Name: large scale non-targeted metabolomic profiling of serum by ultra performance liquid chromatography-mass spectrometry (uplc-ms)
Uploaded: 2013-03-14T13:00:00
Description: 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

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, us...

<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. 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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.

울트라 성능 액체 크로마토 그래피 - 질량 분석법 (UPLC-MS)에 의해 혈청 대규모 비 타겟 Metabolomic 프로파일

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

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Chemistry

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)

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

Ultraperformance 액체 크로마토 그래피 고해상도 질량 분석 (UPLC-HRMS)를 사용하여 생물 소스에서 타겟이 불분명 한 대사

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

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