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 do this, transfer seeds to a 15 mL plastic centrifuge tube (approximately 300 seeds will fill the tube) and cover with aluminum foil. Pierce the foil two-three times using a pin and freeze dry the whole grains overnight (approximately 24 h). Samples can either be returned to the -80 &#176;C freezer at this stage or the next step can be carried out.</li>
	<li>Grind the seeds using a laboratory blender for two runs on high mode for 20 s. 
	NOTE: The blender used for this protocol requires a minimum of approximately 150 seeds to fill the blender to blade height and give a relatively homogenously ground grain sample.</li>
	<li>Remove the blender from the base and tap the side of the blender to bring any coarsely ground grain to the surface of the sample. Coarse material can be discarded or stored.</li>
	<li>Transfer powder-like finely ground material from the blender to a 2 mL plastic microcentrifuge tube. 
	NOTE: Wash the blender with deionized water and rinse with LC-MS-grade MeOH between samples. Ensure the blender is completely dry before proceeding to the next sample.</li>
	<li>Return finely ground grain samples to the freezer or proceed to the next step (metabolite extraction).</li>
</ol>
2. Preparation of extraction solvent
NOTE: Prepare extraction solvent on the same day as performing the extractions.
<ol>
	<li>Prepare at least 2 mL of 1 mg/mL of each standard. Use acetonitrile (ACN) to prepare 2-aminoanthracene, miconazole and d6-transcinnamic acid. Use water to prepare 13C6-sorbitol.</li>
	<li>Take 2 mL of each 1 mg/mL standard and add to a 100 mL volumetric flask.</li>
	<li>Fill the volumetric flask to the line with acetonitrile. Ensure that 100 mL of acetonitrile contains 20 &#181;g/mL of each of the internal standards: 2-aminoanthracene, miconazole, 13C6-sorbitol, d6-transcinnamic acid.</li>
</ol>
3. Metabolite extraction
<ol>
	<li>Weigh 200 mg of finely ground grain into a 2 mL microcentrifuge tube.</li>
	<li>Add 500 &#181;L of extraction solvent to 200 mg of finely ground grain sample.</li>
	<li>Mix using a homogenizer for 2 runs of 20 s at 6,500 rpm.</li>
	<li>Centrifuge at 4 &#176;C for 5 min at 16,100 x g.</li>
	<li>Transfer the supernatant to a 2 mL plastic tube.</li>
	<li>Repeat this procedure from steps 3.1 to 3.5 twice more to give a total supernatant volume of approximately 1.5 mL.</li>
	<li>Vortex to mix the supernatant.</li>
	<li>Transfer an equal volume (55 &#181;L) of each extract to a separate 2 mL tube to make a pooled grain extract sample.</li>
	<li>Transfer a 50 &#181;L aliquot of the extract to a glass vial. 
	NOTE: Extracts can be frozen (-80 &#176;C) at this point or proceed to the next step and follow through to the LC-MS analysis.</li>
</ol>
4. Preparation of solutions for LC-MS analysis
CAUTION: For concentrated acid, always add acid to water/solvent.
<ol>
	<li>Prepare 50 mL of 1 M ammonium formate stock solution. Weigh 3.153 g of ammonium formate and transfer to a 50 mL volumetric flask. Fill volumetric flask to the line with LC-MS grade H2O.</li>
	<li>Prepare 1 L of the mobile phase A consisting of 10 mM ammonium formate, 0.1% formic acid. To do so, add approximately 500 mL of LC-MS grade water to a 1 L volumetric flask. Add 10 mL of 1 M ammonium formate stock and 1 mL of formic acid. Fill volumetric flask to the line with LC-MS grade H2O. Transfer to a 1 L bottle and sonicate for 15 min to degas.</li>
	<li>Prepare 1 L of mobile phase B consisting of 10 mM ammonium formate in 79:20:1 acetonitrile:isopropyl alcohol:water, 0.1% formic acid ratio. Add 200 mL of isopropanol to a 1 L volumetric flask. Add 10 mL of 1 M ammonium formate stock and 1 mL of formic acid. Fill volumetric flask to the line with acetonitrile. Transfer to a 1 L bottle and sonicate for 15 min to degas. 
	NOTE: Dilute the 1 M ammonium formate into the isopropyl alcohol (IPA) before adding the ACN. Ammonium formate is insoluble in CAN.</li>
	<li>Prepare 1 L of mobile phase C consisting of 10 mM ammonium formate in the ratio of 89:10:1 isopropyl alcohol:acetonitrile:water. To do so, add approximately 500 mL of isopropanol, 10 mL of 1 M ammonium formate stock and 100 mL of acetonitrile to a 1 L volumetric flask. Fill to the line with isopropanol. Transfer to a 1 L bottle and sonicate for 15 min to degas.</li>
	<li>Preparation of LC-MS system wash solvents
	<ol>
		<li>Replace the pump-head wash solution with fresh solution. Use 50% methanol, 10% isopropyl alcohol or other as recommended by the manufacturer.</li>
		<li>Prepare strong and weak needle wash solutions for washing the injection fluidics prior to and following sample injection. For the strong wash, add equal volumes of ACN and IPA. For the weak wash, prepare a solution of 10% ACN (requiring approximately 500 mL and 1 L of each of the strong and weak washes respectively for this protocol and number of samples) in separate bottles.</li>
		<li>Set the needle wash volumes to 600 &#181;L and 1800 &#181;L for the strong and weak washes, respectively.</li>
	</ol>
	</li>
</ol>
5. Preparation of samples for LC-MS analysis
<ol>
	<li>As per the manufacturers standard operating procedure for the preparation of 400 ng/&#181;L leucine enkephalin, pipette 7.5 mL of water into the 12 mL leucine-enkephalin vial containing 3 mg of leucine-enkephalin. Freeze at -80 &#176;C in 50 &#181;L aliquots.</li>
	<li>Prepare 100 mL of 5% ACN containing 200 ng/mL leucine-enkephalin (50 &#181;L of 400 ng/&#181;L leucine enkephalin). Prepare on the same day as LC-MS analysis.</li>
	<li>Add 950 &#181;L of 5% acetonitrile containing the injection standard leucine-enkephalin to the 50 &#181;L sample aliquot prepared from step 3.</li>
	<li>Vortex to mix the prepared sample.</li>
</ol>
6. LC-MS setup
NOTE: A detailed description of instrument and acquisition method setup is described in the manufacturer&#39;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.
<ol>
	<li>Open an LC-MS hardware profile.</li>
	<li>Set up the chromatographic method as outlined in Table 1. Ensure that the LC system is equipped with a quaternary solvent manager to set up this gradient. 
	NOTE: IPA is a viscous solvent. It should be introduced at a low flow rate and a sufficient equilibration time should be used before increasing the composition to 98.0%. These steps will prevent the LC system from overpressuring and stopping.</li>
	<li>Set up the mass spectrometer acquisition methods for each of the positive and negative ToF-MS modes over the m/z range 50-1,300. 
	NOTE: The instrument used for the work presented here requires positive and negative methods to be calibrated and run individually (i.e., polarity switching within a method is not possible).</li>
	<li>If the LC column is new, condition the column according to the manufacturer&#39;s recommendation. 
	NOTE: The following steps should be completed directly before data acquisition.</li>
	<li>In an &#39;MS only&#39; hardware profile, calibrate the mass spectrometer according to the manufacturer&#39;s recommendations. Complete this step prior to each mode of acquisition, ensuring that the system has stabilized in each given modality before calibration.</li>
	<li>Purge and flush the LC fluidics system using LC-MS grade solvents, including mobile phase and wash solvents.</li>
	<li>Equilibrate the LC system using the LC method starting conditions, ensuring that column pressure has stabilized.</li>
	<li>Inject sodium formate (0.5 mM in 90% IPA) at the beginning of the sample sequence (described below) to check the instrument calibration.</li>
	<li>Set up the instrument sequence table so that solvent and preparative (extraction) blanks are analyzed first; followed by pooled QC samples (6-10) for system conditioning; then the randomized sample list with QC samples run at regular intervals (e.g., every fifth injection) as technical replicates. Run two QC samples at the end of the sequence. 
	NOTE: It is helpful to include the date and injection/acquisition order in the sample filename as well as the sample ID. For example: YYYY MMDD_Injection number_Variety_Biological replicate. Before pressing start, ensure the LC column pressure is stable and that the LC is connected to the MS.</li>
</ol>
7. Data processing
NOTE: A general data processing workflow is presented in Figure 1.
<ol>
	<li>Check the data quality (internal standard mass accuracy (calculation below) and signal reproducibility) while the sequence is running. To check signal reproducibility, visual inspection of overlaid spectra should suffice. 
	NOTE: Mass error (ppm) = ((Theoretical mass &#8211; measured mass) / theoretical mass) x 106</li>
	<li>Generate an aligned peak intensity matrix containing samples x internal standards (aligned by retention time and m/z values).
	<ol>
		<li>Open the data processing software (see Table of Materials). Under Home &#62; Open, click Data. Navigate to the appropriate file location and open all data files.</li>
		<li>Under Home &#62; Sequence &#62; Processing Type, select Quantitation from the drop-down menu.</li>
		<li>Under Home &#62; Method &#62; Quan, click Calibration Components. Fill in each field using the details provided in Table 2. Click OK.</li>
		<li>On the left panel in the columns next to the data files, fill in the sample type by right clicking on the cell and selecting Unknown. Fill in the level as n/a.</li>
		<li>Under Home &#62; Processing, select Sequence. Choose a location to save the sequence and then click Process.</li>
		<li>Under Home &#62; Results, select Quan. From the Quan viewer, select Export runs to matrix analyzer.</li>
		<li>From matrix analyzer results viewer, click Export to csv. Save the file as a spreadsheet file.</li>
		<li>In spreadsheet software, calculate the average, standard deviation and relative standard deviation of the intensity (peak area) of each internal standard.</li>
	</ol>
	</li>
	<li>Generate an aligned peak intensity matrix containing samples x untargeted features (aligned by retention time and m/z values).
	<ol>
		<li>Open the small molecule discovery analysis software (see Table of Materials) and select File &#62; New to create a new experiment. Name the experiment and choose the location to save and store experiment files. Click Next.</li>
		<li>Select the type of instrument used (high resolution mass spectrometer), data format (profile), and the polarity (positive or negative). Click Next. Select all adducts available in the library and edit adduct library as required. Click Create Experiment. A new page will load where the rest of the data processing will continue/occur.</li>
		<li>Import data files. Select the file format and then select import. Browse to data location and select the data files to be imported. The progress will be shown for each file on the left panel of the page.</li>
		<li>Once data files are imported, select Start Automatic Processing. Choose a method for selecting an alignment reference. Either let the software assess all runs for suitability, give a list of suitable samples (i.e., QC samples) or choose the reference most suitable i.e. a mid-sequence QC sample. Select Yes, automatically align my runs &#62; Next.</li>
		<li>Select Next on the experiment design page (this can be set up later).</li>
		<li>Select Perform Peak Picking and then Set Parameters. Under the Peak Picking Limits tab, select Absolute Ion Intensity and enter 100. Select apply a minimum peak width and enter 0.01 min. Select OK &#62; Finish. When processing is complete, select Close. 
		NOTE: Peak picking limits can be optimized for other data file types as necessary.</li>
		<li>On the bottom right of the screen, select Section Complete. Review aligned runs and make sure each sample is aligned to the reference. The alignment scores were &#62;90% for the data presented here. Select Section Complete.</li>
		<li>On the next page, select between subject design. Name the design. Select Group the runs manually and Create design. Add condition, click on Condition 1 and name the group appropriately. Click Section Complete. 
		NOTE: Continue to add conditions as appropriate to use statistics within the software. Since we only used the software to generate an untargeted matrix, we used a single condition labelled &#39;all&#39;.</li>
		<li>On the next page, select Section Complete. Do not re-do peak picking.</li>
		<li>Review the deconvolution and then click Section Complete.</li>
		<li>Go to File &#62; Export Compound Measurements. Deselect any properties not wanted in the output. Click OK. Choose a location to save the .csv file. Click Save &#62; Open File &#62; Open Folder or Close.</li>
	</ol>
	</li>
	<li>Filter the data using the extraction blanks to remove artefacts (spreadsheet software).
	<ol>
		<li>For each RT x m/z feature, in a new column, calculate the average response in extraction blanks.</li>
		<li>For each RT x m/z feature, calculate the average response in all other samples (including QC samples).</li>
		<li>Calculate the % peak intensity of blanks in samples (average response in blank/average response in samples x 100).</li>
		<li>Sort the percent contribution column from lowest to highest values.</li>
		<li>Remove features which have &#62;5% intensity contribution from blanks.</li>
	</ol>
	</li>
	<li>Filter missing values and correct the feature signals to signals in pooled QC samples.
	<ol>
		<li>Open the data processing software (Table of Materials). Click the View MatrixAnalyzer button. Click the Open Data File button.</li>
		<li>Navigate to the .csv file location containing the peak intensity of RT x m/z features for each sample (untargeted matrix). Select Open.</li>
		<li>Under the QC samples tab, fill out each parameter as required. For the data set described here, use QC category=QC, ignore categories=empty, impute type=none, coverage threshold=80, scale using=no scaling, uncheck log transform, correct using=smoothing spline, smoothing=0.25.</li>
		<li>On the top right of the matrix panel, click the play button QC correction.</li>
		<li>After the correction has been performed on the top right of the matrix panel, click Save Results. Navigate to an appropriate location and save the .csv results file.</li>
	</ol>
	</li>
	<li>Filter the data to remove features which have &#62;20% RSD in QC samples.
	<ol>
		<li>Arrange the data so that samples are in rows and features are in columns.</li>
		<li>In a new column, calculate the average peak intensity for QC samples.</li>
		<li>In a new column, calculate the standard deviation of the peak intensity for QC samples.</li>
		<li>Calculate the relative standard deviation of the peak intensity for QC samples: (QC standard deviation/QC average) x 100.</li>
		<li>Sort the features from lowest to highest %RSD and remove features which have a QC RSD &#62;20%.</li>
	</ol>
	</li>
	<li>Filter the data to remove features with low RSDsample/RSDQC ratios (e.g., &#60;1).
	<ol>
		<li>In a new column, calculate the average peak intensity for samples.</li>
		<li>In a new column, calculate the standard deviation of the peak intensity for samples.</li>
		<li>Calculate the relative standard deviation of the peak intensity of the samples: (sample standard deviation/sample average) x 100.</li>
		<li>In a new column, divide the samples RSD by the QC RSD.</li>
		<li>Sort the values (RSDsample/RSDQC ratios) from highest to lowest and remove features with a ratio &#60;1.</li>
	</ol>
	</li>
	<li>Impute missing values (several methods available online).
	<ol>
		<li>Format the spreadsheet so that samples are in rows and features are in columns. The first column should be the samples filename. Create an additional column next to the filenames and enter the sample groupings. In this case, the samples were grouped by variety.</li>
		<li>Save the spreadsheet as a .csv file.</li>
		<li>Go to the homepage for the web-based analytical pipeline for high-throughput metabolomics (see Table of Materials) and click Click Here to Start.</li>
		<li>Click Statistical Analysis. Under Upload Your Data, select Data Type: peak intensity table, Format: Samples in rows (unpaired) and then Choose File.</li>
		<li>Navigate to .csv file, select Open and then select Submit.</li>
		<li>On the next page, select Missing Value Estimation. Uncheck step 1 (this was performed in AnalyzerPro XD). In step 2, choose a method to estimate missing values. 
		NOTE: For the data presented here - &#39;estimate missing values&#39; with &#39;KNN&#39; was selected.</li>
		<li>At this stage, the data matrix can be downloaded (select Download from the left panel of the web page) or proceed to perform further statistical analyses.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

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 ...

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

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Biochemistry

9.4K Views.  Murdoch University. 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.

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

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

A Tandem Liquid Chromatography&amp;#8211;Mass Spectrometry-based Approach for Metabolite Analysis of &lt;em&gt;Staphylococcus aureus&lt;/em&gt;

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most cases, the action of noncoding RNAs is mediated by proteins whose functions are in turn regulated by these interactions with noncoding RNAs. Consistent with this, a growing number of proteins involved in nuclear functions have been reported to bind RNA and in a few cases the RNA-binding regions of these proteins have been mapped, often through laborious, candidate-based methods.
Here, we report a detailed protocol to perform a high-throughput, proteome-wide unbiased identification of RNA-binding proteins and their RNA-binding regions. The methodology relies on the incorporation of a photoreactive uridine analog in the cellular RNA, followed by UV-mediated protein-RNA crosslinking, and mass spectrometry analyses to reveal RNA-crosslinked peptides within the proteome. Although we describe the procedure for mouse embryonic stem cells, the protocol should be easily adapted to a variety of cultured cells.

We describe a protocol to identify RNA-binding proteins and map their RNA-binding regions in live cells using UV-mediated photocrosslinking and mass spectrometry.

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most ...

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Here, we present a robust protocol to quantify 40 compounds involved in central carbon and energy metabolism in cell-free protein synthesis reactions. The cell-free synthesis mixture is derivatized with aniline for effective separation using reversed-phase liquid chromatography and then quantified by mass spectrometry using isotopically labelled internal standards.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is ...

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan structure are fairly conserved across all bacteria, there is also considerable variation between Gram-positives/negatives and between species. In addition, there are numerous known variations, modifications, or adaptations to the peptidoglycan that can occur within a bacterial species in response to growth phase and/or environmental stimuli. These variations produce a highly dynamic structure that is known to participate in many cellular functions, including growth/division, antibiotic resistance, and host defense avoidance. To understand the variation within peptidoglycan, the overall structure must be broken down into its constitutive parts (known as muropeptides) and assessed for overall cellular composition. Peptidoglycomics uses advanced mass spectrometry combined with high-powered bioinformatic data analysis to examine peptidoglycan composition in fine detail. The following protocol describes the purification of peptidoglycan from bacterial cultures, the acquisition of muropeptide intensity data through a liquid chromatograph&#8212;mass spectrometer, and the differential analysis of peptidoglycan composition using bioinformatics.

This protocol covers a detailed analysis of peptidoglycan composition using liquid chromatography mass spectrometry coupled with advanced feature extraction and bioinformatic analysis software.

Peptidoglycan is an important component of bacterial cell walls and a common cellular target for antimicrobials. Although aspects of peptidoglycan ...

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within a cell, tissue, organ, biological fluid, or organism. Metabolomics traditionally focuses on water-soluble metabolites. The water-soluble metabolome is the final product of a complex cellular network that integrates various genomic, epigenomic, transcriptomic, proteomic, and environmental factors. Hence, the metabolomic analysis directly assesses the outcome of the action for all these factors in a plethora of biological processes within various organisms. One of these organisms is the budding yeast Saccharomyces cerevisiae, a unicellular eukaryote with the fully sequenced genome. Because S. cerevisiae is amenable to comprehensive molecular analyses, it is used as a model for dissecting mechanisms underlying many biological processes within the eukaryotic cell. A versatile analytical method for the robust, sensitive, and accurate quantitative assessment of the water-soluble metabolome would provide the essential methodology for dissecting these mechanisms. Here we present a protocol for the optimized conditions of metabolic activity quenching in and water-soluble metabolite extraction from S. cerevisiae cells. The protocol also describes the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of the extracted water-soluble metabolites. The LC-MS/MS method of non-targeted metabolomics described here is versatile and robust. It enables the identification and quantification of more than 370 water-soluble metabolites with diverse structural, physical, and chemical properties, including different structural isomers and stereoisomeric forms of these metabolites. These metabolites include various energy carrier molecules, nucleotides, amino acids, monosaccharides, intermediates of glycolysis, and tricarboxylic cycle intermediates. The LC-MS/MS method of non-targeted metabolomics is sensitive and allows the identification and quantitation of some water-soluble metabolites at concentrations as low as 0.05 pmol/&#181;L. The method has been successfully used for assessing water-soluble metabolomes of wild-type and mutant yeast cells cultured under different conditions.

Quantitative Metabolomics of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol for the identification and quantitation of major classes of water-soluble metabolites in the yeast Saccharomyces cerevisiae. The described method is versatile, robust, and sensitive. It allows the separation of structural isomers and stereoisomeric forms of water-soluble metabolites from each other.

Metabolomics is a methodology used for the identification and quantification of many low-molecular-weight intermediates and products of metabolism within ...

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, including RNA processing, signal transduction, DNA damage response, miRNA biogenesis, and translation.
In recent years, thanks to biochemical and analytical developments, mass spectrometry (MS)-based proteomics has emerged as the most effective strategy to characterize the cellular methyl-proteome with single-site resolution. However, identifying and profiling in vivo protein R-methylation by MS remains challenging and error-prone, mainly due to the substoichiometric nature of this modification and the presence of various amino acid substitutions and chemical methyl-esterification of acidic residues that are isobaric to methylation. Thus, enrichment methods to enhance the identification of R-methyl-peptides and orthogonal validation strategies to reduce False Discovery Rates (FDR) in methyl-proteomics studies are required.
Here, a protocol specifically designed for high-confidence R-methyl-peptides identification and quantitation from cellular samples is described, which couples metabolic labeling of cells with heavy isotope-encoded Methionine (hmSILAC) and dual protease in-solution digestion of whole cell extract, followed by off-line High-pH Reversed Phase (HpH-RP) chromatography fractionation and affinity enrichment of R-methyl-peptides using anti-pan-R-methyl antibodies. Upon high-resolution MS analysis, raw data are first processed with the MaxQuant software package and the results are then analyzed by hmSEEKER, a software designed for the in-depth search of MS peak pairs corresponding to light and heavy methyl-peptide within the MaxQuant output files.

Protein Arginine (R)-methylation is a wide-spread post-translational modification regulating multiple biological pathways. Mass spectrometry is the best technology to globally profile the R-methyl-proteome, when coupled to biochemical approaches for modified peptide enrichment. The workflow designed for the high confidence identification of global R-methylation in human cells is described here.

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, ...

Profiling Volatile Compounds in Blackcurrant Fruit using Headspace Solid-Phase Microextraction Coupled to Gas Chromatography-Mass Spectrometry

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars with enhanced organoleptic characteristics and thus, to increase consumer acceptance. High-throughput metabolomic platforms have been recently developed to quantify a wide range of metabolites in different plant tissues, including key compounds responsible for fruit taste and aroma quality (volatilomics). A method using headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography-mass spectrometry (GC-MS) is described here for the identification and quantification of VOCs emitted by ripe blackcurrant fruits, a berry highly appreciated for its flavor and health benefits.
Ripe fruits of blackcurrant plants (Ribes nigrum) were harvested and directly frozen in liquid nitrogen. After tissue homogenization to produce a fine powder, samples were thawed and immediately mixed with sodium chloride solution. Following centrifugation, the supernatant was transferred into a headspace glass vial containing sodium chloride. VOCs were then extracted using a solid-phase microextraction (SPME) fiber and a gas chromatograph coupled to an ion trap mass spectrometer. Volatile quantification was performed on the resulting ion chromatograms by integrating peak area, using a specific m/z ion for each VOC. Correct VOC annotation was confirmed by comparing retention times and mass spectra of pure commercial standards run under the same conditions as the samples. More than 60 VOCs were identified in ripe blackcurrant fruits grown in contrasting European locations. Among the identified VOCs, key aroma compounds, such as terpenoids and C6 volatiles, can be used as biomarkers for blackcurrant fruit quality. In addition, advantages and disadvantages of the method are discussed, including prospective improvements. Furthermore, the use of controls for batch correction and minimization of drift intensity have been emphasized.

A headspace solid-phase microextraction-gas-chromatography platform is described here for fast, reliable, and semi-automated volatile identification and quantification in ripe blackcurrant fruits. This technique can be used to increase knowledge about fruit aroma and to select cultivars with enhanced flavor for the purpose of breeding.

There is an increasing interest in measuring volatile organic compounds (VOCs) emitted by ripe fruits for the purpose of breeding varieties or cultivars ...

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an ...