Agilent Lipidomics. Workflow Guide. MS Data Qualitative Workflow. With MS/MS ID Confirmation. Find features in one file by MFE in MH Qual.

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1 Agilent Lipidomics Workflow Guide A MS Data Lipidomics Workflows B MS/MS Data Lipidomics Workflows MS Data Qualitative Workflow MS Data Profiling Workflow With MS/MS ID Confirmation MS/MS Data Qualitative Workflow MS/MS Data Profiling Workflow Find features in one file by MFE in MH Qual Find features in multiple files by MFE in MH Qual Do an MS Data Profiling Workflow preferred ion list Find features in one file by MFE or Auto MS/MS in MH Qual Find features in multiple files by MFE in MH Qual Do an MS SimLipid ID search Find statistically significant features with MPP Find features by Auto MS/MS in MH Qual Do an MS/MS SimLipid ID search Do an MS/MS SimLipid ID search Confirm features by recursive analysis in MH Qual Do an MS SimLipid ID search Confirm MS IDs with an MS/MS SimLipid search Find statistically significant features with MPP

2 Notices Agilent Technologies, Inc No part of this manual may be reproduced in any form or by any means (including electronic storage and retrieval or translation into a foreign language) without prior agreement and written consent from Agilent Technologies, Inc. as governed by United States and international copyright laws. Manual Part Number EN Edition Revision A, December 2012 Printed in USA Agilent Technologies, Inc Stevens Creek Blvd. Santa Clara, CA Acknowledgements Microsoft is either a registered trademark or trademark of Microsoft Corporation in the United States and/or other countries. SimLipid software is made by PREMIER Biosoft International. Warranty The material contained in this document is provided as is, and is subject to being changed, without notice, in future editions. Further, to the maximum extent permitted by applicable law, Agilent disclaims all warranties, either express or implied, with regard to this manual and any information contained herein, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Agilent shall not be liable for errors or for incidental or consequential damages in connection with the furnishing, use, or performance of this document or of any information contained herein. Should Agilent and the user have a separate written agreement with warranty terms covering the material in this document that conflict with these terms, the warranty terms in the separate agreement shall control. Technology Licenses The hardware and/or software described in this document are furnished under a license and may be used or copied only in accordance with the terms of such license. Restricted Rights If software is for use in the performance of a U.S. Government prime contract or subcontract, Software is delivered and licensed as Commercial computer software as defined in DFAR (June 1995), or as a commercial item as defined in FAR 2.101(a) or as Restricted computer software as defined in FAR (June 1987) or any equivalent agency regulation or contract clause. Use, duplication or disclosure of Software is subject to Agilent Technologies standard commercial license terms, and non-dod Departments and Agencies of the U.S. Government will receive no greater than Restricted Rights as defined in FAR (c)(1-2) (June 1987). U.S. Government users will receive no greater than Limited Rights as defined in FAR (June 1987) or DFAR (b)(2) (November 1995), as applicable in any technical data. Safety Notices CAUTION A CAUTION notice denotes a hazard. It calls attention to an operating procedure, practice, or the like that, if not correctly performed or adhered to, could result in damage to the product or loss of important data. Do not proceed beyond a CAUTION notice until the indicated conditions are fully understood and met. WARNING A WARNING notice denotes a hazard. It calls attention to an operating procedure, practice, or the like that, if not correctly performed or adhered to, could result in personal injury or death. Do not proceed beyond a WARNING notice until the indicated conditions are fully understood and met. 2

3 Contents 1 Before You Begin 5 Introduction 6 Overview of the Workflows 10 Required Items 14 2 MS Data Lipidomics Workflows 15 Introduction 16 MS Data Qualitative Workflow 17 MS Data Profiling Workflow 28 MS Data Profiling Workflow with MS/MS ID Confirmation 42 3 MS/MS Data Lipidomics Workflows 47 Introduction 48 MS/MS Data Qualitative Workflow 49 MS/MS Data Profiling Workflow 51 4 Reference Information 55 Definitions 56 References 64 3

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5 Before You Begin Make sure you read and understand the information in this chapter and have the necessary computer equipment, software and data before you start your analysis. A MS Data Lipidomics Workflows B MS/MS Data Lipidomics Workflows MS Data Qualitative Workflow MS Data Profiling Workflow With MS/MS ID Confirmation MS/MS Data Qualitative Workflow MS/MS Data Profiling Workflow Find features in one file by MFE in MH Qual Find features in multiple files by MFE in MH Qual Do an MS Data Profiling Workflow preferred ion list Find features in one file by MFE or Auto MS/MS in MH Qual Find features in multiple files by MFE in MH Qual Do an MS SimLipid ID search Find statistically significant features with MPP Find features by Auto MS/MS in MH Qual Do an MS/MS SimLipid ID search Do an MS/MS SimLipid ID search Confirm features by recursive analysis in MH Qual Do an MS SimLipid ID search Confirm MS IDs with an MS/MS SimLipid search Find statistically significant features with MPP Introduction 6 Overview of the Workflows 10 Required Items 14

6 Before You Begin Introduction Introduction To understand the specifics of the lipidomics workflows, you first familiarize yourself with lipidomics and its studies. What is lipodomics? Lipidomics is one of the four components that make up the field of metabolomics research. Metabolites are the end products of all biological/cellular processes, and metabolome refers to the collective set of all metabolites generated in a biological system (cell, tissue, organ or organism). Metabolomics is the scientific study to characterize and identify the metabolome. The metabolome is comprised of four major classes of biological molecules: sugars, amino acids, nucleotides, and lipids. The systematic study of the entire lipid profile of a cell/tissue/organ/organism is referred to as lipidomics. Mass spectrometry is one of the most widely used technologies in lipidomics research for the identification as well as quantitation of thousands of lipid molecules that constitute the cellular lipidome, the full lipid complement of a biological system. The Agilent Lipidomics Workflow Guide is a supplement to the Metabolomics Discovery Workflow Guide. The explanations of the discovery metabolomics workflow for MS data given in the latter guide are the foundation for understanding the lipidomics profiling workflows for MS and MS/MS data. Please read the introductory material in that guide before you read the rest of this guide. What are lipids? Lipids are a very broad, diversified group of hydrophobic or amphipathic (both hydrophobic and hydrophilic) small molecules that are critically involved in maintaining structural integrity of cellular membranes, serve as cellular energy stores and are also key intermediates of several signal transduction pathways. The amphipathic nature of some lipids lets them form structures such as vesicles, liposomes, or membranes in an aqueous environment. Lipids include fats, waxes, sterols, fat-soluble vitamins (such as vitamins A, D, E, and K), monoglycerides, diglycerides, triglycerides, phospholipids, and others. Biological lipids originate entirely or in part from two distinct types of biochemical subunits or building-blocks : ketoacyl and isoprene groups. Using these building blocks, lipids may be divided into eight categories (see fatty acids, glycerolipids, glycerophospholipids, sphingolipids, saccharolipids, polyketides (derived from condensation of ketoacyl subunits), sterol lipids and prenol lipids (derived from condensation of isoprene subunits). 6

7 Before You Begin Introduction What is discovery lipidomics? Discovery lipidomics experiments involve examining an untargeted suite of lipids, finding the ones with statistically significant variations in abundance within a set of experimental versus control data samples, and determining their chemical structure. Pathway analysis lets you connect the lipid with the biological process or condition. Discovery lipidomics involves the comparison of lipidomes between control and test groups to find differences in their profiles. Discovery lipidomics is a subset of discovery metabolomics and uses the same steps for analyses: profiling, identification, and interpretation. For more details on these steps, see the Metabolomics Discovery Workflow Guide. Profiling Profiling (also known as differential expression analysis) involves finding the interesting lipids with statistically significant variations in abundance within a set of experimental and control samples. Profiling usually involves comprehensive feature extraction to find unexpected lipids. A lipid is represented by its molecular features, which are defined by the combination of retention time, mass or mass spectra and abundance. The steps involved in profiling are: 1. Data Collection: GC/MS or LC/MS is required to separate and detect lipids. Because lipidomes are large and exhibit significant natural variation, even in normal organisms, real differences can only be seen by acquiring data on large numbers of samples containing large numbers of compounds. Analytical instruments that have low error rates and facilitate high throughput are necessary. 2. Feature Finding: Feature finding specifically finds all of the lipids by mass, retention time and abundance. Even with the best separation, though, a peak can contain multiple components. You can find multiple components in the same peak using the molecular feature extraction (MFE) algorithm within the MassHunter Qualitative Analysis program. MFE finds lipids that are chromatographically unresolved or poorly resolved and generates an extracted ion chromatogram and a reconstructed, single component spectrum for each lipid. 3. Filtering Features: With Mass Profiler Professional (MPP) different assessment tools are provided that filter out features, leaving only the most relevant. MPP also handles the next two steps. 4. Data Normalization: Normalization lets data collected over a period of time be corrected for changes in retention time and/or response so that a single feature common to several samples is not treated as a unique feature being separately sought in each sample. 5. Statistical Analysis: Statistical data analysis is used to discover significant differences between the sample sets. Identification Identification is the determination of the chemical structure of these lipids after profiling. MS and MS/MS data are evaluated using searches performed with SimLipid software (PREMIER Biosoft), which is specifically designed to handle lipid data analysis. Accurate mass data makes this database searching more effective by narrow- 7

8 Before You Begin Introduction ing the mass window that needs to be searched and thus reducing the number of possible identities. Interpretation Interpretation, the last step in the workflow, makes connections between the lipids discovered and the biological processes or conditions. Once the lipids are identified, it is necessary to understand their relation to biological pathways in metabolism by interpreting the results of the experiment. Pathway analysis (Pathway Architect) makes connections between the lipid compounds discovered and the biological processes or conditions being studied, and helps to elucidate the biological relevance of lipidomics data in a systems context. The Agilent Lipidomics Workflow Guide focuses on profiling and identification, especially on two profiling lipid workflows for MS data and one for MS/MS data, along with lipid identification with SimLipid for both MS and MS/MS data. Uses of discovery lipidomics Discovery lipidomics may be used to perform the following analyses: Compare two or more biological groups Find and identify potential biomarkers Look for biomarkers of toxicology Understand biological pathways Integrate the lipidomics data with other multi-omics data sets to better understand the systems biology Discover new lipids Develop data mining and data processing procedures that produce characteristic markers for a set of samples Construct statistical models for sample classification. Agilent enables lipidomics research for a variety of applications: Basic and clinical research - Identify and validate lipid biomarkers that correlate with disease states as well as provide fundamental insights into biology Pharmaceutical - Identify lipids and markers of toxicity for drug discovery and development Agriculture - Identify and understand metabolic pathways to optimize crop development, yield improvement, and pesticide/herbicide resistance Environmental - Identify lipids that relate to the effects of chemicals and other stressors in the environment on a biological system Biofuels - Identify lipid profiles to optimize fermentation processes and biofuel production Food/Nutrition - Identify the presence or absence of lipids that correlate with major traits such as food quality, authenticity, taste, and nutritional value, and aid in the development of nutraceuticals 8

9 Before You Begin Introduction What is targeted lipidomics? Targeted lipidomics involves a further narrowing of the feature set of known compounds for either MS or MS/MS data. After a data dependent (Find by Auto MS/ MS) or targeted analysis of the samples with the Qualitative Analysis program, you can export the filtered data into SimLipid for identifying additional compounds and fragments. 9

10 Before You Begin Overview of the Workflows Overview of the Workflows Unlike the Metabolomics Discovery Workflow Guide, which takes you through one discovery or profiling workflow for MS data only and does not focus on metabolite identification, this guide is concerned with all the workflows in which you can use the SimLipid identification software from PREMIER Biosoft for both MS and MS/MS data. Agilent and PREMIER Biosoft have worked together to integrate the lipid identification at MS and MS/MS levels using SimLipid software in the Compound Exchange Format (CEF), which is unique to Agilent. This allows for seamless portability of the lipid output data between different Agilent programs and SimLipid. Some workflows are more useful than others depending on the goals of your study, and the later workflows build on the steps of the previous workflows. Lipidomics workflows for MS Data Depending on the goal of your experiment or study, you can take your MS data through one or more of three processes or workflows: MS Data Qualitative Workflow MS Data Profiling Workflow MS Data Profiling Workflow with MS/MS ID confirmation Each of these workflows builds on the previous one. For the second and third workflows, this guide refers you to previous instructions where appropriate. MS Data Qualitative Workflow If you want to quickly see if a lipid category or class is present in your data, you use the simple MS Data Qualitative Workflow. See Figure 1. Figure 1 MS Data Qualitative Workflow 10

11 Before You Begin Overview of the Workflows MS Data Profiling Workflow A typical Agilent lipidomics profiling workflow for MS data is illustrated in Figure 2, starting with data acquisition through analysis involving untargeted (discovery) LC/ MS. Molecular feature extraction (MFE) and Find by Formula (FbF) are two different algorithms used by the Qualitative Analysis program for finding compounds. Find by Formula (FbF) is usually used for recursive analysis with the results files generated by MPP for quality control, statistical analysis, visualization and interpretation. The SimLipid CEF file can be used by MPP and Pathway Architect for a biological pathways analysis. Figure 2 MS Data Profiling Workflow MS Data Profiling Workflow with MS/MS ID Confirmation The specific goals of the MS data lipidomics profiling workflow with MS/MS ID confirmation are: 1 Present an effective step-by-step process using the Qualitative Analysis program to extract features from the MS data and MPP to filter and analyze the features to produce only the most relevant and significant. 2 Identify the compounds in SimLipid. 3 Use the preferred ion list derived from the first goal to run LC/MS/MS in Auto MS/MS mode on a few representative samples and then run a Find by Auto MS/MS analysis on the MS/MS data with the Qualitative Analysis program. 4 Confirm lipid compound identification with SimLipid using the MS/MS data set for the representative samples from the original sample set used to acquire the MS data. 11

12 Before You Begin Overview of the Workflows Figure 3 MS/MS ID Confirmation for MS Data Profiling Workflow Lipidomics workflows for MS/MS data Two workflows exist for MS/MS data: MS/MS Data Qualitative Workflow and the MS/MS Data Profiling Workflow. To reduce the number of compounds to be identified with SimLipid, the qualitative lipidomics workflow for MS/MS data uses either the MFE or Auto MS/MS function in the Qualitative Analysis program. The profiling lipidomics workflow for MS/MS data uses MFE to extract MS1 and MS2 features. You upload the MFE CEF files into SimLipid to identify the compounds before using MPP because at this time MPP strips the MS/MS information needed for the SimLipid search following MPP analysis. MS/MS Data Qualitative Workflow The first MS/MS lipidomics workflow MS/MS Data Qualitative Workflow is similar to that for MS Data. 12

13 Before You Begin Overview of the Workflows Figure 4 MS/MS Data Qualitative Workflow MS/MS Data Profiling Workflow This workflow is different in one important way from its MS counterpart. In order to find the compounds of greatest statistical significance for your experiment with MPP, you first search for and identify all the compounds in a file with SimLipid and export the data to an annotated CEF file, which you then import into MPP. Figure 5 MS/MS Data Profiling Workflow 13

14 Before You Begin Required Items Required Items See the Metabolomics Discovery Workflow Guide for a description of the required hardware and software, as well as optional software, for working with the lipidomics workflows. That guide also discusses compliance issues. The only additional software you need is SimLipid 3.3 or later. To follow the instructions in this guide you must have first purchased SimLipid 3.3 or later from PREMIER Biosoft International. You can purchase the software at Follow the installation instructions on the web site. SimLipid 3.3 is a lipids identification tool whose new features were specifically designed to accommodate Agilent CEF files. Although SimLipid 3.3 can also import data files for lipid identification from other companies, it has these advantages when working with Agilent files: Imports up to 100 data files containing compounds, rather than raw spectra, making the analysis more effective and efficient. Performs a neutral mass search at the MS1 level for MS/MS data Uses adducts in the MS1 level data for MS/MS identification Identifies lipids at any stage of the lipidomics discovery workflow Exports CEF files to be used in Agilent data analysis software, including Pathway Architect. 14

15 MS Data Lipidomics Workflows You can choose between three MS data lipidomics workflows: an MS data qualitative workflow, an MS data profiling workflow and an MS data profiling workflow with MS/MS confirmation of the MS data Sim- Lipid identification. A MS Data Lipidomics Workflows MS Data Qualitative Workflow MS Data Profiling Workflow With MS/MS ID Confirmation Find features in one file by MFE in MH Qual Find features in multiple files by MFE in MH Qual Do an MS Data Profiling Workflow preferred ion list Do an MS SimLipid ID search Find statistically significant features with MPP Find features by Auto MS/MS in MH Qual Confirm features by recursive analysis in MH Qual Do an MS SimLipid ID search Confirm MS IDs with an MS/MS SimLipid search Introduction 16 MS Data Qualitative Workflow 17 MS Data Profiling Workflow 28 MS Data Profiling Workflow with MS/MS ID Confirmation 42

16 MS Data Lipidomics Workflows Introduction Introduction Lipidomics is the process used to identify and quantify the lipids of a biological system in a specified state and is a branch of metabolomics. This guide is supplemental to the Metabolomics Discovery Workflow Guide, and shows all possible MS data workflows, as well as MS/MS data workflows, for lipid identification. This chapter presents the three MS data lipidomics workflows and shows how to do SimLipid ID searches with MS data. Where appropriate, the chapter refers to the Metabolomics Discovery Workflow Guide rather than redo instructions for the same set of actions. Three MS data lipidomics workflows For MS data you can use one or more of three lipidomics workflows, each of which includes lipid identification with SimLipid: MS Data Qualitative Workflow a simple workflow with two steps, finding compounds (called features) using MFE on one file in MassHunter Qualitative Analysis and subsequently identifying the compounds with SimLipid. MS Data Profiling Workflow a more complex workflow that includes two of the major steps in discovery lipidomics experiments, profiling and identification, for a set of files whose samples came from or were subjected to different conditions. The result files from the SimLipid identification can then be used by Pathway Architect for the third step of discovery lipidomics: interpretation, which relates the identification data with biological pathways. This guide does not cover the third step. Discovery lipidomics experiments use Mass Profiler Professional and involve examining an untargeted suite of lipids, finding the lipids with statistically significant variations in abundance within a set of experimental versus control samples, and answering questions related to causality and relationships. MS Data Profiling Workflow with MS/MS ID Confirmation a workflow that includes the MS Data Profiling Workflow, whose preferred ion list is used to acquire MS/MS data in Auto MS/MS mode on one sample or a few representative samples from the original sample set run to produce the MS data. You use the Find by Auto MS/MS function in the MassHunter Qualitative Analysis program to find the compounds in the MS/MS data and then identify them with SimLipid, ultimately comparing the identifications to those found with the MS data. 16

17 MS Data Lipidomics Workflows MS Data Qualitative Workflow Find features in one file by MFE in MH Qual Do an MS SimLipid ID search MS Data Qualitative Workflow The MS Data Qualitative Workflow includes two steps: Step 1: Find features in one file by MFE in the MassHunter Qualitative Analysis program Compounds, also known as molecular features, are extracted from your data based on mass spectral and chromatographic characteristics. The process is referred to as Molecular Feature Extraction (MFE). Molecular feature extraction quickly and automatically generates a complete, accurate list of your compounds, which includes molecular weight, retention time, m/z, and abundance. Since this guide emphasizes preparing data for lipid identification, this step shows you how to set up an MFE method specifically for lipids. This step includes these procedures: Create a method to Find Compounds by Molecular Feature. on page 17 Save your Find Compounds by Molecular Feature method. on page 20 Run the method. on page 20 Step 2: Do an MS SimLipid ID search SimLipid lets you easily search thousands of compounds in the MFE CEF file with its High Throughput Search. With SimLipid you can further restrict the search based on retention time, mass range, compound number, error tolerance, threshold, ion species, lipid category and lipid class. Step 1: Find features in one file by MFE in the MassHunter Qualitative Analysis program The settings for the example lipid MS Data method vary slightly from those given for the example data method used in the Metabolomics Discovery Workflow Guide. Agilent suggests you use the settings below only as a starting point and then optimize them for your set of data. 1. Create a method to Find Compounds by Molecular Feature. a Start MassHunter Qualitative Analysis. 1. Click Cancel in the Open Data File dialog box to start MassHunter Qualitative Analysis without opening any data files. You do not need to open a data file at this time. Data files may be opened later by clicking File > Open Data File. 2. Enable advanced parameters in the user interface. See the Metabolomics Discovery Workflow guide for detailed instructions. b Open the Find Compounds by Molecular Feature section in the Method Editor window. 1. In the Method Explorer, click Find Compounds. 2. Click Find by Molecular Feature. c Edit the parameters on each tab. 17

18 MS Data Lipidomics Workflows MS Data Qualitative Workflow 1. Click the Extraction tab, and for each parameter take the action described in Table 1. Table 1 Extraction Parameters For This Parameter: Target data type Input data range Peak filters Take This Action: Select Small molecules. Clear check boxes. Click Use peaks with height and type Click the Ion Species tab, and for each parameter take the action described in Table 2. Table 2 Ion Species Parameters For This Parameter: Positive ions Negative ions Neutral losses Salt dominated positive ions Take This Action: Mark +H, +Na, +NH4. Mark -H, +CH3COO. Clear check boxes. Clear check box. 3. Click the Charge State tab, and for each parameter take the action described in Table 3. Table 3 Charge State Parameters For This Parameter: Take This Action: Peak spacing tolerance Type m/z and 7.0 ppm. Isotope model Select Common organic molecules. Limit assigned charge states... Mark check box and type 1. Treat ions with unassigned... Clear check box. 4. Click the Compound Filters tab, and for each parameter take the action described in Table 4. Table 4 Compound Filters Parameters For This Parameter: Relative height Absolute height Restrict retention times to Take This Action: Clear check box. Mark check box and type Clear check box. Restrict charge states to Mark check box and type 1. 18

19 MS Data Lipidomics Workflows MS Data Qualitative Workflow 5. Click the Mass Filters tab, and for each parameter take the action described in Table 5. Table 5 Mass Filters Parameters For This Parameter: Filter mass list Take This Action: Clear check box unless you intend to remove known neutral masses from the data set. 6. Click the Mass Defect tab, and for each parameter take the action described in Table 6. Table 6 Mass Defect Parameters For This Parameter: Filter results on mass defect Take This Action: Clear check box unless you want the natural mass defect range to increase with increasing mass. If you mark the check box, select Variable. 7. Click the Results tab, and for each parameter take the action described in Table 7. Table 7 Results Parameters For This Parameter: Delete previous compounds Highlight first compound Chromatograms and spectra Display only the largest Take This Action: Mark check box. Click radio button. Mark Extract MFE spectrum and Extract ECC. Clear check box. 8. Click the Advanced tab, and for each parameter take the action described in Table 8. Table 8 Advanced Parameters For This Parameter: Compound ion count threshold Compounds with indeterminate neutral mass Take This Action: Click Include all. Click Exclude. d Set the Export CEF options. 1. From Method Explorer, click Export. 2. Click CEF Options. 3. In the Method Editor window, click At the location of the data file. 4. Click Auto-generate new export file name. 19

20 MS Data Lipidomics Workflows 2. Save your Find Compounds by Molecular Feature method. MS Data Qualitative Workflow After you have edited your method to find compounds by molecular feature (MFE), it is recommended you save the method using a name that is readily distinguished from the names that you will use later in the next two workflows. Distinct methods let you readily process your data, whether MS data or MS/MS data, without having to edit the workflow actions every time you switch between workflows. a Click Method > Save As. b Select the folder and type a method name in the Save Method dialog box. It is recommended to add the text MFE QUAL at the end of your file name to distinguish it from the file names of the methods created in the next two workflows. c Click Save. 3. Run the method. Follow the instructions in the section, Confirm the MFE method on a single data file, of the Find Features chapter in the Metabolomics Discovery Workflow Guide. In this case, you are not confirming the method; you are actually running it on a single file. Therefore, step 4, Export the results for the single sample to a CEF file (optional), in that section is not optional. You must export the results for the single sample to a CEF file. Step 2: Do an MS SimLipid ID search For more instructions on how to use SimLipid with Agilent data, see the SimLipid online help. 1. Import the Agilent MS MFE CEF file into SimLipid. With SimLipid 3.3 you can import up to 100 Agilent MS MFE CEF files, although in this step you import only one file. a Create a project 1. Select File > New > Project or click the New Project icon, 2. Enter a project name. 3. Click Create. b Select the exported MS MFE CEF file., from the tool bar. 1. Click the Open Agilent CEF format icon,. 2. Select Import up to 3 files. The Open Agilent peak list files dialog box appears. Note: When you click to open this dialog box or a different folder, you may have to wait a moment for the server to act on the communication. 3. Select the file and click Open. The Load Compounds dialog box appears. c Specify data filters. 1. Click Specify Range. The Specify Range dialog box appears with a series of filters you can use to reduce the number of compounds for identification. 20

21 MS Data Lipidomics Workflows MS Data Qualitative Workflow 2. Specify the filters to reduce the data set subject to the MS Search. Note: The m/z Filter applies only to MS/MS data containing MS1 level data. Figure 6 Specify Range dialog box 3. Click OK. The Load Compounds dialog box now appears with only those compounds marked that meet the values entered in the Specify Range dialog box. Figure 7 Load Compounds dialog box 4. Click Load Selection. 21

22 MS Data Lipidomics Workflows 2. Do a high-throughput MS search on multiple compounds. MS Data Qualitative Workflow Even though you can search multiple compounds, you cannot, however, search more than 1000 compounds at a time. a Select the file. 1. Click the High Throughput Search icon,, and select Agilent Data. Figure 8 File Selection dialog box for High Throughput Lipid Search 2. Select the file for the search. 3. Click Specify Range to choose the filters for the data, if necessary, and click OK. 4. Click OK. The High Throughput Lipid Search dialog box appears, where you can select compounds and enter search parameters. b Select compounds. 1. Mark the check box at the top of the compound list. All compounds will be selected unless there are more than 1000; then only the first 1000 will be selected, or select each compound individually. 2. Click Specify Range or Edit to filter the compounds that you selected. c Enter search parameters. 1. Enter values for Error Tolerance and Threshold Intensity. 1-5 ppm for the Error Tolerance is reasonable. 2. Mark check boxes for Positive Mode and Negative Mode ion species. 3. Mark check boxes for Lipid Category and Lipid Class. 4. For Agilent data, do not mark Search based on specified ion species by ignoring all the information available in the files. Agilent MS data contains ion species information that SimLipid can use for the search. Substituting this data with the results of a non-specific algorithm can greatly increase the false positives. 22

23 MS Data Lipidomics Workflows MS Data Qualitative Workflow Figure 9 High Throughput Search window d Do the search and review results. 1. Click Search. A message appears labelling the results with a number, #. 2. Click the HTP Load Request # icon,, or click View > Load High Throughput Search Results > Load HTP_Request #. A message log appears containing the number of identified compounds and a list of compounds whose search yielded no results. Figure 10 Message Log after a High Throughput Search 3. Click OK. 4. When the message, Results exported successfully appears, click OK. e Generate a high-throughput report Sometimes it is difficult to find compounds with results when there are a large number of compounds and only a few identified. Generating a report can help you find the compounds with results. 23

24 MS Data Lipidomics Workflows MS Data Qualitative Workflow 1. Click the Generate a High Throughput Report icon,. Figure 11 File Selection dialog box for generating a High Throughput Report 2. Select the file on which to report. 3. Click OK. The Generate High Throughput Report dialog box appears with the MS tab already selected. 4. Select the compounds on which to report. 5. Click Select All so you can choose the search hit with the best delta mass. 6. Select the columns for the report and the format for the report, Microsoft Excel, csv or html. Figure 12 Generate High Throughput Report dialog box 7. Click OK. 24

25 MS Data Lipidomics Workflows MS Data Qualitative Workflow The report opens in the format you have chosen, in this case, Microsoft Excel. Figure 13 Microsoft Excel report (as pdf) for 3 identified sphingolipids Note: The report, HTPReport_#, where # is the number of the request, appears under the \\Program Files\SimLipid 3.3\Output folder. 3. Repeat step 2 until all compounds in the file are searched. Every time you generate a report for 1000 compounds, the results are added to the previous results, and all appear in the most recent report. The final CEF file contains all the results, which you can then export. You can choose to search all the compounds first before generating a report. 4. Export the results to a CEF file. a Click the Export Results to Agilent CEF file icon,. b Select the file for export and the folder for the results and click OK. If you do not change the default folder, Windows Explorer appears with the Output folder open, which contains the CEF file(s). The file automatically retains the name of the MS MFE.cef file you imported. SimLipid automatically writes the lipid match with the lowest match error to the CEF file. Do an MS Search on a single compound (optional). You always have the option of doing an MS search on a single compound instead of the high throughput search. a Do the search. 1. Expand the file in the Navigation pane to see the compounds. 25

26 MS Data Lipidomics Workflows MS Data Qualitative Workflow Figure 14 SimLipid compound navigation 2. Select a compound and click the MS Search icon,. 3. Change the MS Search parameters from the default, if necessary. Figure 15 MS Search dialog box 4. Click Search. The results appear automatically under the Search Results tab. Figure 16 Search results for a single compound b Review the results. To reduce the number of displayed hits, click Show distinct Chemical Composition or Show distinct Experimental m/z. 26

27 MS Data Lipidomics Workflows MS Data Qualitative Workflow To see the structure for each lipid, click Lipid Structure. c Generate a report for a single compound. 1. Click the Export Results icon,. or click Export Selected Results. 2. Select the lipids to report on. 3. Select the lipid properties to report. 4. Click Browse to select the folder for the report and rename the.html to a name of your choosing. 5. Click Save. 6. Click OK when the message Results exported successfully appears. Because you can use Export Results to report on only one compound, use Generate High Throughput Report to include all the compounds on which you have run MS Search. d Repeat steps a-c for each compound you intend to search. e Export results to a CEF file. Each compound on which you did an MS Search is included in the CEF file. 1. Click the Export Results to Agilent CEF file icon,. 2. Select the file for export and the folder for the results and click OK. If you do not change the default folder, Windows Explorer appears with the Output folder open, which contains the CEF file(s). The file automatically retains the name of the MS MFE.cef file you imported. Next workflow... You have now completed the first MS data lipidomics workflow with SimLipid identification. In the next workflow you Find Compounds by Molecular Feature (MFE) in multiple files and import the results into Mass Profiler Professional to find the most significant features for the purposes of your experiment. 27

28 MS Data Lipidomics Workflows MS Data Profiling Workflow Find features in multiple files by MFE in MH Qual Find statistically significant features with MPP MS Data Profiling Workflow This MS data lipidomics workflow includes three steps, the first of which is the same first step as the MS Data Qualitative Workflow but done with multiple files. Step 1. Find features in multiple files by MFE in the MassHunter Qualitative Analysis program Step 2. Find statistically significant features with MPP This step reduces the number of compounds to only those that are significant to identify for your profiling experiment. After you create a project and experiment, the Experiment Creation Wizard guides you through the necessary steps to organize your experiment, import your MS data, define your experimental variables, and prepare your data for analysis. The data preparation includes filtering, alignment, normalization, and baselining. Confirm features by recursive analysis in MH Qual Do an MS SimLipid ID search To further prepare the data for lipid identification, you use additional filtering techniques and an initial differential expression analysis found in the Significance Testing and Fold Change wizard. You can also do recursive feature finding with the MassHunter Qualitative Analysis program and/or an advanced analysis before using SimLipid. Agilent recommends you go through as many steps of this lipidomics workflow with your MS data as you intend before identifying your lipids. Step 2 includes these procedures: Create a new experiment. on page 29 Do the differential analysis. on page 32 Export the data for identification. on page 39 Save your project. on page 40 Step 3. Do an MS SimLipid ID search Step 2 produces a single CEF file. Note: Help and detailed information regarding the various parameters and statistical treatments are available at anytime by pressing the F1 key on the keyboard or referring to the Metabolomics Discovery Workflow Guide or the Mass Profiler Professional User Manual. Features of example MS data This guide takes you through the steps for each of the MS data profiling workflows using example data, which includes these samples: 8 human serum control samples 4 positive ESI and 4 negative ESI 8 in disease state #1 4 positive ESI and 4 negative ESI 8 in disease state #2 4 positive ESI and 4 negative ESI To illustrate this workflow, this guide uses the positive ESI samples. Producing a complete profiling experiment would include using the negative ESI samples in a separate MPP experiment, but the instructions would be the same as for the positive ESI half of the profiling experiment. This guide therefore lets you do the negative ESI half with your own data, if appropriate. 28

29 MS Data Lipidomics Workflows MS Data Profiling Workflow Step 1: Find features in multiple files by MFE in the MassHunter Qualitative Analysis program Follow the instructions in the MS Data Qualitative Workflow for Step 1: Find features in one file by MFE in the MassHunter Qualitative Analysis program on page 17 but with the following changes: Enable the method to run in MassHunter DA Reprocessor through Worklist Actions. See the Metabolomics Discovery Workflow Guide for the instructions. Save the method with MFE Profiling at the end of the name to distinguish it from the MFE methods set up in the other workflows. If you have both positive ESI and negative ESI files, run all the files simultaneously. Find compounds using DA Reprocessor on the files set up for your discovery experiment. See the Metabolomics Discovery Workflow Guide for the instructions. Step 2: Find statistically significant features with MPP First, you create a project and experiment in Mass Profiler Professional (MPP) just as you would at the beginning of the metabolomics workflow. Then you import, filter, align, normalize and baseline the data with the Experiment Creation wizard. After you create and organize an experiment, you do an initial differential analysis with the Significance Testing and Fold Change wizard. This involves filtering your data and doing some initial statistical analysis in order to reduce the number of compounds to those that are the most significant for your discovery experiment. This guide uses the example MS data described in Features of example MS data on page 28. The figures you see represent the setup and analysis of a discovery experiment intended to find the lipids that reflect the differences between human serum samples in Disease State 1, Disease State 2 and a set of controls. If you have both positive and negative ESI files, do this step first with the positive ESI data. Do the same for the negative ESI data. Then export the data to two corresponding CEF files for SimLipid identification. See the Metabolomics Discovery Workflow Guide for more detailed instructions. 1. Create a new experiment. a Start Agilent Mass Profiler Professional. Double-click the Mass Profiler Professional icon located on the desktop. or Click Start > All Programs > Agilent > MassHunter Workstation > Mass Profiler Professional > Mass Profiler Professional. b Create a new project. c Create a new experiment. Use the same settings as are in the Metabolomics Discovery Workflow Guide: Experiment type -- Unidentified Workflow type -- Analysis: Significance Testing and Fold Change 29

30 MS Data Lipidomics Workflows MS Data Profiling Workflow After you create and name the experiment, the Experiment Creation Wizard appears. Remember to click Next when you want to move to the next step or page in the Wizard. 2. Import the data with the Experiment Creation Wizard. a Select the data source and organism (Step 1 of 11). b Select the sample data to import (Step 2 of 11). First select the Positive ESI files to import. c Review and order the selected files (Step 5 of 11). Figure 17 Imported and sorted data files 3. Group the samples with the Experiment Creation Wizard. a Group the samples based on the independent variables and replicate structure of your experiment (Step 6 of 11). Your sample grouping is determined by your experiment definition. Review the section Define an Experiment in the Metabolomics Discovery Workflow Guide for an overview of a discovery experiment. 1. Add any new parameter for your experiment. 2. Assign at least two values to the parameter. 3. Click Next. 30

31 MS Data Lipidomics Workflows MS Data Profiling Workflow Figure 18 Experiment Grouping Note: When entering Parameter Names and parameter Assign Values, it is very important that the entries use identical letters, numbers, punctuation, and case in order for the Experiment Grouping to function properly. 4. Filter, align and normalize the sample data. For each of the steps below, follow the instructions under the Filter, align, and normalize the sample data in the Metabolomics Discovery Workflow Guide, using the same parameter values. a Select and enter the data filter parameters (Step 7 of 11). Filtering during the experiment creation process may be used to reject low-intensity data or restrict the range of data. After data is imported, several filtering options may be applied: Abundance, Retention Time, Mass, Flags, Number of ions, Mass and Minimum Quality Score. b Select and enter the retention time and mass alignment parameters (Step 8 of 11). Unidentified compounds from different samples are aligned or grouped together if their retention times are within the specified tolerance window and the mass spectral similarity, as determined by a simple dot product calculation, is above the specified level. c View and review the compounds present and absent in each sample (Step 9 of 11). 31

32 MS Data Lipidomics Workflows MS Data Profiling Workflow This step lets you review a summary of the compounds present and absent in each of the samples based on the experiment parameters, including the application of the filter and alignment parameters. d Select whether to normalize the data. (Step 10 of 11). Normalizing the data reduces the variability caused by sample preparation and instrument response. From the list of compounds present in all of the samples you may pick one as an internal standard. No internal or external standard is selected at this time. e Select whether to compare features in each sample to the response of the features across multiple samples (Step 11 of 11). Baselining is a technique used to view and compare data that involves converting the original data values to values that are expressed as changes in the data values relative to a calculated statistical value derived from the data. The calculated statistical value is referred to as the baseline. 5. Do the differential analysis. The Significance Testing and Fold Change Wizard immediately starts after the Experiment Creation Wizard if you selected Analysis: Significance Testing and Fold Change for the Workflow type in the New Experiment dialog box. Figure 19 Summary Report profile plot of the one-variable experiment example showing 24 samples The Significance Testing and Fold Change workflow helps you create an initial differential expression from your data and identify the most significant features from among all of the features previously found using molecular feature extraction. The 32

33 MS Data Lipidomics Workflows MS Data Profiling Workflow steps necessary to create your initial differential expression are predetermined and based on the experiment type, experiment grouping, and conditions you enter when creating your project and setting up your experiment. The workflow displays the sequence of steps on the left-hand side navigator with the current step highlighted (Figure 19). Some steps may be automatically skipped for your experiment. In the case of our example, the experiment requires steps 3-7 to produce a list of the most significant compounds to be identified. Note: Each step filters out more compounds that are not relevant to the experiment. The number of compounds decreases from about 330,000 in 24 samples to only 1403 that have to be identified. a Review the summary report (Step 1 of 8). You do not enter analysis parameters at this step. To review the data, change the plot view, export selected data, or export the plot to a file, click and right-click features available on the plot. Remember to click Next when you want to move to the next step or page in the Wizard. b Skip the Experiment Grouping step (Step 2 of 8). Your data is presented with the experiment grouping you entered in Group the samples with the Experiment Creation Wizard. on page 30. In this step you have an opportunity to change your experiment grouping but will not do so at this time. When you click Next at this step going forward, each new page is the result of an algorithm operating with default values to produce the plot and data on the page. After you click Next, the Filter Flags page appears (Figure 20). Figure 20 Filter Flags when it first appears with the default calculation 33

34 MS Data Lipidomics Workflows MS Data Profiling Workflow A major objective of Filter Flags is to remove one-hit wonders from further consideration. A one-hit wonder is an entity that appears in only one sample, is absent from the replicate samples, and does not provide any utility for statistical analysis. With default values the Filter Flags algorithm reduced the number of compounds relevant to the experiment from about 330,000 to 42,992. c Click Re-run Filter to change the Filter Flags default parameter values, if necessary (Step 3 of 8). In the example case, the X value for Retain entities in which at least X out of 24 samples have acceptable values was changed from 1 to 2. When applied, the program reduced the number of compounds to 24,997 (Figure 21). Figure 21 Filter Flags after changing the Re-run Filter parameter value 34

35 MS Data Lipidomics Workflows MS Data Profiling Workflow After you click Next, the Filter By Frequency page appears (Figure 22). Note: Using the default values, the Filter By Frequency algorithm reduced the relevant compounds for the experiment to Figure 22 Filter by Frequency d Click Re-run Filter to change the Filter By Frequency default parameter values (Step 4 of 8). Filter By Frequency is set by default to retain the entities that appear in at least 100% of all the samples in at least one condition. This is the recommended percentage for experiments that contain five or fewer replicates. Even though the 24 samples in the example case contain 8 replicates, the percentage is kept the same. 35

36 MS Data Lipidomics Workflows MS Data Profiling Workflow After you click Next, the QC on samples page appears (Figure 23) with the results of the Principal Component Analysis (PCA). Figure 23 QC on samples: PCA Score showing the initial separation of the different disease states and the controls e Review the sample quality in QC on samples (Step 5 of 8). A detailed description of this page of the wizard and of Principal Component Analysis is given in the Metabolomics Discovery Workflow Guide. 36

37 MS Data Lipidomics Workflows MS Data Profiling Workflow After you click Next, the Significance Analysis page appears (Figure 24). Figure 24 Significance analysis based on a 1-way ANOVA using the one-variable experiment example. A 1-way ANOVA significance analysis does not present a graphical representation of the entities relationships. f Assess the differential Significance Analysis (Step 6 of 8). The entities are filtered based on their p-values calculated from a statistical analysis that is selected based on the samples and experiment grouping. To pass a larger number of entities, change the position of the slider to increase the p- value. The statistical analysis is either a T-test or an Analysis of Variance (ANOVA) based on the samples and experiment grouping. The statistical analysis applied is described in section 4.7 Significance Analysis in the Mass Profiler Professional User Manual. In our example case, the analysis is a one-way ANOVA. For a detailed definition of p-value, see p-value on page 60 in this guide. For a detailed description of this wizard page, see the Metabolomics Discovery Workflow Guide. Note: For this example, only 1673 of the 4512 entities remaining satisfied a corrected p-value cut-off of

38 MS Data Lipidomics Workflows MS Data Profiling Workflow After you click Next, the Fold Change page appears with the results from the Fold Change algorithm (Figure 25). Figure 25 Fold Change for 2X Control Intensities g Re-calculate the Fold Change, if necessary (Step 7 of 8). For our one-variable example case, the program finds 1403 compounds whose intensities are 2X greater than those in the controls. ID Browser is skipped by the Significance Analysis and Fold Change wizard. You use SimLipid instead. h Click Finish. 38

39 MS Data Lipidomics Workflows MS Data Profiling Workflow The advanced workflow mode appears, letting you have access to all features available in Mass Profiler Professional through the Workflow Browser. Figure 26 shows the Mass Profiler Professional Navigator on the left with the current project and experiment entities, as well as the Workflow Browser on the right. Figure 26 The main functional areas of Mass Profiler Professional Refer to the Metabolomics Discovery Workflow Guide for further information about the layout. 6. Export the data for identification. You are now ready to export the MPP data to a CEF file for SimLipid identification. See Figure 26. a In the Workflow Browser, click Results Interpretation. b Select Export for Identification. c In the Export dialog box, click Choose and select the Fold Change entity. d Click Browse and select a folder that can contain the CEF file that you are creating, separate from the CEF files that were generated by MFE. You may have to create a new folder. e Name the CEF file and click Save. 39

40 MS Data Lipidomics Workflows MS Data Profiling Workflow Figure 27 Export (to CEF) dialog box The Export dialog box should now look like Figure 27. f Click OK. Note: MPP has reduced the original 24 files of about 330,000 compounds to one file with 1403 compounds for identification. 7. Set up a second experiment for negative ESI files, if you have them. Follow the instructions for the previous steps 1-6 for the negative ESI files. You should then have two CEF files, one for the positive ESI data, and one for the negative ESI data. 8. Save your project. Save your current analysis as a TAR file for archiving, restoration of any future analysis to the current results, sharing the data with a collaborator, or sharing the data with Agilent customer support. a Click Project > Export Project. Figure 28 Menu selection to export your current analysis b Mark the check box next to the experiment you wish to save. Figure 29 c Click OK. Choose Experiments dialog box for saving your experiment 40

41 MS Data Lipidomics Workflows MS Data Profiling Workflow d Select or create the file folder. e Type File name. f Click Save. Step 3. Do an MS SimLipid ID search Follow the instructions in the MS Data Qualitative Workflow in Step 2: Do an MS SimLipid ID search on page 20 and be sure to do this: Create and name a new project and import the MPP CEF file to it. SimLipid searches in the same way for either an MFE CEF file or an MPP CEF file. Next workflow... You have now completed the second MS data lipidomics workflow. In the next workflow you do the MS Data Profiling Workflow and confirm the lipid IDs using SimLipid with MS/MS data. 41

42 MS Data Lipidomics Workflows MS Data Profiling Workflow with MS/MS ID Confirmation Do an MS Data Profiling Workflow preferred ion list Find features by Auto MS/MS in MH Qual Confirm MS IDs with an MS/MS SimLipid search MS Data Profiling Workflow with MS/MS ID Confirmation This is the third MS lipidomics workflow with SimLipid identification. It comprises the MS Data Profiling Workflow with an MS/MS ID confirmation of the compounds identified using the MS Data Profiling Workflow with SimLipid identification. Step 1. Do an MS Data Profiling Workflow From this workflow you derive a preferred ion list to use in Step 2. Step 2. Find features by Auto MS/MS in the MassHunter Qualitative Analysis program With a preferred ion list derived from the MS MPP results and SimLipid identification results, you can do an LC/MS/MS acquisition on a few representative samples of the original sample set, to produce MS/MS data for auto/targeted feature finding in MassHunter Qualitative Analysis. Finding features with MS/MS data leads to improvement in the accuracy of your compound identification. Because you are confirming the identities of relevant compounds found in all the MS data samples for your discovery experiment, you need only run the LC/MS/ MS on one sample or a few representative samples of the set. Step 3. Confirm MS IDs with an MS/MS SimLipid search This step gives you instructions for importing the Auto MS/MS CEF files into SimLipid, and identifying the compounds with SimLipid to confirm the identification of those found for the MS data and to identify lipids you could not identify before. Step 1: Do an MS Data Profiling Workflow Follow the instructions in MS Data Profiling Workflow on page 28. Step 2. Find features by Auto MS/MS in the MassHunter Qualitative Analysis program Because the MS/MS data is acquired using a preferred ion list derived from an analysis of the MS data and in Auto MS/MS mode, only MS/MS spectra need accompany the feature information. For this reason Find by Auto MS/MS feature finding is sufficient. 1. Create an Auto MS/MS method. a Start MassHunter Qualitative Analysis. 1. Click Cancel in the Open Data File dialog box to start MassHunter Qualitative Analysis without opening any data files. You do not need to open a data file at this time. Data files may be opened later by clicking File > Open Data File. 2. Enable advanced parameters in the user interface. b Open the Auto MS/MS Feature Find section of the Method Editor. In Method Explorer under Find Compounds, click Find by Auto MS/MS. c Edit the parameters on each tab. 42

43 MS Data Lipidomics Workflows MS Data Profiling Workflow with MS/MS ID Confirmation 1. Click the Processing tab and for each parameter take the action described in Table 9. Table 9 Processing parameters For This Parameter: Take This Action: Retention time window Type Positive and Negative MS/MS TIC thresh. Type Mass match tolerance Type Negative ions Limit to the largest compounds Filter results by fragments Compounds with same precursor m/z occurring at different times: Same as parameter above Mark -H, +CH3COO. Clear check box. Clear check box. For Remove if there are more than, type 15. For Except when the TIC exceeds, type Click the Excluded Masses tab and for each parameter take the action described in Table 10. Table 10 Excluded Masses parameters For This Parameter: Click Exclude masses (or m/z ranges) from all new chromatograms Single m/z expansion for this chromatogram Take This Action: Type these m/z value(s): , Click Symmetric (ppm); type +/ Click the Results tab and for each parameter take the action described in Table 11. Table 11 Results parameters For This Parameter: Delete previous compounds Highlight first compound Chromatograms and spectra Extract separate MS/MS spectrum per collision energy Take This Action: Mark check box. Click radio button. Mark Extract MS/MS. Click this option. 2. Save your Find Compounds by Auto MS/MS method. After you have edited your method to find compounds by Auto MS/MS, it is recommended you save the method using a name that is readily distinguished from the names that you use in the other workflows. Distinct methods let you readily process your data, whether MS data or MS/MS data, without having to edit the workflow actions every time you switch between running MFE and Auto MS/MS in the worklist. a Click the Method > Save As command. 43

44 MS Data Lipidomics Workflows MS Data Profiling Workflow with MS/MS ID Confirmation b Select the folder and type a method name in the Save Method dialog box. It is recommended to add the text Auto MS/MS at the end of your file name to distinguish it from the file names that are recommended in the other workflows. c Click the Save button. 3. Run the method. Follow the instructions for Run the method. on page 20. Step 3. Confirm MS data IDs by using SimLipid on MS/ MS data With SimLipid 3.3 you can import up to 100 Agilent MS/MS MFE CEF files, do searches on the MS/MS data and then export the modified CEF files to MPP for more analysis. 1. Import Agilent MS/MS Data into SimLipid. a Set up a new project. 1. Select File > New > Project or click the New Project icon,, from the tool bar. 2. Enter a project name, such as Auto MS/MS Confirmation. 3. Click Create. b Open your one or few CEF files. Follow the instructions for Select the exported MS MFE CEF file. on page 20 but use MS/MS filters. 2. Do a high-throughput MS/ MS search on multiple compounds. Follow the instructions in Do a high-throughput MS search on multiple compounds. on page 22. The instructions for a high-throughput MS/MS search are almost the same with these differences: The High Throughput Lipid Search dialog box has MS & MS/MS selected. Because the Auto MS/MS or data dependent MS/MS files contain little or no MS1 level data, or ion species information, the searches are more likely to be effective if you select the adduct you think should be present in the category of lipids you are attempting to identify. If you are doing a general search and need to specify more than one ion species, mark the Search based on specified ion species by ignoring all the information available in the files check box. If the charge state of the precursor ion is 1, you can ignore any fragment ions above the m/z of the precursor ion and mark the Match Fragment Ions up to Precursor Ion m/z only check box. If the charge state of the precursor ion is 2 or greater and the charge state of the fragment ions is less than that of the precursor ion, do not mark the check box. 44

45 MS Data Lipidomics Workflows MS Data Profiling Workflow with MS/MS ID Confirmation Figure 30 High Throughput Lipid Search for MS/MS data Click the MS/MS tab on the Generate High Throughput Report dialog box and select the lipid and output options. Figure 31 Generate High Throughput Report for MS/MS Data 45

46 MS Data Lipidomics Workflows Do an MS/MS Search on a single compound (optional) MS Data Profiling Workflow with MS/MS ID Confirmation You always have the option of doing an MS/MS search on a single compound rather than a high throughput search. Follow the instructions for Do an MS Search on a single compound (optional). on page 25. They are almost the same instructions for the MS/MS search on a single compound with these changes: Click the MS/MS Search icon,. The MS/MS Search dialog box appears. For the additional fields, mark the radio buttons and enter appropriate values or mark the check box: Precursor Ion Error Tolerance Charge State Match Fragment Ions up to Precursor Ion m/z only Search based on specified ion species by ignoring all the information available in the files See Do a high-throughput MS/MS search on multiple compounds. on page 44 for explanations of the fields. Figure 32 MS/MS Search dialog box 46

47 MS/MS Data Lipidomics Workflows You can choose between two lipidomics workflows for MS/MS data: an MS/MS data qualitative workflow and an MS/MS data profiling workflow. B MS/MS Data Lipidomics Workflows MS/MS Data Qualitative Workflow MS/MS Data Profiling Workflow Find features in one file by MFE or Auto MS/MS in MH Qual Find features in multiple files by MFE in MH Qual Do an MS/MS SimLipid ID search Do an MS/MS SimLipid ID search Find statistically significant features with MPP Introduction 48 MS/MS Data Qualitative Workflow 49 MS/MS Data Profiling Workflow 51

48 MS/MS Data Lipidomics Workflows Introduction Introduction Use these workflows if you only have access to a Q-TOF or MS/MS data. The two workflows described in this section are similar to the first two workflows for MS data, but the MS data and MS/MS data profiling workflows have some important differences. Two lipidomics workflows with SimLipid identification For MS/MS data you can use one or both of the two lipidomics workflows, each of which includes lipid identification with SimLipid: MS/MS Qualitative Workflow a simple workflow with two steps, finding compounds (called features) using MFE or Auto MS/MS (if the data was acquired in Auto MS/MS mode) in MassHunter Qualitative Analysis and subsequently identifying the compounds with SimLipid MS/MS Data Profiling Workflow a more complex workflow that includes a modified profiling workflow, where annotated CEF files from SimLipid, not MFE CEF files, are imported into MPP for statistical analysis. This section shows you how to search and identify the compounds using SimLipid and how to export the results to annotated CEF files, which you use in MPP to further reduce the number of compounds to the most significant for the experiment. This guide refers to the Metabolomics Discovery Workflow Guide rather than redo steps for the same set of actions. 48

49 MS/MS Data Lipidomics Workflows MS/MS Data Qualitative Workflow Find features in one file by MFE or Auto MS/MS in MH Qual Do an MS/MS SimLipid ID search MS/MS Data Qualitative Workflow This is the first MS/MS lipidomics workflow with SimLipid identification. With this workflow you can quickly determine if a particular lipid category or class is present in the MS/MS data. Step 1: Find features in one file by MFE or Auto MS/MS in the MassHunter Qualitative Analysis program This step is almost identical to step 1 of the MS Data Qualitative workflow except you enter a value for a parameter you need only for MS/MS data. Since this guide emphasizes preparing data for lipid identification, this step shows you how to set up an MFE method specifically for lipids. Step 2: Do an MS/MS lipid ID search You can do an MS/MS search on MS/MS data with MS1 level spectra and MS2 level spectra, or you can perform a search with just MS/MS spectra. If you do a search with no profiling first, you may have to identify thousands of compounds to find the ones you seek. Step 1: Find features in one file by MFE or Auto MS/MS in the MassHunter Qualitative Analysis program Follow the instructions in Step 1: Find features in one file by MFE in the Mass- Hunter Qualitative Analysis program on page 17 but with these changes: In Method Editor: Find Compounds by Molecular Feature, click the Peak Filters (MS/MS) tab and mark the check box for Relative Height >/= % of largest peak. Type 10 for the percentage. Under the Results tab, make sure you mark the Extract MFE Spectrum and Extract MS/MS Spectrum check boxes. Save the method to a name that has MS/MS Qual at the end to distinguish it from those in the other workflows. If you have both positive ESI and negative ESI files, find features by MFE for both at the same time. Or, if you have Auto MS/MS data, follow the instructions in Step 2. Find features by Auto MS/MS in the MassHunter Qualitative Analysis program on page 42. Step 2: Do an MS/MS SimLipid ID search Follow the instructions for Step 3. Confirm MS data IDs by using SimLipid on MS/ MS data on page 44 but with this change: For MFE data, do not mark the Search based on specified ion species by ignoring all the information available in the files check box. Next workflow... You have now completed the first MS/MS data lipidomics workflow with SimLipid identification. In the next workflow you do a profiling workflow on MS/MS data but in reverse order. After doing a molecular feature extraction, you search the multiple files with SimLipid and import the SimLipid results into Mass Profiler Professional to 49

50 MS/MS Data Lipidomics Workflows MS/MS Data Qualitative Workflow ultimately filter and analyze the features to reduce the set to the most relevant and significant ones. 50

51 MS/MS Data Lipidomics Workflows MS/MS Data Profiling Workflow Find features in multiple files by MFE in MH Qual MS/MS Data Profiling Workflow This is the second MS/MS data lipidomics workflow with SimLipid identification. Step 1. Find features in multiple files by MFE in the MassHunter Qualitative Analysis program This step is virtually identical to the first step in the MS/MS Data Qualitative Workflow except you use DA Reprocessor to run multiple files. Step 2. Do an MS/MS SimLipid ID search Do an MS/MS SimLipid ID search At present you must do this step prior to working with MPP because MPP strips out the MS/MS compound information from the MFE CEF file so that it is useless for identification, but MPP can use the annotated MS/MS information after identification for its analysis algorithms. Step 3. Find statistically significant features with MPP Find statistically significant features with MPP After you import the annotated CEF files you can set up an experiment and do statistical analyses on the CEF files to reduce the number of compounds to only those that are significant for the profiling experiment. Because you have already annotated the compounds, you do not need to do another lipid identification. Step 1. Find features in multiple files by MFE in the MassHunter Qualitative Analysis program. Follow the instructions in Step 1: Find features in multiple files by MFE in the Mass- Hunter Qualitative Analysis program on page 29 but with these additional changes: In Method Editor: Find Compounds by Molecular Feature, click the Peak Filters tab and mark the check box for Relative Height >/= % of largest peak. Type 10 for the percentage. Make sure the Extract MFE Spectrum and Extract MS/MS Spectrum check boxes are marked. Save the method to a name that has MS/MS Profiling at the end to distinguish it from those in the other workflows. Step 2. Do an MS/MS lipid ID search. At this time you cannot use MS/MS MFE CEF files directly in Mass Profiler Professional and then identify the results. You must first identify all the compounds in the MS/MS MFE CEF files with SimLipid and then take the annotated CEF files into MPP. Follow the instructions in Step 3. Confirm MS data IDs by using SimLipid on MS/ MS data on page 44 but for multiple files and with these changes: Open your CEF files using batch mode 1. Click the Open Agilent CEF format icon,. 2. If you intend to run searches on more than 3 files, select Import files in Batch Mode. The Open Agilent peak list files dialog box appears. 51

52 MS/MS Data Lipidomics Workflows MS/MS Data Profiling Workflow Note: When you click to open this dialog box or a different folder, you may have to wait a moment for the server to act on the communication. 3. Select the first file, press Shift and select the contiguous files, then click Open. If the files are not contiguous, press Ctrl and select the files. The Specify Range dialog box appears with a series of filters you can use to reduce the number of compounds for identification. Specify data filters 1. Click MS/MS Data. 2. Specify the ranges and filters to reduce the data set subject to the MS/MS Search. Figure 33 Specify Range dialog box 3. Click OK. The files are now loaded into the project. Do a high throughput search on the first 1000 compounds for each file. Do not mark the Search based on specified ion species by ignoring all available data in the file check box. These files carry ion species information. Repeat the search until you have searched all the compounds in all the files of interest and the lipids are identified. After searching all the files, export the data to annotated CEF files. You can create all the CEF files at one time. 52

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