The presented tutorial was performed and developed within the Proteomics and Metabolomics Facility at Colorado State University which is partially funded by the CSU Research Administration Resources for Scholarly Projects.

1. Sample Organization
<ol>
	<li>To create a plate map for sample preparation on a spreadsheet, in the first column enter a sample list in order of loading. In a second column enter the 96 well plate locations using correct nomenclature for your autosampler software.</li>
	<li>Save one well in each plate for QC samples.</li>
	<li>If your LC autosampler can handle two plates at a time, separate the sample list into batches of 190 and save this information in a second worksheet.</li>
	<li>Within each of these batches, use the randomize function [note: "=rand()"in Excel] to randomize the injection order. Now copy and paste your injection order into the LC-MS sample queue software system. Randomization should occur among batches, within batches, and be unique across injection replicates.</li>
</ol>
2. Serum and Quality Control (QC) Sample Preparation Procedures
<ol>
	<li>Prepare a 10 ml stock of QC sample containing minimum of four compounds of known mass and retention time that span the chromatographic elution of the experiment. (note: caffeine, reserpine, sulfadimethoxine, and terfenadine at a concentration of 2 &mu;g/ml each).</li>
	<li>Thaw the serum samples on ice and gently vortex for ~5 sec.</li>
	<li>Pre-wet the tips of a 12-channel pipettor with methanol to help prevent tip-dripping during sample dispensing. Then add 370 &mu;l of cold methanol to each well of a 96 well plate.</li>
	<li>Transfer 100 &mu;l of each sample to the corresponding plate location from the plate maps. Cover the plates and incubating at -80 &deg;C for 30 min to precipitate the proteins.</li>
	<li>Spin the plates in a 4 &deg;C centrifuge at 3,200 x g for 30 min. Transfer 250 &mu;l of supernatant to a new 96-well and repeat the centrifugation.</li>
	<li>Now transfer 60 &mu;l aliquots of the supernatant to three 96-well plates suitable for the UPLC or LC autosampler.</li>
	<li>Add 100 &mu;l of QC sample mix to the reserved well position on the plate.</li>
	<li>Seal each plate with adhesive film. Solvent may evaporate quickly after the well is pierced, and therefore injections should only be performed once from each plate. Plates in the sample queue can be stored in the short-term at -20 &deg;C.</li>
</ol>
3. UPLC-MS Data Acquisition
<ol>
	<li>Set the autosampler to 4 &deg;C and place first two plates into autosampler.</li>
	<li>Set up the acquisition method using one of the two alternative reverse phase gradients. Gradient A is biased towards non-polar molecules while gradient B will provide improved coverage of moderately polar molecules.
	<ol>
		<li>Gradient A: 
		<table border="1">
			<tbody>
				<tr>
					<td>Time (min)</td>
					<td>%A</td>
					<td>%B</td>
					<td>curve</td>
				</tr>
				<tr>
					<td>0.0</td>
					<td>100</td>
					<td>0</td>
					<td>&nbsp;</td>
				</tr>
				<tr>
					<td>0.1</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
				<tr>
					<td>1.0</td>
					<td>60</td>
					<td>40</td>
					<td>6</td>
				</tr>
				<tr>
					<td>3.0</td>
					<td>30</td>
					<td>70</td>
					<td>6</td>
				</tr>
				<tr>
					<td>11.0</td>
					<td>0</td>
					<td>100</td>
					<td>6</td>
				</tr>
				<tr>
					<td>17.0</td>
					<td>0</td>
					<td>100</td>
					<td>6</td>
				</tr>
				<tr>
					<td>17.1</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
				<tr>
					<td>23.0</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
			</tbody>
		</table>
		</li>
		<li>Gradient B 
		<table border="1">
			<tbody>
				<tr>
					<td>Time (min)</td>
					<td>%A</td>
					<td>%B</td>
					<td>curve</td>
				</tr>
				<tr>
					<td>0.0</td>
					<td>100</td>
					<td>0</td>
					<td>&nbsp;</td>
				</tr>
				<tr>
					<td>1</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
				<tr>
					<td>13</td>
					<td>5</td>
					<td>95</td>
					<td>6</td>
				</tr>
				<tr>
					<td>16</td>
					<td>5</td>
					<td>95</td>
					<td>6</td>
				</tr>
				<tr>
					<td>16.05</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
				<tr>
					<td>20</td>
					<td>100</td>
					<td>0</td>
					<td>6</td>
				</tr>
			</tbody>
		</table>
		</li>
	</ol>
	</li>
	<li>Set mass spectrometer to collect data in positive mode scanning from 50-1,200 m/z. Additional specific instrument conditions will vary according to the system used. The following represents the conditions use on our Waters Xevo G2 Q-TOF mass spectrometer: Collision Energy = 6V; Scan Time =0.2 sec per scan; Capillary Voltage =2,200 V; Source Temperature =150 &deg;C Desolvation Temperature =350 &deg;C Desolvation Gas Flow (N2) = 800 L/hr).</li>
	<li>Inject the QC samples five times at the beginning of each 2-plate batch of samples. After conditioning the UPLC-MS system with the first two injections, monitor for peak area (&lt;25% RSD), retention time (+/- 0.05 min), and mass accuracy (+/- 3 ppm) of the third through fifth injections. These parameters may need to be adjusted to fit the specifications of the instrument being used for data acquisition.</li>
	<li>If QC samples pass, acquire data on serum samples for those two plates. If QC samples fail, sample data is not collected until the issue is resolved and the QC samples pass. In our laboratory we have found that these parameters are consistently stable over three days of injections (i.e. the injection of 2 96 well plates). If your system is found to be unstable over this time period, more frequent QC injections may be appropriate. The QC sample is used not to correct poor quality data, but to prevent poor quality data from ever being collected.</li>
</ol>
4. Peak Detection, Integration, Alignment, and Normalization
There are a variety of options to perform these steps including freeware and vendor specific tools. This step has been skipped in the video tutorial but our approach is described below as an example.
For each sample, the analytical data generated from this type of experiment is a profile of ions as described by a retention time, m/z value, and a spectral intensity. A non-targeted metabolite profiling experiment will be comprised of many samples and thus peak detection, retention time alignment, and normalization must be performed to enable subsequent statistical analysis of the dataset.
<ol>
	<li>Export raw data into .cdf format using the Waters DataBridge software. Perform peak detection and retention time alignment using the XCMS software package 10 (freeware available at <a href="http://metlin.scripps.edu/xcms/" target="_blank">http://metlin.scripps.edu/xcms/</a>) and is executed in the R statistical environment11). The website contains documentation on how to download and run the software.</li>
	<li>Use the "matchedfilter" method within XCMS with the following parameters: full width at half max = 8 sec, S/N ration = 3, maximum of 100 peaks for each extracted ion chromatogram. Perform feature grouping, retention time correction, regrouping, and fillPeaks steps. The output of the XCMS software is a data matrix with all detectable features listed as rows, each experimental sample as a column, and cells are populated with the peak area of each feature in each sample. Transposition of this dataset may be necessary for incorporation into downstream statistical workflows.</li>
	<li>Normalize the data matrix in R by dividing the peak area of each feature in a given sample by the sum of the extracted ion peak area (referred to as total ion current, or TIC for that sample (multiplied by a 100,000 to make the numbers more amenable for visual data interpretation).</li>
	<li>Remove outliers. Outliers in the data can result from either a bad injection of a good sample or a good injection of a bad sample.
	<ol>
		<li>Perform outlier detection in R using the outliers package with the following parameters: (1) TIC values and (2) PC scores with 99.9% confidence.</li>
		<li>If outliers are present, the corresponding data files are removed and peak detection, alignment, and normalization procedures are performed again.</li>
	</ol>
	</li>
	<li>Average the normalized peak areas by sample identifier (to average multiple injections). This must be done prior to performing statistical analysis (note injection replicates account for analytical variation, but cannot be used as true biological replicates).</li>
</ol>
5. Statistical Analysis
For metabolomics experiments, both multivariate and univariate statistical techniques are necessary for data interpretation. Two techniques that are commonly used include Principal Component Analysis and Analysis of Variance (ANOVA). There are multiple ways to approach statistical analysis of the data and both open source and commercial software tools are available. This step has been skipped in the video tutorial but our approach is described below as an example.
<ol>
	<li>Perform ANOVA by applying an aov function to the dataset in R.</li>
	<li>Account for false positives using a Benjamini-Hochberg adjustment (p.adjust function in R).</li>
	<li>Conduct PCA with the Pareto-scaled (unit variance can also be applied) and mean-centered dataset. Both PC scores and loadings are calculated using the pcaMethods package in R.</li>
	<li>Use ANOVA p-values, loading scores, and fold-change parameters to determine molecular features that best described variation associated with the experimental treatment.</li>
	<li>Manually interrogate each molecular feature of interest to determine if it corresponds to a molecular ion, adduct (e.g. sodium, potassium, or dimer), or neutral loss.</li>
</ol>
6. Metabolite Identification
The workflow presented here is very general and can be applied to results from any instrument platform. An alternative strategy is presented in the Discussion section.
<ol>
	<li>For all statistically significant features (as determined from analysis in Step 6) review the raw data and confirm that parent ions, sodium adducts, isotopes, and fragments follow similar trends across the dataset. For example, the ANOVA-derived p-value for a parent ion should be similar to its isotope. This indicates that both molecular features correspond to the same metabolite.</li>
	<li>Search accurate mass measurements and/or neutral losses of statistically significant molecular features (choose C12 isotope if multiple isotopes are significant) against in-house (if available) or publically available metabolite databases such as:
	<ol class="lroman">
		<li>Human Metabolome Database (<a href="http://www.hmdb.ca/" target="_blank">http://www.hmdb.ca/</a>)</li>
		<li>Metlin (<a href="http://metlin.scripps.edu/metabo_search_alt2.php" target="_blank">http://metlin.scripps.edu/metabo_search_alt2.php</a>)</li>
		<li>Mass Bank (<a href="http://www.massbank.jp/?lang=en" target="_blank">http://www.massbank.jp/?lang=en</a>)</li>
		<li>Lipid Maps (<a href="http://www.lipidmaps.org/data/standards/index.html" target="_blank">http://www.lipidmaps.org/data/standards/index.html</a>)</li>
		<li>National Institute of Standards and Technology MS Search (<a href="http://chemdata.nist.gov/mass-spc/ms-search/" target="_blank">http://chemdata.nist.gov/mass-spc/ms-search/</a>)</li>
	</ol>
	</li>
	<li>Use software to predict a molecular formula from the accurate mass and isotopic distribution of your significant molecular features. In Waters software, this is performed using the Elemental Comp tool in the MassLynx package.</li>
	<li>Filter candidate metabolite identifications by: mass error (as suitable for your instrument platform), predicted molecular formula, biological relevance in the trends observed with respect to experimental design, and retention time (e.g. lipids and other non-polar compounds will have longer retention times when employing reverse phase chromatography).</li>
	<li>Perform subsequent experiments to acquire the MS/MS fragmentation for statistically significant molecular features. Whenever possible, metabolite identification should be based on matching experimental fragmentation peaks and retention time to that of an authentic standard acquired on the same instrument. Alternatively, experimental fragmentation can be matched to a spectral database.</li>
	<li>Identification confidence should be assigned to all reported metabolite identifications based on metabolomics standards initiative recommendations12. 
	Level 1: confident molecular identification based on orthogonal analytical parameters (accurate mass, retention time, and MS/MS fragmentation) relative to an authentic compound. 
	Level 2 refers to a putative identification base on physicochemical properties and/or spectral similarity with literature or spectral libraries. 
	Level 3 refers to the putative identification of a compound class based on physicochemical properties or spectral similarity. 
	Level 4 refers to an unknown compound.</li>
</ol>

<ol>
	<li>Theodoridis, G., Gika, H. G., 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=Mass+Spectrometry-Based+Holistic+Analytical+Approaches+for+Metabolite+Profiling+in+Systems+Biology+Studies.">Mass Spectrometry-Based Holistic Analytical Approaches for Metabolite Profiling in Systems Biology Studies.</a> Mass Spectrom. Rev. 30, 884-906 (2011).</li><li>Zelena, E., 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=Development+of+a+robust+and+repeatable+UPLC-MS+method+for+the+long-term+metabolomic+study+of+human+serum.">Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum.</a> Analytical Chemistry. 81, 1357-1364 (2009).</li><li>Michopoulos, F., Lai, L., Gika, H., Theodoridis, G., Wilson, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UPLC-MS-based+analysis+of+human+plasma+for+metabonomics+using+solvent+precipitation+or+solid+phase+extraction.">UPLC-MS-based analysis of human plasma for metabonomics using solvent precipitation or solid phase extraction.</a> Journal of Proteome Research. 8, 2114-2121 (2009).</li><li>Kamburov, A., Cavill, R., Ebbels, T. M., Herwig, R., Keun, H. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+pathway-level+analysis+of+transcriptomics+and+metabolomics+data+with+IMPaLA.">Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA.</a> Bioinformatics. 27, 2917-2918 (2011).</li><li>Xia, J., Wishart, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetPA:+a+web-based+metabolomics+tool+for+pathway+analysis+and+visualization.">MetPA: a web-based metabolomics tool for pathway analysis and visualization.</a> Bioinformatics. 26, 2342-2344 (2010).</li><li>Johnson, C. H., Gonzalez, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Challenges+and+opportunities+of+metabolomics.">Challenges and opportunities of metabolomics.</a> J. Cell. Physiol. 227, 2975-2981 (2012).</li><li>Issaq, H. J., Waybright, T. J., Veenstra, T. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+biomarker+discovery:+Opportunities+and+pitfalls+in+analytical+methods.">Cancer biomarker discovery: Opportunities and pitfalls in analytical methods.</a> Electrophoresis. 32, 967-975 (2011).</li><li>Zhou, B., Xiao, J. F., Tuli, L., Ressom, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LC-MS-based+metabolomics.">LC-MS-based metabolomics.</a> Mol. Biosyst. 8, 470-481 (2012).</li><li>Weckwerth, W., Morgenthal, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomics:+from+pattern+recognition+to+biological+interpretation.">Metabolomics: from pattern recognition to biological interpretation.</a> Drug Discov. Today. 10, 1551-1558 (2005).</li><li>Smith, C. A., 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=METLIN+-+A+metabolite+mass+spectral+database.">METLIN - A metabolite mass spectral database.</a> Ther. Drug Monit. 27, 747-751 (2005).</li><li>Culpepper, S. A., Aguinis, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=R+is+for+Revolution:+A+Cutting-Edge,+Free,+Open+Source+Statistical+Package.">R is for Revolution: A Cutting-Edge, Free, Open Source Statistical Package.</a> Organ Res. Methods. 14, 735-740 (2011).</li><li>Sumner, L. W., 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=Proposed+minimum+reporting+standards+for+chemical+analysis.">Proposed minimum reporting standards for chemical analysis.</a> Metabolomics. 3, 211-221 (2007).</li><li>Vuckovic, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+trends+and+challenges+in+sample+preparation+for+global+metabolomics+using+liquid+chromatography-mass+spectrometry.">Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry.</a> Anal. Bioanal. Chem. 403, 1523-1548 (2012).</li><li>Garcia-Canaveras, J. C., Donato, M. T., Castell, J. V., Lahoz, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comprehensive+untargeted+metabonomic+analysis+of+human+steatotic+liver+tissue+by+RP+and+HILIC+chromatography+coupled+to+mass+spectrometry+reveals+important+metabolic+alterations.">A comprehensive untargeted metabonomic analysis of human steatotic liver tissue by RP and HILIC chromatography coupled to mass spectrometry reveals important metabolic alterations.</a> Journal of Proteome Research. 10, 4825-4834 (2011).</li><li>Nordstrom, A., Want, E., Northen, T., Lehtio, J., Siuzdak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+ionization+mass+spectrometry+strategy+used+to+reveal+the+complexity+of+metabolomics.">Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics.</a> Analytical Chemistry. 80, 421-429 (2008).</li><li>Horai, H., 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=MassBank:+a+public+repository+for+sharing+mass+spectral+data+for+life+sciences.">MassBank: a public repository for sharing mass spectral data for life sciences.</a> J. Mass Spectrom. 45, 703-714 (2010).</li><li>Wishart, D. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+metabolite+identification.">Advances in metabolite identification.</a> Bioanalysis. 3, 1769-1782 (2011).</li><li>Broeckling, C., Heuberger, A., Prince, J., Ingelsson, E., Prenni, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assigning+precursor-product+ion+relationships+in+indiscriminant+MS/MS+data+from+non-targeted+metabolite+profiling+studies.">Assigning precursor-product ion relationships in indiscriminant MS/MS data from non-targeted metabolite profiling studies.</a> Metabolomics. , 1-11 (2012).</li><li>Kangas, L. 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=In+silico+identification+software+(ISIS):+a+machine+learning+approach+to+tandem+mass+spectral+identification+of+lipids.">In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids.</a> Bioinformatics. 28, 1705-1713 (2012).</li></ol>

The authors declare that they have no competing financial interests.

This tutorial is meant to serve as a starting point for conducting large scale non-targeted metabolite profiling by UPLC-MS. The workflow is focused on metabolites that can be extracted with an aqueous methanol solvent, retained on a C8 or C18 UPLC column, and detected as positive ions. In the situation where there is not a pre-determined bias towards a specific metabolite class and a hypothesis generating global profile is desired, this protocol is valuable as it will result in the detection of a large percentage of ser...

The term "metabolomics" can encompass many things. For example, a metabolomics experiment can be performed using a variety of analytical platforms such as NMR and both gas and/or liquid chromatography coupled with mass spectrometry. Furthermore, metabolomics experiments can be performed in a targeted or non-targeted manner, or a combination of both. A targeted metabolomics experiment will involve directed analysis of a panel of molecules important to the biological question at hand (e.g. small molecules involved in the TCA cycle will allow for accurate quantitation of that pathway). In this situation, the biological hypothesis is dictating the choice of metabolites to be targeted in the analysis and the analytical steps are optimized for the detection of these molecules. Alternatively, a non-targeted metabolomics experiment is hypothesis generating. In this case, the experiment is performed in a broad and unbiased manner to enable detection of as many metabolites as possible. The results from a non-targeted experiment will drive the next step of the research (which in many cases may involve a targeted metabolomics workflow). It is also possible to combine the two approaches, in which case an experiment is performed in a non-targeted manner while concurrently a panel of known molecules are monitored within the data. 
The tutorial presented here is focused specifically on non-targeted metabolite profiling of serum. As described above, the non-targeted approach provides an unbiased view of the detectable metabolites, can generate large amounts of information, and ultimately allow for novel discoveries. The use of this approach, specifically employing ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS), is becoming widespread 1, 2, 3 and involves the following steps: (1) experimental design (2) sample collection (3) sample preparation (4) data acquisition by UPLC-MS (5) data pre-processing (peak detection, integration, alignment, and normalization) (6) statistical data analysis (both uni- and multivariate) (7) metabolite identification and (8) biological interpretation. 
Currently, there are no established standard methods for UPLC-MS based non-targeted metabolite profiling and subsequent data pre-processing steps. This lack of standardization is due in part to one of the primary analytical challenges of metabolite profiling; the chemical diversity of the metabolome. Because of this diversity, it is impossible for a single extraction method or mass spectrometry acquisition method to provide comprehensive coverage of all metabolites in a single analysis. In concept, metabolite coverage can be maximized by using multiple extractions (e.g. aqueous, methanol, chloroform:methanol, etc.) coupled with various chromatographic conditions (e.g. reverse phase, HILIC, etc.) and various ionization modes (e.g. positive ion, negative ion, chemical ionization, etc.). Often, however, researchers do not have a pre-determined bias for a specific chemical class and thus the expense of performing multiple extractions and instrument acquisitions is not warranted, especially for large-scale experiments. Thus, the video tutorial presented here was designed to provide a general procedure for large scale non-targeted metabolite profiling of serum by UPLC-MS. It will enable new and established laboratories to perform these types of experiments and the building blocks upon which they can expand the approach for various sample types, specific chemical classes, or targeted analysis. Specifically, this protocol will include the steps of: serum sample preparation, sample organization for large scale studies, UPLC-MS data acquisition, quality control (QC) procedures, and metabolite identification. Strategies for data pre-processing and statistical analysis are also presented.
The protocol will not focus on the steps of experimental design, sample collection, or biological data interpretation as it is outside the scope of this tutorial. However, many resources exist in the literature for these topics and the authors encourage researchers new to metabolomics to explore these thoroughly 4, 5, 6, 7, 8, 9. In particular, experimental design is extremely important and is critical to the success of a non-targeted metabolomics experiment. Factors such as appropriate biological replication and consistency in sample collection procedure (e.g. time on bench, storage temperature, storage time, freeze-thaws, etc.) must be considered to ensure a viable study and to facilitate appropriate biological interpretation of the data.

<table border="1">
 <tbody>
 <tr>
 <td>Name of Reagent/Material</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td>96 well plates - 500 &mu;l wells</td>
 <td>VWR</td>
 <td>40002-020</td>
 <td>These are used for sample preparation</td>
 </tr>
 <tr>
 <td>96 well plate mats</td>
 <td>VWR</td>
 <td>89026-514</td>
 <td>These are used for sample preparation</td>
 </tr>
 <tr>
 <td>96 well plates - 350 &mu;l wells</td>
 <td>Waters Corporation</td>
 <td>WAT058943</td>
 <td>These are used for sample injection</td>
 </tr>
 <tr>
 <td>96 well plate mats</td>
 <td>Waters Corporation</td>
 <td>186000857</td>
 <td>These are used for sample injection</td>
 </tr>
 <tr>
 <td>96 well plate heat seals</td>
 <td>Waters Corporation</td>
 <td>186002789</td>
 <td>These can be used for sample injection or long term storage</td>
 </tr>
 <tr>
 <td>96 well plate heat sealer </td>
 <td>Waters Corporation</td>
 <td>186002786</td>
 <td></td>
 </tr>
 <tr>
 <td>LC-MS grade methanol</td>
 <td>Fluka</td>
 <td>34966</td>
 <td></td>
 </tr>
 <tr>
 <td>LC-MS grade acetonitrile</td>
 <td>Fluka</td>
 <td>34967</td>
 <td></td>
 </tr>
 <tr>
 <td>LC-MS grade aater</td>
 <td>Fluka</td>
 <td>39253</td>
 <td></td>
 </tr>
 <tr>
 <td>LC-MS grade formic acid</td>
 <td>Fluka</td>
 <td>56302</td>
 <td></td>
 </tr>
 <tr>
 <td>Multichannel electronic pipettor</td>
 <td>VWR</td>
 <td>89000-674</td>
 <td></td>
 </tr>
 <tr>
 <td>Pipett tips</td>
 <td>Eclipse (purchased through Light Labs)</td>
 <td>B-5061/B-4061</td>
 <td></td>
 </tr>
 <tr>
 <td>Chilled centrifuge - Allegra X-12R</td>
 <td>Beckman Coulter</td>
 <td>N/A - contact Beckman Coulter</td>
 <td></td>
 </tr>
 <tr>
 <td>Acquity Ultra performance Liquid Chromatography (UPLC) System</td>
 <td>Waters Corporation</td>
 <td>N/A - contact Waters Corporation</td>
 <td></td>
 </tr>
 <tr>
 <td>UPLC C8 column (gradient option a)</td>
 <td>Waters Corporation</td>
 <td>186002876</td>
 <td></td>
 </tr>
 <tr>
 <td>UplC T3 column (gradient option b) </td>
 <td>Waters Corporation</td>
 <td>186003536</td>
 <td></td>
 </tr>
 <tr>
 <td>Xevo G2 Q-TOF Mass spectrometer</td>
 <td>Waters Corporation</td>
 <td>N/A - contact Waters Corporation</td>
 <td></td>
 </tr>
 </tbody>
</table>

large scale non-targeted metabolomic profiling of serum by ultra performance liquid chromatography-mass spectrometry (uplc-ms)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

The basic analytical steps of a non-targeted metabolite profiling experiment by UPLC-MS are outlined in Figure 1. The raw data for each sample can be visualized as a base peak chromatogram. Figure 2 shows an example base peak chromatogram of a serum sample analyzed by gradient option (a) in the tutorial. Following statistical analysis as described above, metabolite identification is attempted for all statistically significant molecular features. Confident identification (level 1) req...

Watch this Scientific Journal Video about Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS at JoVE.com

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

规模以上非靶向代谢组学剖析血清超高效液相色谱 - 质谱（UPLC-MS）

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

large-scale-non-targeted-metabolomic-profiling-serum-ultra

Research

JoVE Journal

Chemistry

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

12.6K 次观看.  Colorado State University. Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

视频: Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

不相关的代谢组学，生物源使用超高效液相色谱 - 高分辨质谱（UPLC-HRMS）

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

科研

化学

Profiling the Triacylglyceride Contents in Bat Integumentary Lipids by Preparative Thin Layer Chromatography and MALDI-TOF Mass Spectrometry

The mammalian integument includes sebaceous glands that secrete an oily material onto the skin surface. Sebum production is part of the innate immune system that is protective against pathogenic microbes. Abnormal sebum production and chemical composition are also a clinical symptom of specific skin diseases. Sebum contains a complex mixture of lipids, including triacylglycerides, which is species-specific. The broad chemical properties exhibited by diverse lipid classes hinder the specific determination of sebum composition. Analytical techniques for lipids typically require chemical derivatizations that are labor-intensive and increase sample preparation costs. This paper describes how to extract lipids from mammalian integument, separate broad lipid classes by thin-layer chromatography, and profile the triacylglyceride contents using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. This robust method enables a direct determination of the triacylglyceride profiles among species and individuals, and it can be readily applied to any taxonomic group of mammals.

Mammalian integument contains solvent-extractable lipids that can provide chemical compositions characteristic of individual species. This paper presents a routine method for separating broad lipid classes isolated from integumentary tissues using thin layer chromatography and determining the triacylglyceride profile by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

用制备薄层色谱和MALDI-TOF质谱分析的三酸甘油酯含量的蝙蝠外皮血脂

The mammalian integument includes sebaceous glands that secrete an oily material onto the skin surface. Sebum production is part of the innate immune ...

Isolation and Preparation of Bacterial Cell Walls for Compositional Analysis by Ultra Performance Liquid Chromatography

The bacterial cell wall is critical for the determination of cell shape during growth and division, and maintains the mechanical integrity of cells in the face of turgor pressures several atmospheres in magnitude. Across the diverse shapes and sizes of the bacterial kingdom, the cell wall is composed of peptidoglycan, a macromolecular network of sugar strands crosslinked by short peptides. Peptidoglycan&#8217;s central importance to bacterial physiology underlies its use as an antibiotic target and has motivated genetic, structural, and cell biological studies of how it is robustly assembled during growth and division. Nonetheless, extensive investigations are still required to fully characterize the key enzymatic activities in peptidoglycan synthesis and the chemical composition of bacterial cell walls. High Performance Liquid Chromatography (HPLC) is a powerful analytical method for quantifying differences in the chemical composition of the walls of bacteria grown under a variety of environmental and genetic conditions, but its throughput is often limited. Here, we present a straightforward procedure for the isolation and preparation of bacterial cell walls for biological analyses of peptidoglycan via HPLC and Ultra Performance Liquid Chromatography (UPLC), an extension of HPLC that utilizes pumps to deliver ultra-high pressures of up to 15,000 psi, compared with 6,000 psi for HPLC. In combination with the preparation of bacterial cell walls presented here, the low-volume sample injectors, detectors with high sampling rates, smaller sample volumes, and shorter run times of UPLC will enable high resolution and throughput for novel discoveries of peptidoglycan composition and fundamental bacterial cell biology in most biological laboratories with access to an ultracentrifuge and UPLC.

The bacterial cell wall is composed of peptidoglycan, a macromolecular network of sugar strands crosslinked by peptides. Ultra Performance Liquid Chromatography provides high resolution and throughput for novel discoveries of peptidoglycan composition. We present a procedure for the isolation of cell walls (sacculi) and their subsequent preparation for analysis via UPLC.

通过超性能液相色谱对细菌细胞壁进行隔离和制备，进行组成分析

The bacterial cell wall is critical for the determination of cell shape during growth and division, and maintains the mechanical integrity of cells in the ...

Post Column Derivatization Using Reaction Flow High Performance Liquid Chromatography Columns

A protocol for the use of reaction flow high performance liquid chromatography columns for methods employing post column derivatization (PCD) is presented. A major difficulty in adapting PCD to modern HPLC systems and columns is the need for large volume reaction coils that enable reagent mixing and then the derivatization reaction to take place. This large post column dead volume leads to band broadening, which results in a loss of observed separation efficiency and indeed detection in sensitivity. In reaction flow post column derivatization (RF-PCD) the derivatization reagent(s) are pumped against the flow of mobile phase into either one or two of the outer ports of the reaction flow column where it is mixed with column effluent inside a frit housed within the column end fitting. This technique allows for more efficient mixing of the column effluent and derivatization reagent(s) meaning that the volume of the reaction loops can be minimized or even eliminated altogether. It has been found that RF-PCD methods perform better than conventional PCD methods in terms of observed separation efficiency and signal to noise ratio. A further advantage of RF-PCD techniques is the ability to monitor effluent coming from the central port in its underivatized state. RF-PCD has currently been trialed on a relatively small range of post column reactions, however, there is currently no reason to suggest that RF-PCD could not be adapted to any existing one or two component (as long as both reagents are added at the same time) post column derivatization reaction.

A protocol for the use of reaction flow high performance liquid chromatography columns for methods employing post column derivatization (PCD) is presented.

柱后衍生用反应流高效液相色谱柱

A protocol for the use of reaction flow high performance liquid chromatography columns for methods employing post column derivatization (PCD) is ...

Radiolabeling and Quantification of Cellular Levels of Phosphoinositides by High Performance Liquid Chromatography-coupled Flow Scintillation

Phosphoinositides (PtdInsPs) are essential signaling lipids responsible for recruiting specific effectors and conferring organelles with molecular identity and function. Each of the seven PtdInsPs varies in their distribution and abundance, which are tightly regulated by specific kinases and phosphatases. The abundance of PtdInsPs can change abruptly in response to various signaling events or disturbance of the regulatory machinery. To understand how these events lead to changes in the amount of PtdInsPs and their resulting impact, it is important to quantify PtdInsP levels before and after a signaling event or between control and abnormal conditions. However, due to their low abundance and similarity, quantifying the relative amounts of each PtdInsP can be challenging. This article describes a method for quantifying PtdInsP levels by metabolically labeling cells with 3H-myo-inositol, which is incorporated into PtdInsPs. Phospholipids are then precipitated and deacylated. The resulting soluble 3H-glycero-inositides are further extracted, separated by high-performance liquid chromatography (HPLC), and detected by flow scintillation. The labeling and processing of yeast samples is described in detail, as well as the instrumental setup for the HPLC and flow scintillator. Despite losing structural information regarding acyl chain content, this method is sensitive and can be optimized to concurrently quantify all seven PtdInsPs in cells.

Phosphoinositides are signaling lipids whose relative abundance rapidly changes in response to various stimuli. This article describes a method to measure the abundance of phosphoinositides by metabolically labeling cells with 3H-myo-inositol, followed by extraction and deacylation. Extracted glycero-inositides are then separated by high-performance liquid chromatography and quantified by flow scintillation.

放射性标记和磷酸肌醇的细胞水平的定量高效液相色谱耦合流动闪烁

Phosphoinositides (PtdInsPs) are essential signaling lipids responsible for recruiting specific effectors and conferring organelles with molecular ...

A Study of the Complexation of Mercury(II) with Dicysteinyl Tetrapeptides by Electrospray Ionization Mass Spectrometry

In this study we evaluated a method for the characterization of complexes, formed in different relative ratios of mercury(II) to dicysteinyl tetrapeptide, by electrospray ionization orbitrap mass spectrometry. This strategy is based on previous successful characterization of mercury-dicysteinyl complexes involving tripeptides by utilizing mass spectrometry among other techniques. Mercury(II) chloride and a dicysteinyl tetrapeptide were incubated in a degassed buffered medium at varying stoichiometric ratios. The complexes formed were subsequently analyzed on an electrospray mass spectrometer consisting of a hybrid linear ion- and orbi- trap mass analyzer. The electrospray ionization mass spectrometry (ESI-MS) spectra were acquired in the positive mode and the observed peaks were then analyzed for distinct mercury isotopic distribution patterns and associated monoisotopic peak. This work demonstrates that an accurate stoichiometry of mercury and peptide in the complexes formed under specified electrospray ionization conditions can be determined by using high resolution ESI MS based on distinct mercury isotopic distribution patterns.

A Study of the Complexation of MercuryII with Dicysteinyl Tetrapeptides by Electrospray Ionization Mass Spectrometry

The characterization of complexes formed in different relative ratios of mercury(II) to dicysteinyl tetrapeptides by electrospray ionization orbitrap mass spectrometry is presented.

汞（II）与Dicysteinyl四肽的络合的电喷雾质​​谱研究

In this study we evaluated a method for the characterization of complexes, formed in different relative ratios of mercury(II) to dicysteinyl tetrapeptide, ...

An HS-MRM Assay for the Quantification of Host-cell Proteins in Protein Biopharmaceuticals by Liquid Chromatography Ion Mobility QTOF Mass Spectrometry

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring high sensitivity and a wide dynamic range. Mass spectrometry-based quantification assays for proteins typically involve protein digestion followed by the selective reaction monitoring/multiple reaction monitoring (SRM/MRM) quantification of peptides using a low-resolution (Rs ~1,000) tandem quadrupole mass spectrometer. One of the limitations of this approach is the interference phenomenon observed when the peptide of interest has the "same" precursor and fragment mass (in terms of m/z values) as other co-eluting peptides present in the sample (within a 1-Da window). To avoid this phenomenon, we propose an alternative mass spectrometric approach, a high selectivity (HS) MRM assay that combines the ion mobility separation of peptide precursors with the high-resolution (Rs ~30,000) MS detection of peptide fragments. We explored the capabilities of this approach to quantify low-abundance peptide standards spiked in a monoclonal antibody (mAb) digest and demonstrated that it has the sensitivity and dynamic range (at least 3 orders of magnitude) typically achieved in HCP analysis. All six peptide standards were detected at concentrations as low as 0.1 nM (1 femtomole loaded on a 2.1-mm ID chromatographic column) in the presence of a high-abundance peptide background (2 &#181;g of a mAb digest loaded on-column). When considering the MW of rabbit phosphorylase (97.2 kDa), from which the spiked peptides were derived, the LOQ of this assay is lower than 50 ppm. Relative standard deviations (RSD) of peak areas (n = 4 replicates) were less than 15% across the entire concentration range investigated (0.1-100 nM or 1-1,000 ppm) in this study.

Here, we describe a chromatographic assay coupled with the ion mobility separation of peptide precursors followed by the high-resolution (~30,000) MS-detection of peptide fragments for the quantification of spiked peptide standards in a monoclonal antibody digest.

液相色谱离子迁移 QTOF 质谱测定蛋白质生物中宿主细胞蛋白的 HS-MRM 法

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring ...

Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing metabolic reactions, DNA repair and replication. Therefore, a detailed understanding of these processes provides critical information on how cells function. Integrative structural MS methods offer structural and dynamical information on protein complex assembly, complex connectivity, subunit stoichiometry, protein oligomerization and ligand binding. Recent advances in integrative structural MS have allowed for the characterization of challenging biological systems including large DNA binding proteins and membrane proteins. This protocol describes how to integrate diverse MS data such as native MS and ion mobility-mass spectrometry (IM-MS) with molecular dynamics simulations to gain insights into a helicase-nuclease DNA repair protein complex. The resulting approach provides a framework for detailed studies of ligand binding to other protein complexes involved in important biological processes.

Mass spectrometry (MS) has emerged as an important tool for the investigation of structure and dynamics of macromolecular assemblies. Here, we integrate MS-based approaches to interrogate protein complex formation and ligand binding.

综合结构质谱分析蛋白质结构和蛋白质配体配合物

Proteins are an important class of biological macromolecules that play many key roles in cellular functions including gene expression, catalyzing ...

Luminophore Formation in Various Conformations of Bovine Serum Albumin by Binding of Gold(III)

The purpose of the presented protocols is to study the process of Au(III) binding to BSA, yielding conformation change-induced red fluorescence (&#955;em = 640 nm) of BSA-Au(III) complexes. The method adjusts the pH to show that the emergence of the red fluorescence is correlated with the pH-induced equilibrium transitions of the BSA conformations. Red fluorescent BSA-Au(III) complexes can only be formed with an adjustment of pH at or above 9.7, which corresponds to the &#34;A-form&#34; conformation of BSA. The protocol to adjust the BSA to Au molar ratio and to monitor the time-course of the process of Au(III) binding is described. The minimum number of Au(III) per BSA, to produce the red fluorescence, is less than seven. We describe the protocol in steps to illustrate the presence of multiple Au(III) binding sites in BSA. First, by adding copper (Cu(II)) or nickel (Ni(II)) cations followed by Au(III), this method reveals a binding site for Au(III) that is not the red fluorophore. Second, by modifying BSA by thiol capping agents, another nonfluorophore-forming Au(III) binding site is revealed. Third, changing the BSA conformation by cleaving and capping of the disulfide bonds, the possible Au(III) binding site(s) are illustrated. The protocol described, to control the BSA conformations and Au(III) binding, can be generally applied to study the interactions of other proteins and metal cations.

Luminophore Formation in Various Conformations of Bovine Serum Albumin by Binding of GoldIII

The protocols for studying the binding of gold cations (Au(III)) to various conformations of bovine serum albumin (BSA) as well as for characterizing the conformational dependent unique BSA-Au fluorescence are presented.

结合金 (III.) 对牛血清白蛋白不同构象的 Luminophore 形成

The purpose of the presented protocols is to study the process of Au(III) binding to BSA, yielding conformation change-induced red fluorescence (&#955;em ...

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as a useful tool for top-down proteomics that aims to characterize proteoforms in complex proteomes. However, the application of CZE-MS/MS for large-scale top-down proteomics has been impeded by the low sample-loading capacity and narrow separation window of CZE. Here, a protocol is described using CZE-MS/MS with a microliter-scale sample-loading volume and a 90-min separation window for large-scale top-down proteomics. The CZE-MS/MS platform is based on a linear polyacrylamide (LPA)-coated separation capillary with extremely low electroosmotic flow, a dynamic pH-junction-based online sample concentration method with a high efficiency for protein stacking, an electro-kinetically pumped sheath flow CE-MS interface with extremely high sensitivity, and an ion trap mass spectrometer with high mass resolution and scan speed. The platform can be used for the high-resolution characterization of simple intact protein samples and the large-scale characterization of proteoforms in various complex proteomes. As an example, a highly efficient separation of a standard protein mixture and a highly sensitive detection of many impurities using the platform is demonstrated. As another example, this platform can produce over 500 proteoform and 190 protein identifications from an Escherichia coli proteome in a single CZE-MS/MS run.

A detailed protocol is described for the separation, identification, and characterization of proteoforms in protein samples using capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS). The protocol can be used for the high-resolution characterization of proteoforms in simple protein samples and the large-scale identification of proteoforms in complex proteome samples.