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>
	<li>Hall, R., 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=Plant+metabolomics:+the+missing+link+between+genotype+and+phenotype.">Plant metabolomics: the missing link between genotype and phenotype.</a> Plant Cell. 14, (2002).</li><li>Beleggia, R., 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=Effect+of+genotype,+environment+and+genotype-by-environment+interaction+on+metabolite+profiling+in+durum+wheat+(Triticum+durum+Desf.)+grain.">Effect of genotype, environment and genotype-by-environment interaction on metabolite profiling in durum wheat (Triticum durum Desf.) grain.</a> Journal of Cereal Science. 57 (2), 183-192 (2013).</li><li>Das, A., Kim, D. -. W., Khadka, P., Rakwal, R., Rohila, J. 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. 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=Drought+Responses+of+Leaf+Tissues+from+Wheat+Cultivars+of+Differing+Drought+Tolerance+at+the+Metabolite+Level.">Drought Responses of Leaf Tissues from Wheat Cultivars of Differing Drought Tolerance at the Metabolite Level.</a> Molecular Plant. 5 (2), 418-429 (2012).</li><li>Wang, 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=Sharing+and+community+curation+of+mass+spectrometry+data+with+Global+Natural+Products+Social+Molecular+Networking.">Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.</a> Nature Biotechnology. 34 (8), 828-837 (2016).</li><li>Shahaf, N., 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=The+WEIZMASS+spectral+library+for+high-confidence+metabolite+identification.">The WEIZMASS spectral library for high-confidence metabolite identification.</a> Nature Communications. 7 (1), 12423 (2016).</li></ol>

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 ...

The metabolite is influenced by factors such as the genome environment and management practices. Understanding the interactions among these factors could allow more accurate prediction and management of a product's yield and quality. By incorporating a third mobile phase, rather than the traditional two, this technique allows the separation and detection of a wider range of metabolites.Metabolomics could be applied to any area of biology. Firstly, to achieve a deeper understanding of an organism's biochemistry, and help explain their response to abiotic or biotic stress in relation to the physiology. Secondly, to associate biomarkers with the perturbation under study.As isopropyl alcohol is a viscous solvent, it should be introduced at low flare rate, and sufficient equilibration time used before increasing the composition to 98%These steps will prevent the chromatic graphic system from over-pressuring, returning an error, or potentially damaging the analytical column. To begin preparation of the grains, use a laboratory blender to grind the grain. Run the blender on high speed for 20 seconds and then repeat.Remove the blender jar from the base. Tap the side of the blender jar to bring any coarsely-ground grain to the surface of the sample. The coarsely-ground grain can be discarded, or stored.Transfer the finely-ground grain from the blender to a two-milliliter plastic micro centrifuge tube. On the same day as the extraction, prepare the extraction solvent, as described in the manuscript. Next, weigh 200 milligrams of finely-ground grain into a two-milliliter micro centrifuge tube.Add 500 microliters of extraction solvent to the grain in the tube. In addition, prepare a blank by adding 500 microliters of extraction solvent to an empty tube. Using a homogenizer, set at 6, 500 RPM, mix the solvent and grain for 20 seconds.Then, repeat for another 20 seconds. Then, centrifuge the tube at four degrees Celsius and 16, 100 times G, for five minutes. Transfer the supernatant to a two-milliliter plastic tube and retain the pellet.Repeat the extraction process on the pellet twice. Combine the three supernatants, yielding a total extract volume of approximately 1.5 milliliters and mix by vortexing. To make a pooled sample of all the grain extracts to be used for quality control, transfer 55 microliters of each grain extract to a two-milliliter tube and vortex.Then, transfer 50 microliter aliquots of this pooled sample to glass vials. Transfer a 50-microliter aliquot of each grain sample extract to glass vials. As described in the written manuscript, prepare the solutions that will be needed for liquid chromatography mass spectrometry.On the day of the LCMS analysis, prepare 100 milliliters of 5%acetonitrile, containing 200 nanograms per milliliter leucine enkephalin. Add 950 microliters of this solution to the glass vial containing the 50 microliter aliquot of the grain extract. Mix the contents in the vial by vortexing.First, set up an calibrate the instruments as described in the manuscript and the manufacturer's user guide. Next, purge and flush the LC fluidics system using LCMS grade solvents, including mobile phase and wash solvents. Equilibrate the LC system using the LC method starting conditions, ensuring that column pressure has stabilized.Set up the instrument sequence table. Inject sodium formate at the beginning of the sample sequence to check the instrument calibration. Analyze solvent and preparative blanks first, followed by pooled quality control samples for system conditioning, and then the randomized sample list.Run quality control samples at regular intervals, as technical replicates. Run two quality control samples at the end of the sequence. While the sequence is running, check the data quality, including both the internal standard mass accuracy and the signal reproducibility.To check signal reproducibility, visual inspection of overlaid spectra should suffice. Four internal standards were included in the solution used to prepare the grain extracts. During LCMS, good mass accuracy and single reproducibility of internal standards were observed for both positive and negative ionization modes.Based on analysis of blanks, 421 signals in the negative mode and 835 signals in the positive mode were judged to be artifacts. These signals were present in blanks at intensities equal to or greater than 5%of the average single intensity in grain samples. After removal of the artifacts and further filtering of the data, the negative mode returned 483 features and the positive mode returned 523 features.Approximately 250 features were biologically relevant and differed significantly in intensity across wheat varieties. The quality control measures, such as the inclusion of preparative blanks, internal standards, and pooled samples, are important to remember in this protocol.

untargeted-liquid-chromatography-mass-spectrometry-based-metabolomics

Research

JoVE Journal

Biochemistry

9.4K visualizações.  Murdoch University. O metabólito é influenciado por fatores como o ambiente do genoma e as práticas de manejo. Compreender as interações entre esses fatores poderia permitir uma previsão e gerenciamento mais precisos do rendimento e qualidade de um produto. Ao incorporar uma terceira fase móvel, em vez das duas tradicionais, esta técnica permite a separação e detecção de uma gama mais ampla de metabólitos.Metabolômica pode ser aplicada a qualquer área da biologia. Em primeiro lugar, para ob...