A method for the untargeted analysis of wheat grain metabolites and lipids is presented. The protocol includes an acetonitrile metabolite extraction method and reversed phase liquid chromatography-mass spectrometry methodology, with acquisition in positive and negative electrospray ionization modes.
Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism's biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.
Understanding the interactions between genes, environment and management practices in agriculture could allow more accurate prediction and management of product yield and quality. Plant metabolites are influenced by factors such as the genome, environment (climate, rainfall etc.), and in an agriculture setting, the way crops are managed (i.e., application of fertilizer, fungicide etc.). Unlike the genome, the metabolome is influenced by all of these factors and hence metabolomics data provides a biochemical fingerprint of these interactions at a particular time. There are usually one of two goals for a metabolomics-based study: firstly, to achieve a deeper understanding of the organism's biochemistry and help explain the mechanism of response to perturbation (abiotic or biotic stress) in relation to the physiology; and secondly, to associate biomarkers with the perturbation under study. In both cases, the outcome of having this knowledge is a more precise management strategy to achieve the goal of improved yield size and quality.
The plant metabolome is predicted to contain thousands1 of small molecules with varied physicochemical properties. Currently, no metabolomics platforms (predominantly mass spectrometry and nuclear magnetic resonance spectroscopy) can capture the entire metabolome in a single analysis. Developing such techniques (sample preparation, metabolite extraction and analysis), which provide as great a coverage of the metabolome as possible within a single analytical run, is a key aim for metabolomics researchers. Previous untargeted metabolomics analyses of wheat grain have combined data from multiple chromatographic separations and acquisition polarities and/or instrumentation for greater metabolome coverage. However, this has required samples to be prepared and acquired separately for each modality. For example, Beleggia et al.2 prepared a derivatized sample for the GC-MS analysis of polar analytes in addition to the GC-MS analysis of the nonpolar analytes. Das et al.3 used both GC- and LC-MS methods to improve coverage in their analyses; however, this approach would generally require separate sample preparations as described above as well as two independent analytical platforms. Previous analyses of wheat grain using GC-MS2,3,4 and LC-MS3,5 platforms have yielded 50 to 412 (55 identified) features for GC-MS, 409 for combined GC-MS and LC-MS and several thousand for an LC-MS lipidomics analysis5. By combining at least two modes into a single analysis, extended metabolome coverage can be maintained, increasing the richness of biological interpretation while also offering savings in both time and cost.
To permit the efficient separation of a wide range of lipid species by reversed-phase chromatography, modern lipidomics methodologies commonly use a high proportion of isopropanol in the elution solvent6, providing amenability to lipid classes that might otherwise be unresolved by the chromatography. For an efficient lipid separation, the starting mobile phase is also much higher in organic composition7 than the typical reversed phase chromatographic methods, which consider other classes of molecules. The high organic composition at the start of the gradient makes these methods less suitable to many other classes of molecules. Most notably, reversed phase liquid chromatography employs a binary solvent gradient, starting with a mostly aqueous composition and increasing in organic content as the elution strength of the chromatography is increased. To this end, we sought to combine the two approaches to achieve separation of both lipid and non-lipid classes of metabolites within a single analysis.
Here, we present a chromatographic method that uses a third mobile phase and enables a combined traditional reversed phase and lipidomics-appropriate chromatography method using a single sample preparation and one analytical column. We have adopted many of the quality control measures and data filtering steps that have previously been implemented in predominantly clinical metabolomics studies. These approaches are useful in determining robust features with high technical reproducibility and biological relevance and excludes those which do not meet these criteria. For example, we describe repeat analysis of the pooled QC sample8, QC correction9, data filtering9,10 and imputation of missing features11.
This method is appropriate for 30 samples (approximately 150 seeds per sample). Three biological replicates of ten different field-grown wheat varieties were used here.
1. Preparation of grains
2. Preparation of extraction solvent
NOTE: Prepare extraction solvent on the same day as performing the extractions.
3. Metabolite extraction
4. Preparation of solutions for LC-MS analysis
CAUTION: For concentrated acid, always add acid to water/solvent.
5. Preparation of samples for LC-MS analysis
6. LC-MS setup
NOTE: A detailed description of instrument and acquisition method setup is described in the manufacturer's user guide. A general guide and the details specific to this protocol are outlined below. The following steps can be completed at any time prior to acquiring the data.
7. Data processing
NOTE: A general data processing workflow is presented in Figure 1.
The plant metabolome is influenced by a combination of its genome and environment, and additionally in an agricultural setting, the crop management regime. We demonstrate that genetic differences between wheat varieties can be observed at the metabolite level, here, with over 500 measured compounds showing significantly different concentrations between varieties in the grain alone. Good mass accuracy (<10 ppm error) and signal reproducibility (<20% RSD) of internal standards (Figure 2) were observed for both negative and positive ionization modes (Table 3). The described sample preparation and liquid chromatography-mass spectrometry-based analysis yielded >900 deconvoluted features in negative ionization mode and >1300 deconvoluted features in positive ionization mode. Preparative blanks (Figure 3) were included to determine whether the sample preparation and analysis methods introduced artefact features, and thus all non-biological influences eliminated from the data matrix. It was found that 421 signals in the negative mode and 835 signals in the positive mode had signal intensities equal to or greater than 5% of the average signal intensity in grain samples. These features were removed and after further data filtering steps (step 7 and Figure 1), the negative mode returned 483 features and the positive mode returned 523 features, forming the metabolic snapshot. The method was successful in detecting features, which had significantly different intensities between wheat varieties (Figure 4) with >500 significant features across both ionization modes. In negative ionization mode, the majority of significant features were in the reversed phase gradient and in positive ionization mode, the majority of significant features were in the lipid gradient (Figure 4).
Figure 1: The workflow used in this analysis for data checking, processing and filtering. Step 1 is conducted using the data acquisition/viewing software on the instrument so that 'on-the-fly' assessments can be conducted. This includes calculating the mass error (ppm) of internal standards and overlaying internal standard peaks for visual assessment of data reproducibility. Steps 2-7 describe the data processing procedure outlined in the protocol, step 7. Please click here to view a larger version of this figure.
Figure 2: Extracted ion chromatograms. Extracted ion chromatograms of 13C6-sorbitol (dark blue), leucine-enkephalin (pink), d6-trans-cinnamic acid (orange), 2-aminoanthracene (green) and miconazole (light blue) internal standards in positive (top) and negative (bottom) electrospray ionization (ESI) modes. The internal standard retention times and intensities are shown. ESI + and ESI – Please click here to view a larger version of this figure.
Figure 3: Total ion chromatogram (TIC) overlay of preparative blanks showing negative mode (pink) and positive mode (blue) acquisitions. One internal standard, miconazole, is shown. Please click here to view a larger version of this figure.
Figure 4: Total ion chromatogram (TIC) overlay, showing negative mode (pink) and positive mode (blue) acquisitions and number of features significantly different between wheat variety across the chromatographic gradient. In negative mode, the greatest number of significant features was found when mobile phase B composition was high. In positive mode, the greatest number of significant features was found when mobile phase C composition was high. One internal standard, miconazole, is shown. Please click here to view a larger version of this figure.
Segment | Time | Flow rate | %A | %B | %C | Curve |
(min) | (mL/min) | |||||
1 | Initial | 0.6 | 98 | 2 | 0 | 6 |
2 | 1 | 0.6 | 98 | 2 | 0 | 6 |
3 | 7 | 0.8 | 2 | 98 | 0 | 6 |
4 | 7.1 | 0.8 | 0 | 100 | 0 | 6 |
5 | 10 | 0.8 | 0 | 100 | 0 | 6 |
6 | 18 | 0.4 | 0 | 10 | 90 | 6 |
7 | 21 | 0.4 | 0 | 2 | 98 | 6 |
8 | 21.1 | 0.4 | 98 | 2 | 0 | 6 |
9 | 24 | 0.4 | 98 | 2 | 0 | 6 |
10 | 24.1 | 0.6 | 98 | 2 | 0 | 6 |
11 | 25 | 0.6 | 98 | 2 | 0 | 6 |
Table 1: Liquid chromatography timed program of mobile phase compositions.
Parameter | Internal standard | ||||
13C6-sorbitol | Leucine-enkephalin | d6-transcinnamic acid | 2-amino-anthracene | Miconazole | |
Quan m/z | 211.09 (187.09) | 556.28 (554.26) | 155.097 (153.08) | 194.1 | 414.99 |
Mass tolerance (amu) | 0.01 (0.05) | 0.01 (0.05) | 0.01 (0.05) | 0.01 | 0.01 |
Retention time | 1.2 | 4.6 | 5.1 | 6.5 | 7 |
Retention time window | 0.1 (0.5) | 0.1 (0.5) | 0.1 (0.5) | 0.1 | 0.1 |
Detection type | Highest | Highest | Highest | Highest | Highest |
Response type | Area | Area | Area | Area | Area |
Area threshold | 10 | 10 (50) | 10 (50) | 10 | 10 |
Width threshold | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 |
Height threshold | 0 | 0 | 0 | 0 | 0 |
Signal-to-noise ratio | 5 | 5 | 3 (5) | 5 | 5 |
Smoothing | 5 | 5 (3) | 5 (3) | 5 | 5 |
Table 2: Peak detection parameters for internal standards in positive (and negative) acquisition modes.
Mass accuracy (ppm) | %RSD Before QC correction | %RSD After QC correction | ||
Negative mode | 13C6-sorbitol | 4.59 | 6.12 | 7.08 |
D6-transcinnamic acid | 7.94 | 3.93 | 5.99 | |
Leucine-enkephalin | 0.91 | 1.8 | 1.96 | |
Positive mode | 13C6-sorbitol | 5.65 | 14.1 | 15.3 |
Leucine-enkephalin | 3 | 3.24 | 5 | |
D6-transcinnamic acid | 8.03 | 5.41 | 9.81 | |
2-aminoanthracene | 3.99 | 7.97 | 5.45 | |
Miconazole | 1.8 | 3.01 | 5.72 |
Table 3: Sample (n=30) internal standard mass accuracy (ppm) and signal reproducibility before and after QC-correction expressed as relative standard deviation (%).
Here, we present an LC-MS-based untargeted metabolomics method for the analysis of wheat grain. The method combines four acquisition modes (reversed phase and lipid-amenable reversed phase with positive and negative ionization) into two modes by introducing a third mobile phase into the reversed phase gradient. The combined approach yielded approximately 500 biologically relevant features per ion polarity with roughly half of these significantly different in intensity between wheat varieties. Significant changes in metabolite concentration in the grain of different wheat varieties indicates altered biochemistry, which may be linked to disease resistance, stress tolerance and other phenotypic traits that are important for grain quality and yield. For example, metabolomics approaches have been used to describe novel defense mechanisms12 and propose the role of metabolites in drought tolerance13. Future applications of this protocol may be able to further link biochemical profiles of particular varieties to genetic traits that are desirable for certain environments and management practices. In turn, this would allow production of optimal grain quality and yield for selected genotypes.
The inclusion of internal standards is critical to this protocol to allow the user to determine changes in signal, retention time shifts and as indicators of mass accuracy. Changes in signal may indicate, for example, sub optimal extraction, injection (including fluidic system blockages), or detector performance. Retention time shifts may indicate poor pump performance, inappropriate mobile phase gradient equilibration or that the LC column stationary phase has deteriorated. Poor mass accuracy can be indicative of a drifted calibration and that the system requires re-calibration. In all of the above cases, the system should be stopped, and the appropriate maintenance/replacement of parts performed. We included four standards in the extraction solution used to prepare grain and a standard in the final sample added prior to injection. Care was taken to ensure that standards were amenable to each ionization mode and covered a range of retention times; however, we acknowledge that this array of standards could be improved with the inclusion of a labeled lipid standard. It has been shown that wheat grain contains hundreds of triacylglycerols (TAGs)5, any of which would be a suitable addition to this protocol. The inclusion of preparative blanks and pooled QC samples8 are also critical steps in this protocol. Thousands of ion features are detected in untargeted mass spectrometry methods and it is important to exclude those which are present only in blank samples and also those which are not reproducibly detected (i.e., high %RSD) throughout the analysis.
Although the current method saves considerable time and resources, if a quaternary solvent manager is not available, standard reversed phase and lipid methods can be used to achieve the same results. The extraction volume used in this protocol would suffice for the analysis of additional acquisition modes. This protocol describes an acetonitrile extraction. Whilst successful, an alternative extraction solvent, or combination of solvents, will provide a different metabolite coverage, which may in turn deliver more features and/or give better (or a lesser) extraction efficiency of some compounds. We have not attempted to establish the metabolite identity of the statistically significant measurements resolved in this protocol; however, mass spectral databases for plant metabolites and lipids are available and developing5,14,15. To identify the metabolites, tandem mass spectra (MS/MS) would need to be collected in addition to full scan data. These can be collected during the initial run using pooled samples and an appropriate MS/MS method or on reserved extract (stored at -80 °C) once metabolites of interest have been determined. We observed large fold changes of compounds between varieties so we would recommend doing both and in the second instance, using a variety known to contain a high concentration of the compound of interest to obtain the highest quality MS/MS spectrum.
The authors have nothing to disclose.
The authors would like to acknowledge the West Australian Premier's Agriculture and Food Fellowship program (Department of Jobs, Tourism, Science and Innovation, Government of Western Australia) and the Premier's Fellow, Professor Simon Cook (Centre for Digital Agriculture, Curtin University and Murdoch University). Field trials and grain sample collection were supported by the government of Western Australia's Royalties for Regions program. We acknowledge Grantley Stainer and Robert French for their contributions to field trials. The NCRIS-funded Bioplatforms Australia is acknowledged for equipment funding.
13C6-sorbitol | Merck Sigma-Aldrich | 605514 | |
2-aminoanthracene | Merck Sigma-Aldrich | A38800-1 g | |
Acetonitrile | ThermoFisher Scientific | FSBA955-4 | Optima LC-MS grade |
Ammonium formate | Merck Sigma-Aldrich | 516961-100 mL | >99.995% |
Analyst TF | Sciex | Version 1.7 | |
AnalyzerPro software | SpectralWorks Ltd. | Data processing software used for step 7.2. Version 5.7 | |
AnalyzerPro XD sortware | SpectralWorks Ltd. | Data processing software used for step 7.5. Version 1.4 | |
Balance | Sartorius. Precision Balances Pty. Ltd. | ||
d6-transcinnamic acid | Isotec | 513962-250 mg | |
Formic acid | Ajax Finechem Pty. Ltd. | A2471-500 mL | 99% |
Freeze dryer (Freezone 2.5 Plus) | Labconco | 7670031 | |
Glass Schott bottles (100 mL, 500 mL, 1 L) | |||
Glass vials (2 mL) and screw cap lids (pre-slit) | Velocity Scientific Solutions | VSS-913 (vials), VSS-SC91191 (lids) | |
Installation kit for Sciex TripleToF | Sciex | p/n 4456736 | |
Isopropanol | ThermoFisher Scientific | FSBA464-4 | Optima LC-MS grade |
Laboratory blender | Waring commercial | Model HGBTWTS3 | |
Leucine-enkephalin | Waters | p/n 700008842 | Tuning solution |
Metaboanalyst | https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml | Web-based analytical pipeline for high-throughput metabolomics. Free, web-based tool. Version 4.0. | |
Methanol | ThermoFisher Scientific | FSBA456-4 | Optima LC-MS grade |
Miconazole | Merck Sigma-Aldrich | M3512-1 g | |
Microcentrifuge (Eppendorf 5415R) | Eppendorf (Distributed by Crown Scientific Pty. Ltd.) | 5426 No. 0021716 | |
Microcentrifuge tubes (2 mL) | SSIbio | 1310-S0 | |
Microsoft Office Excel | Microsoft | ||
Peak View software | Sciex | Version 1.2 (64-bit) | |
Pipette tips (200 uL, 100 uL) | ThermoFisher Scientific | MBP2069-05-HR (200 uL), MBP2179-05-HR (1000 uL) | |
Pipettes (200 uL, 1000 uL) | ThermoFisher Scientific | ||
Plastic centrifuge tubes (15 mL) | ThermoFisher Scientific | NUN339650 | |
Progenesis QI | Nonlinear Dynamics | Samll molecule discovery analysis software. Version 2.3 (64-bit) | |
Sciex 5600 triple ToF mass spectrometer | Sciex | ||
Screw-cap lysis tubes (2 mL) with ceramic beads | Bertin Technologies | ||
Sodium formate | Merck Sigma-Aldrich | 456020-25 g | |
Tissue lyser/homogeniser | Bertin Technologies | Serial 0001620 | |
Volumetric flasks (10 mL, 50 mL, 100 mL, 200 mL, 1 L) | |||
Vortex mixer | IKA Works Inc. (Distributed by Crown Scientific Pty. Ltd.) | 001722 | |
Water | ThermoFisher Scientific | FSBW6-4 | Optima LC-MS grade |
Water's Acquity LC system equipped with quaternary pumps | Waters | ||
Water's Aquity UPLC 100mm HSST3 C18 column | Waters | p/n 186005614 |