Name: untargeted liquid chromatography-mass spectrometry-based metabolomics analysis of wheat grain
Uploaded: 2020-03-13T12:00:00
Description: Watch this Scientific Journal Video about Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain at JoVE.com

The authors would like to acknowledge the West Australian Premier&#39;s Agriculture and Food Fellowship program (Department of Jobs, Tourism, Science and Innovation, Government of Western Australia) and the Premier&#39;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&#39;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.

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
<ol>
	<li>Retrieve samples (whole grains) from -80 &#176;C storage. 
	NOTE: Freeze-drying of seeds is recommended shortly after harvest if samples are being collected from multiple seasons. This minimizes any changes in metabolite concentration that may occur after varying periods of storage. To ...

<ol>
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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unraveling+Key+Metabolomic+Alterations+in+Wheat+Embryos+Derived+from+Freshly+Harvested+and+Water-Imbibed+Seeds+of+Two+Wheat+Cultivars+with+Contrasting+Dormancy+Status.">Unraveling Key Metabolomic Alterations in Wheat Embryos Derived from Freshly Harvested and Water-Imbibed Seeds of Two Wheat Cultivars with Contrasting Dormancy Status.</a> Frontiers in Plant Science. 8 (1203), (2017).</li><li>Francki, M. G., Hayton, S., Gummer, J. P. A., Rawlinson, C., Trengove, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomic+profiling+and+genomic+analysis+of+wheat+aneuploid+lines+to+identify+genes+controlling+biochemical+pathways+in+mature+grain.">Metabolomic profiling and genomic analysis of wheat aneuploid lines to identify genes controlling biochemical pathways in mature grain.</a> Plant Biotechnology Journal. 14 (2), 649-660 (2016).</li><li>Riewe, D., Wiebach, J., Altmann, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+Annotation+and+Quantification+of+Wheat+Seed+Oxidized+Lipids+by+High-Resolution+LC-MS/MS.">Structure Annotation and Quantification of Wheat Seed Oxidized Lipids by High-Resolution LC-MS/MS.</a> Plant Physiology. 175 (2), 600-618 (2017).</li><li>Blazenovic, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+Annotation+of+All+Mass+Spectra+in+Untargeted+Metabolomics.">Structure Annotation of All Mass Spectra in Untargeted Metabolomics.</a> Analytical Chemistry. 91 (3), 2155-2162 (2019).</li><li>Castro-Perez, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+LC-MSE+Lipidomic+Analysis+using+a+Shotgun+Approach+and+Its+Application+to+Biomarker+Detection+and+Identification+in+Osteoarthritis+Patients.">Comprehensive LC-MSE Lipidomic Analysis using a Shotgun Approach and Its Application to Biomarker Detection and Identification in Osteoarthritis Patients.</a> Journal of Proteome Research. 9 (5), 2377-2389 (2010).</li><li>Sangster, T., Major, H., Plumb, R., Wilson, A. J., Wilson, I. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+pragmatic+and+readily+implemented+quality+control+strategy+for+HPLC-MS+and+GC-MS-based+metabonomic+analysis.">A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis.</a> Analyst. 131 (10), 1075-1078 (2006).</li><li>Dunn, W. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Procedures+for+large-scale+metabolic+profiling+of+serum+and+plasma+using+gas+chromatography+and+liquid+chromatography+coupled+to+mass+spectrometry.">Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.</a> Nature Protocols. 6 (7), 1060-1083 (2011).</li><li>Broadhurst, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+and+considerations+for+the+use+of+system+suitability+and+quality+control+samples+in+mass+spectrometry+assays+applied+in+untargeted+clinical+metabolomic+studies.">Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.</a> Metabolomics. 14 (6), 72 (2018).</li><li>Chong, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetaboAnalyst+4.0:+towards+more+transparent+and+integrative+metabolomics+analysis.">MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.</a> Nucleic Acids Research. 46 (1), 486-494 (2018).</li><li>Du Fall, L. A., Solomon, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+necrotrophic+effector+SnToxA+induces+the+synthesis+of+a+novel+phytoalexin+in+wheat.">The necrotrophic effector SnToxA induces the synthesis of a novel phytoalexin in wheat.</a> New Phytologist. 200 (1), 185-200 (2013).</li><li>Bowne, J. 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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 metab...

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&#39;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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>13C6-sorbitol</td>
			<td>Merck Sigma-Aldrich</td>
			<td>605514</td>
			<td></td>
		</tr>
		<tr>
			<td>2-aminoanthracene</td>
			<td>Merck Sigma-Aldrich</td>
			<td>A38800-1 g</td>
			<td></td>
		</tr>
		<tr>
			<td>Acetonitrile</td>
			<td>ThermoFisher Scientific</td>
			<td>FSBA955-4</td>
			<td>Optima LC-MS grade</td>
		</tr>
		<tr>
			<td>Ammonium formate</td>
			<td>Merck Sigma-Aldrich</td>
			<td>516961-100 mL</td>
			<td>&#62;99.995%</td>
		</tr>
		<tr>
			<td>Analyst TF</td>
			<td>Sciex</td>
			<td></td>
			<td>Version 1.7</td>
		</tr>
		<tr>
			<td>AnalyzerPro software</td>
			<td>SpectralWorks Ltd.</td>
			<td></td>
			<td>Data processing software used for step 7.2. Version 5.7</td>
		</tr>
		<tr>
			<td>AnalyzerPro XD sortware</td>
			<td>SpectralWorks Ltd.</td>
			<td></td>
			<td>Data processing software used for step 7.5. Version 1.4</td>
		</tr>
		<tr>
			<td>Balance</td>
			<td>Sartorius. Precision Balances Pty. Ltd.</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>d6-transcinnamic acid</td>
			<td>Isotec</td>
			<td>513962-250 mg</td>
			<td></td>
		</tr>
		<tr>
			<td>Formic acid</td>
			<td>Ajax Finechem Pty. Ltd.</td>
			<td>A2471-500 mL</td>
			<td>99%</td>
		</tr>
		<tr>
			<td>Freeze dryer (Freezone 2.5 Plus)</td>
			<td>Labconco</td>
			<td>7670031</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass Schott bottles (100 mL, 500 mL, 1 L)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Glass vials (2 mL) and screw cap lids (pre-slit)</td>
			<td>Velocity Scientific Solutions</td>
			<td>VSS-913 (vials), VSS-SC91191 (lids)</td>
			<td></td>
		</tr>
		<tr>
			<td>Installation kit for Sciex TripleToF</td>
			<td>Sciex</td>
			<td>p/n 4456736</td>
			<td></td>
		</tr>
		<tr>
			<td>Isopropanol</td>
			<td>ThermoFisher Scientific</td>
			<td>FSBA464-4</td>
			<td>Optima LC-MS grade</td>
		</tr>
		<tr>
			<td>Laboratory blender</td>
			<td>Waring commercial</td>
			<td></td>
			<td>Model HGBTWTS3</td>
		</tr>
		<tr>
			<td>Leucine-enkephalin</td>
			<td>Waters</td>
			<td>p/n 700008842</td>
			<td>Tuning solution</td>
		</tr>
		<tr>
			<td>Metaboanalyst</td>
			<td><a href="https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml" target="_blank">https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml</a></td>
			<td></td>
			<td>Web-based analytical pipeline for high-throughput metabolomics. Free, web-based tool. Version 4.0.</td>
		</tr>
		<tr>
			<td>Methanol</td>
			<td>ThermoFisher Scientific</td>
			<td>FSBA456-4</td>
			<td>Optima LC-MS grade</td>
		</tr>
		<tr>
			<td>Miconazole</td>
			<td>Merck Sigma-Aldrich</td>
			<td>M3512-1 g</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcentrifuge (Eppendorf 5415R)</td>
			<td>Eppendorf (Distributed by Crown Scientific Pty. Ltd.)</td>
			<td></td>
			<td>5426 No. 0021716</td>
		</tr>
		<tr>
			<td>Microcentrifuge tubes (2 mL)</td>
			<td>SSIbio</td>
			<td>1310-S0</td>
			<td></td>
		</tr>
		<tr>
			<td>Microsoft Office Excel</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Peak View software</td>
			<td>Sciex</td>
			<td></td>
			<td>Version 1.2 (64-bit)</td>
		</tr>
		<tr>
			<td>Pipette tips (200 uL, 100 uL)</td>
			<td>ThermoFisher Scientific</td>
			<td>MBP2069-05-HR (200 uL), MBP2179-05-HR (1000 uL)</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipettes (200 uL, 1000 uL)</td>
			<td>ThermoFisher Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Plastic centrifuge tubes (15 mL)</td>
			<td>ThermoFisher Scientific</td>
			<td>NUN339650</td>
			<td></td>
		</tr>
		<tr>
			<td>Progenesis QI</td>
			<td>Nonlinear Dynamics</td>
			<td></td>
			<td>Samll molecule discovery analysis software. Version 2.3 (64-bit)</td>
		</tr>
		<tr>
			<td>Sciex 5600 triple ToF mass spectrometer</td>
			<td>Sciex</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Screw-cap lysis tubes (2 mL) with ceramic beads</td>
			<td>Bertin Technologies</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium formate</td>
			<td>Merck Sigma-Aldrich</td>
			<td>456020-25 g</td>
			<td></td>
		</tr>
		<tr>
			<td>Tissue lyser/homogeniser</td>
			<td>Bertin Technologies</td>
			<td></td>
			<td>Serial 0001620</td>
		</tr>
		<tr>
			<td>Volumetric flasks (10 mL, 50 mL, 100 mL, 200 mL, 1 L)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Vortex mixer</td>
			<td>IKA Works Inc. (Distributed by Crown Scientific Pty. Ltd.)</td>
			<td></td>
			<td>001722</td>
		</tr>
		<tr>
			<td>Water</td>
			<td>ThermoFisher Scientific</td>
			<td>FSBW6-4</td>
			<td>Optima LC-MS grade</td>
		</tr>
		<tr>
			<td>Water&#39;s Acquity LC system equipped with quaternary pumps</td>
			<td>Waters</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Water&#39;s Aquity UPLC 100mm HSST3 C18 column</td>
			<td>Waters</td>
			<td>p/n 186005614</td>
			<td></td>
		</tr>
	</tbody>
</table>

untargeted liquid chromatography-mass spectrometry-based metabolomics analysis of wheat grain

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&#39;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.

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 (&#60;10 ppm error) and signal reproducibility (&#60;20% RSD) of internal standards (<str...

Watch this Scientific Journal Video about Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain at JoVE.com

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

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 ...

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