome of Arabidopsis. Plant Physiology, 136(4), 4159-4168.
Lesiak, A. D., Cody, R. B., Dane, A. J., & Musah, R. A. (2014). Rapid detection by direct analysis in real time-mass spectrometry (DART-MS) of psychoactive plant drugs of abuse: The case of Mitragyna speciosa aka “Kratom”. Forensic Science International, 242, 210-218.
Meijón, M., Feito, I., Oravec, M., Delatorre, C., Weckwerth, W., Majada, J., & Valledor, L. (2016). Exploring natural variation of Pinus pinaster Aiton using metabolomics: is it possible to identify the region of origin of a pine from its metabolites? Molecular Ecology, 25(4), 959-976.
Oravec, M., Novotná, K., Rajsnerová, P., et al. (2015). Target and non-target metabolomics profiling of different barley varieties affected by enhanced ultraviolet radiation and various C:N stoichiometry. FASEB Journal 29(1 Supplement), Abstract 887.7.
Peñuelas, J., & Sardans, J. (2009). Ecological metabolomics. Chemistry and Ecology, 25(4), 305-309.
Rajsnerová, P., Klem, K., Holub, P., Novotná, K., Večeřová, K., Kozáčiková, M., … Urban, O. (2015). Morphological, biochemical and physiological traits of upper and lower canopy leaves of European beech tend to converge with increasing altitude. Tree Physiology, 35(1), 47-60.
Rivas-Ubach, A., Sardans, J., Pérez-Trujillo, M., Estiarte, M., & Peñuelas, J. (2012). Strong relationship between elemental stoichiometry and metabolome in plants. Proceedings of the National Academy of Sciences, 109(11), 4181-4186.
Rivas‐Ubach, A., Pérez‐Trujillo, M., Sardans, J., Gargallo‐Garriga, A., Parella, T., & Peñuelas, J. (2013). Ecometabolomics: optimized NMR‐based method. Methods in Ecology and Evolution, 4(5), 464-473.
Rivas‐Ubach, A., Sardans, J., Hódar, J. A., Garcia‐Porta, J., Guenther, A., Oravec, M., … Peñuelas, J. (2016). Similar local, but different systemic, metabolomic responses of closely related pine subspecies to folivory by caterpillars of the processionary moth. Plant Biology, 18(3), 484-494.
Rivas‐Ubach, A., Sardans, J., Hódar, J. A., Garcia‐Porta, J., Guenther, A., Paša‐Tolić, L., … Peñuelas, J. (2017). Close and distant: Contrasting the metabolism of two closely related subspecies of Scots pine under the effects of folivory and summer drought. Ecology and Evolution, 7(21), 8976-8988.
Sardans, J., & Peñuelas, J. (2013). Plant-soil interactions in Mediterranean forest and shrublands: impacts of climatic change. Plant and Soil, 365(1-2), 1-33.
Sardans, J., Peñuelas, J., & Rivas-Ubach, A. (2011). Ecological metabolomics: overview of current developments and future challenges. Chemoecology, 21(4), 191-225.
Takats, Z., Wiseman, J. M., & Cooks, R. G. (2005). Ambient mass spectrometry using desorption electrospray ionization (DESI): instrumentation, mechanisms and applications in forensics, chemistry, and biology. Journal of Mass Spectrometry, 40(10), 1261-1275.
Večeřová, K., Večeřa, Z., Dočekal, B., Oravec, M., Pompeiano, A., Tříska, J., & Urban, O. (2016). Changes of primary and secondary metabolites in barley plants exposed to CdO nanoparticles. Environmental Pollution, 218, 207-218.
Weckwerth, W., & Kahl, G. (Eds.). (2013). The Handbook of Plant Metabolomics. Chichester: John Wiley & Sons.
Whitehouse, C. M., Dreyer, R. N., Yamashita, M., & Fenn, J. B. (1989). Electrospray ionization for mass-spectrometry of large biomolecules. Science, 246(4926), 64-71.
Yoshikawa, K., Hirasawa, T., Ogawa, K., Hidaka, Y., Nakajima, T., Furusawa, C., & Shimizu, H. (2013). Integrated transcriptomic and metabolomic analysis of the central metabolism of Synechocystis sp. PCC 6803 under different trophic conditions. Biotechnology Journal, 8(5), 571-580.
Yuan, J., Doucette, C. D., Fowler, W. U., Feng, X. J., Piazza, M., Rabitz, H. A., … Rabinowitz, J. D. (2009). Metabolomics‐driven quantitative analysis of ammonia assimilation in E. coli. Molecular Systems Biology, 5(1), 302.
Zhou, M., McDonald, J. F., & Fernández, F. M. (2010). Optimization of a direct analysis in real time/time-of-flight mass spectrometry method for rapid serum metabolomic fingerprinting. Journal of the American Society for Mass Spectrometry, 21(1), 68-75.
Authors: Oravec M1, Večeřová K1, Gargallo-Garriga A1,2,3, Sardans J2,3, Urban O1
Reviewers: Holub P1, Klem K4, Peñuelas J2,3
1 Global Change Research Institute, The Czech Academy of Sciences, Brno, Czech Republic
2 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
3 CREAF, Cerdanyola del Vallès, Spain
4 Mendel University in Brno, Faculty of AgriSciences, Brno, Czech Republic