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>
	<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

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Biochemistry

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. ...

Routine Collection of High-Resolution cryo-EM Datasets Using 200 KV Transmission Electron Microscope

Cryo-electron microscopy (cryo-EM) has been established as a routine method for protein structure determination during the past decade, taking an ever-increasing share of published structural data. Recent advances in TEM technology and automation have boosted both the speed of data collection and quality of acquired images while simultaneously decreasing the required level of expertise for obtaining cryo-EM maps at sub-3 &#197; resolutions. While most of such high-resolution structures have been obtained using state-of-the-art 300 kV cryo-TEM systems, high-resolution structures can be also obtained with 200 kV cryo-TEM systems, especially when equipped with an energy filter. Additionally, automation of microscope alignments and data collection with real-time image quality assessment reduces system complexity and assures optimal microscope settings, resulting in increased yield of high-quality images and overall throughput of data collection. This protocol demonstrates the implementation of recent technological advances and automation features on a 200 kV cryo-transmission electron microscope and shows how to collect data for the reconstruction of 3D maps that are sufficient for de novo atomic model building. We focus on best practices, critical variables, and common issues that must be considered to enable the routine collection of such high-resolution cryo-EM datasets. Particularly the following essential topics are reviewed in detail: i) automation of microscope alignments, ii) selection of suitable areas for data acquisition, iii) optimal optical parameters for high-quality, high-throughput data collection, iv) energy filter tuning for zero-loss imaging, and v) data management and quality assessment. Application of the best practices and improvement of achievable resolution using an energy filter will be demonstrated on the example of apo-ferritin that was reconstructed to 1.6 &#197;, and Thermoplasma acidophilum 20S proteasome reconstructed to 2.1-&#197; resolution using a 200 kV TEM equipped with an energy filter and a direct electron detector.

High-resolution cryo-EM maps of macromolecules can be also achieved by using 200 kV TEM microscopes. This protocol shows the best practices for setting accurate optics alignments, data acquisition schemes, and selection of imaging areas that are all essential for the successful collection of high-resolution datasets using a 200 kV TEM.