Name: an integrated workflow of identification and quantification on fdr control-based untargeted metabolome
Uploaded: 2022-09-20T13:00:00.000+00:00
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Description: Watch this Scientific Journal Video about An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome at JoVE.com

This work is supported by National Key Research and Development Program (2018YFC0910200/2017YFA0505001) and the Guangdong Key R&#38;D Program (2019B020226001).

1. Prepare metabolomics datasets for analysis
NOTE: In this demonstration, we use metabolomics datasets without QC sample. Data for case and control groups are needed. For demonstration, we use a public dataset in GNPS database27.
<ol>
	<li>Go to the webpage&#160;https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp. Click&#160;Browse Datasets.</li>
	<li>Search the keyword &#34;msv000084112&#34; in the Title column. Click the dataset ID number for details and download the dataset using FTP.</li>
	<li>Put the raw data in the folder /msv000084112. 
	&#8203;NOTE: This dataset was acquired using C18 RP-UHPLC on Q Exactive platform in positive mode. It represents a cohort with an uncharacterized disease of the metabolism of urine samples data, including 33 samples of healthy people, 12 blank samples, two mixing samples, and 82 samples of patients28 (Supplementary Material 8). To demonstrate the workflow, we randomly chose six samples of healthy people (NH) as a control group and six samples with the disease (NT) as a case group to perform the workflow.</li>
</ol>
2. Data format conversion
NOTE: If the dataset is the raw data generated directly from the mass spectrometer, it is usually in .raw, .wiff or .cdf format. They should be converted to mzXML and mgf formats. Here, we use the msconvert tool in ProteoWizard29 package to do the format conversion.
<ol>
	<li>Download the ProteoWizard from&#160;https://proteowizard.sourceforge.io/download.html and install it.</li>
	<li>Convert data format using msconvert.exe under the ProteoWizard installation path.
	<ol>
		<li>Convert the raw data to mzXML format and store them in /mzXML folder:/msconvert.exe /raw/*.raw -o /raw/mzXML/ --filter &#34;peakPicking true [1,2]&#34; --filter &#34;zeroSamples removeExtra&#34; --mzML --zlib --mz64 --filter &#34;msLevel 1-2&#34; --filter &#34;titleMaker &#60;RunId&#62;.&#60;ScanNumber&#62;.&#60;ScanNumber&#62;. &#60;ChargeState&#62;&#34;.</li>
		<li>Convert the raw/mzXML data to mgf format and store them in /mgf folder:/msconvert.exe /msv000084112/*.raw -o /msv000084112/mgf/ --filter &#34;peakPicking true [1,2]&#34; --filter &#34;zeroSamples removeExtra&#34; --mgf --mz64 --filter &#34;msLevel 1-2&#34; --filter &#34;titleMaker &#60;RunId&#62;.&#60;ScanNumber&#62;.&#60;ScanNumber&#62;. &#60;ChargeState&#62;&#34;.</li>
	</ol>
	</li>
</ol>
3. Prepare the reference spectral library for the metabolites
NOTE: XY-meta supports the reference spectral libraries only in mgf format.
<ol>
	<li>Go to the webpage https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp. Search the keyword &#8220;NIST&#8221; to find the item. Click&#160;View for the details and download the library. 
	NOTE: GNPS Public Spectral Libraries collected many metabolites libraries, arranged in type, origin, species, and collection modes. Although only a small fraction of these libraries are generated using standard materials, they are usually sufficient for most fundamental research.</li>
	<li>Put the downloaded library GNPS-NIST14-MATCHES.mgf into the /database folder.</li>
</ol>
4. Metabolites identification and FDR control
<ol>
	<li>Download the XY-meta (Windows version). Find the parameter configuration file parameter.default under the /XY-Meta-Win/config/ folder. Change its content according to Supplementary Material 1. 
	NOTE: In solution, metabolites often form adducts with anions or cations, which leads to a mass shift of parent ions. Therefore, it is necessary to set the types of adducts. We provided adduct lists for ion exchange column and reverse analytical columns under positive charge mode and negative charge mode in the /adduct folder. Users may also edit their own adduct list according to their research project. The adduct list should be in the same format as the provided list.</li>
	<li>Perform the metabolite identification and FDR control using XY-Meta:XY-Meta.exe -S /XY-Meta-Win/config/parameter.default -D /msv000084112/ pos_wt-1_a.mgf -R /database/GNPS-NIST14-MATCHES.mgf. 
	&#8203;NOTE: XY-Meta does not support wildcards in parameters. Therefore, a single command should be used to process each mgf file. For a large number of files, a batch file is recommended.</li>
</ol>
5. Differential analysis
NOTE: metaX is an open-source R package. Please install it according to the guide at https://github.com/wenbostar/metaX. 8GB RAM is required for this analysis.
<ol>
	<li>Edit a sampleList.txt file to specify the sample and its corresponding MS data. Please refer to Supplementary Material 2. 
	NOTE: metaX supports quantitative analysis for the datasets with QC samples. When using QC samples, please modify the class property to NA for QC samples.</li>
	<li>Create /output folder to store the results of quantitative analysis. Use R to run the script in Supplementary Material 3 to use metaX to quantify the MOCK and WT groups. 
	&#8203;NOTE: Before running the script in Supplementary Material 3, modify the paths in the script to the actual local paths.</li>
</ol>
6. Integration of qualitative and quantitative results
<ol>
	<li>Run the R script in Supplementary Material 4 to annotate the peaks in qualitative and quantitative analysis using metabolite identifications. 
	NOTE: Before running the script in Supplementary Material 4, please modify the paths in the script to your actual local paths.</li>
</ol>

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No conflicts of interest.

The FDR control of untargeted metabolites has been a great challenge. Here, we demonstrated a complete pipeline of large-scale untargeted metabolomics analysis (qualitative and quantitative) with FDR control. This effectively reduces the false positives, which are very common in MS analysis.
Preparing an appropriate reference spectral library for your study is a key point. A successful and sensitive MS/MS identification requires not only proper matching algorithms, but also proper reference spectral libraries. The applicability of public spectral libraries are limited due to the following reasons: (1) many public spectral libraries do not include complete metabolite lists. (2) The spectra in public spectral libraries originated from various MS instruments and/or various fragmentation conditions30,31. Therefore, we suggest you collect spectra in the same instrument and the same fragmentation conditions using standard metabolites to construct an &#34;exclusive&#34; spectral library. Also, these conditions should be maintained during the actual measurements. Additionally, when modifying the parameters file, the tolerance of the precursor ions and the fragment ions should coincide with the parameters of the instrument. Normally, the range of precursor tolerance should be between 10 ppm and 20 ppm, and the fragment tolerance should be set between 0.01 Da and 0.5 Da. For this data set, the parameters of instrument are unknown, but the fragment tolerance of 0.05 Da is a conservative choice for this workflow to perform normally.
Users may still receive various error messages when they run this pipeline. Common mistakes include erroneous input file path, missing parameter file, and file access conflict (e.g., access denied by the operating system and simultaneous accessing of the same file).
To be noted, this workflow is currently only applicable to the targeted and untargeted metabolomic analysis of small molecules less than 1,000 Da, and cannot be used to analyze the metabolomes of macromolecules such as glycan chains or lipid chains. In addition, both the data independent acquirement (DIA) data and ion mobility data are not suitable for analysis with this workflow. This workflow does not support the use of m/z and retention time of metabolites to annotate peak detection results and only supports differential analysis of two groups of data with more than two samples.
For a long time, the identification results of untargeted metabolome dominated by peak detection technology has tended to contain a lot of false positives, mainly due to the large number of metabolite isomers and different ionic adduct forms. Comparing the MS/MS spectra of metabolites with the reference spectra of known metabolites can resolve the structure of the metabolites to distinguish isomers32. However, a metabolite cannot be identified if the reference spectrum of a metabolite is not publicly or commercially available7. Therefore, building a reliable library of metabolite reference spectra is a big challenge. Reference spectra of low-quality and with similar structure leads to random matching of experimental spectra. Therefore, FDR control of identification results is necessary to ensure confident identifications. Users can use this pipeline to automatically identify metabolome with FDR control, as well as quantitation and differential analysis, by providing the necessary input data as the protocol required. That is convenient and economical for many researchers especially for beginners.

Metabolites play important roles in biological processes. Metabolites are often regulators of various processes like energy transfer, hormone regulations, regulation of neurotransmitters, cellular communications, and protein post-translational modifications, etc1,2,3,4. Untargeted metabolomics provides a global view of numerous metabolites5,6. With advances in mass spectrometry and chromatography technologies, the throughput of metabolome MS/MS spectra is rapidly increasing in recent years7,8,9,10,11. To identify metabolites from these huge datasets, various annotation software were developed11, such as MZmine12, MS-FINDER13, CFM-ID14, MetFrag15, and SLAW16. However, these identifications often contain many false positives. The reasons include: (1) The MS/MS spectra contain random noise, which may mislead the peak matching. (2) Isomers and differences in fragmentation energies cause multiple spectra fingerprints and thus increase the volume of the reference library. (3) The quality of reference libraries varies. A proper standard to build a good reference spectral library is needed. Therefore, a systematic false discovery rate (FDR) control for untargeted metabolomics is essential for functional metabolome research7,8,9,17.
Both the Empirical Bayes approach and Target-Decoy strategy tackled the FDR control problem generally. Kerstin Scheubert et al. showed that the Target-Decoy strategy on decoy database generated from fragmentation tree-based method is the best method for FDR control9. Xusheng Wang et al. designed a method for decoy generation based on the octet rule in chemistry and improved the precision of FDR estimation17. The spectral library for generating decoy database was demonstrated for better performance18. Here, we improved the spectral library-based method and developed a software called XY-Meta19 that can further improve FDR estimation&#39;s precision. It uses the existing reference spectral library to generate a decoy library for the FDR control under the Target-Decoy scheme. XY-Meta supports its own spectra matching and cosine similarity algorithms. It allows conventional search and iterative search modes. In the step of FDR assessment, it supports Target-Decoy concatenated mode and separated mode. For better flexibility, XY-Meta accepts external decoy libraries.
Peak detection and quantification of metabolites is also an important step of untargeted metabolome analysis. Peak detection is the main method for metabolome identification. In general, the accuracy of peak detection of metabolites was affected by multiple factors, such as noise signals of mass spectrometry, low abundance of metabolites, contaminants, and degradation products of metabolites20. When the number of samples of is too large or the liquid chromatography column was replaced in experiments of untargeted metabolome, remarkable batch effects may appear, which is a major challenge for metabolome quantitation21,22,23. Currently, software like XCMS24, Workflow4Metabolomic25, iMet-Q26, and metaX19 can perform peak detection and quantitation of untargeted metabolome, but we suggest that the pipeline of metaX is more complete and easier to use. Here, we demonstrate the process of identification and FDR control for a publicly available dataset msv000084112 using XY-Meta, and the peak detection and quantification of metabolites using metaX. This workflow only requires two groups, and each group needs at least two samples. MS/MS spectra data is needed, regardless of the mass spectrometer platform, ionization mode, charge mode, and sample type, and can support sample-based normalization and peak-based normalization. Following this example, researchers can perform metabolomics identification and quantification in an easy-to-handle way. Using this pipeline requires R programming capability. To help the researcher without any programming knowledge, we also developed a cloud analysis platform for metabolomics analysis. We demonstrated this cloud analysis platform in Supplementary Material 5.

<table><tbody><tr><td>GNPS</td><td>open source</td><td>n/a</td><td>https://gnps.ucsd.edu/ProteoSAFe/static/gnps-splash.jsp</td></tr><tr><td>XY-Meta</td><td>open source</td><td>n/a</td><td>https://github.com/DLI-ShenZhen/XY-Meta</td></tr><tr><td>metaX</td><td>open source</td><td>n/a</td><td>https://github.com/wenbostar/metaX</td></tr><tr><td>ProteoWizard</td><td>Free Download</td><td>3.0.22116.18c918b-x86_64</td><td>https://proteowizard.sourceforge.io/download.html</td></tr><tr><td>CHI.Client</td><td>Free Download</td><td>ndp48-x86-x64-allos-enu</td><td>http://www.chi-biotech.com/technology.html?ty=ypt</td></tr></tbody></table>

an integrated workflow of identification and quantification on fdr control-based untargeted metabolome

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

The raw data of msv000084112 was converted by msconvert.exe and generated mgf files (Supplementary Material S6).
XY-Meta generated GNPS-NIST14-MATCHES_Decoy.mgf file under /database folder. This is the decoy library generated from the original reference spectral library GNPS-NIST14-MATCHES.mgf. This decoy library can be reused. When reusing this decoy library, the user should set the decoy_pattern as 1 in parameter.default file, and set the decoyinput as the absolute path of the decoy library. The identification results were generated under /mgf folder (with the suffix .meta), which includes spectra matching scores, FDR, m/z of the metabolites, retention time, and the name of the metabolites (Supplementary Material 7).
The quantitative analysis by metaX was in /output folder. The general quantitative distribution of NH and NT are similar, with low fluctuation of the mean values (Figure 1A). There was only a small fraction of missing values: only 3.39% of the metabolites have more than 30% of the missing values (Figure 1B). metaX remarkably increased the proportion of the metabolites with CV &#8804; 0.3 (Figure 1C). The box plots were stored in /metaX_box folder. The elution profiles were stored in /metaX_eic folder. The metabolite peaks were recorded in metaX-feature.txt. The quantitative values of the metabolites which were identified in both groups and the differential analysis results were stored in metaX_peaks.txt (Figure 1D). Applying the threshold of |LogFC| &#8805; 1 and p-value &#60; 0.05, 342 metabolites were differentially detected, with 206 up-regulated and 136 down-regulated (Supplementary Material 9).
We annotated the metaX detected peaks using the FDR &#60; 0.01 identifications. If a peak can be annotated by multiple metabolites, we took the one with the highest spectrum matching score as the final annotation. Using these criteria, we annotated six differential metabolite peaks (Figure 2).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63625/63625fig01.jpg" /> 
Figure 1. Quantitative analysis by metaX. (A) Box plot of quantified metabolites of all samples. (B) Histogram of missing value distribution. (C) PCA plot of two group samples. (D) Venn diagram of differentially detected metabolites from three statistical test methods. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/63625fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63625/63625fig02.jpg" /> 
Figure 2. Retention time (RT) and m/z distribution of all annotated metabolites. Red dots represent the significant and differentially detected metabolites. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/63625fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Material 1: The parameter file of XY-Meta. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/Supplementary material S1.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 2: Grouping information sheet of samples for metaX. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/Supplementary material S2.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 3: The script for integrating the workflow of XY-Meta and metaX. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/Supplementary material S3.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 4: The script for annotating the peaks using metabolome identifications. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/Supplementary material S4.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 5: A complete workflow for metabolome analysis using the cloud platform. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/Supplementary material S5.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 6: An mgf file converted from msconvert for a sample data of msv000084112. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/supplementary material S6.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 7: An identification result table from XY-Meta for a sample data of msv000084112. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/supplementary material S7.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 8: The cohort clinical information sheet of msv000084112. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/supplementary material S8.zip" target="_blank">Please click here to download this File.</a>
Supplementary Material 9: Identification list of all metabolites and differential analysis results of all metabolite peaks. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63625/supplementary material S9.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome at JoVE.com

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

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

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

an-integrated-workflow-identification-quantification-on-fdr-control

Nanyang Technological University

Research

JoVE Journal

Biochemistry

3.6K Views.  Jinan University. Dit protocol maakt kwalitatieve en kwantitatieve analyse van ongericht metaboloom mogelijk op basis van FDR-kwaliteitscontrole die de valse positieven van metabolietidentificatie effectief vermindert. Deze workflow integreert XY-Meta die de target-decoy-strategie gebruikt om FDR nauwkeuriger te evalueren voor metaboloomidentificatie in de kwalitatieve analysemodule. Deze maatregel kan vals-positieve resultaten van ongerichte metaboloomidentificatie effectief filteren, wat de robuustheid van h...

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

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.

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.

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

Chemistry

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.

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

A Simple Fractionated Extraction Method for the Comprehensive Analysis of Metabolites, Lipids, and Proteins from a Single Sample

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually determined by transcriptomic, proteomic, metabolomics and lipidomic measurements. In this protocol, we introduce a simple method for the reproducible extraction of metabolites, lipids and proteins from biological tissues using a single aliquot per sample. The extraction method is based on a methyl tert-butyl ether: methanol: water system for liquid: liquid partitioning of hydrophobic and polar metabolites into two immiscible phases along with the precipitation of proteins and other macromolecules as a solid pellet. This method, therefore, provides three different fractions of specific molecular composition, which are fully compatible with common high throughput 'omics' technologies such as liquid chromatography (LC) or gas chromatography (GC) coupled to mass spectrometers. Even though the method was initially developed for the analysis of different plant tissue samples, it has proved to be fully compatible for the extraction and analysis of biological samples from systems as diverse as algae, insects, and mammalian tissues and cell cultures.

A protocol for comprehensive extraction of lipids, metabolites and proteins from biological tissues using one sample is presented.

Understanding of complex biological systems requires the measurement, analysis and integration of multiple compound classes of the living cell, usually ...

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of protein-protein interactions (PPI) is no novelty, experimental approaches aiming to characterize endogenous protein-metabolite interactions (PMI) constitute a rather recent development. Herein, we present a protocol that allows simultaneous characterization of the PPI and PMI of a protein of choice, referred to as bait. Our protocol was optimized for Arabidopsis cell cultures and combines affinity purification (AP) with mass spectrometry (MS)-based protein and metabolite detection. In short, transgenic Arabidopsis lines, expressing bait protein fused to an affinity tag, are first lysed to obtain a native cellular extract. Anti-tag antibodies are used to pull down protein and metabolite partners of the bait protein. The affinity-purified complexes are extracted using a one-step methyl tert-butyl ether (MTBE)/methanol/water method. Whilst metabolites separate into either the polar or the hydrophobic phase, proteins can be found in the pellet. Both metabolites and proteins are then analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS or UPLC-MS/MS). Empty-vector (EV) control lines are used to exclude false positives. The major advantage of our protocol is that it enables identification of protein and metabolite partners of a target protein in parallel in near-physiological conditions (cellular lysate). The presented method is straightforward, fast, and can be easily adapted to biological systems other than plant cell cultures.

Protein-protein and protein-metabolite interactions are crucial for all cellular functions. Herein, we describe a protocol that allows parallel analysis of these interactions with a protein of choice. Our protocol was optimized for plant cell cultures and combines affinity purification with mass spectrometry-based protein and metabolite detection.

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Environment

Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the local to federal levels. The continuing evolution of PFAS chemistries presents a challenge to the environmental monitoring, where ongoing development of targeted methods necessarily lags the discovery of new chemical compounds. There is a need, therefore, to have forward-looking methodologies that can detect emerging and unexpected compounds, monitor these species over time, and resolve details of their chemical structure to enable future work in human health. To this end, non-targeted analysis by high-resolution mass spectrometry offers a broad base detection approach that can be combined with almost any sample preparation scheme and provides significant capabilities for compound identification after detection. Herein, we describe a solid-phase extraction (SPE) based sample concentration method tuned for shorter chain and more hydrophilic PFAS chemistries, such as per fluorinated ether acids and sulfonates, and describe analysis of samples prepared in this fashion in both targeted and non-targeted modes. Targeted methods provide superior quantification when reference standards are available but are intrinsically limited to expected compounds when performing analysis. In contrast, a non-targeted approach can identify the presence of unexpected compounds and provide some information about their chemical structure. Information about chemical features can be used to correlate compounds across sample locations and track abundance and occurrence over time.

Here, we present a protocol for the sequential targeted quantification and non-targeted analysis of fluorinated compounds in water by mass spectrometry. This methodology provides quantitative levels of known fluorochemical compounds and identifies unknown chemicals in related samples with semi-quantitative estimates of their abundance.

Historical and emerging per- and polyfluoroalkyl substances (PFASs) have garnered significant interest from the public and government agencies from the ...

Multi-step Preparation Technique to Recover Multiple Metabolite Compound Classes for In-depth and Informative Metabolomic Analysis

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital aspect of metabolomics is sample preparation whereby inconsistent techniques generate unreliable results. This technique encompasses protein precipitation, liquid-liquid extraction, and solid-phase extraction as a means of fractionating metabolites into four distinct classes.&#160;Improved enrichment of low abundance molecules with a resulting increase in sensitivity is obtained, and ultimately results in more confident identification of molecules. This technique has been applied to plasma, bronchoalveolar lavage fluid, and cerebrospinal fluid samples with volumes as low as 50 &#181;l. &#160;Samples can be used for multiple downstream applications; for example, the pellet resulting from protein precipitation can be stored for later analysis. The supernatant from that step undergoes liquid-liquid extraction using water and strong organic solvent to separate the hydrophilic and hydrophobic compounds. Once fractionated, the hydrophilic layer can be processed for later analysis or discarded if not needed. The hydrophobic fraction is further treated with a series of solvents during three solid-phase extraction steps to separate it into fatty acids, neutral lipids, and phospholipids. This allows the technician the flexibility to choose which class of compounds is preferred for analysis. It also aids in more reliable metabolite identification since some knowledge of chemical class exists.

The reliability of results in metabolomics experiments depends on the effectiveness and reproducibility of the sample preparation. Described is a rigorous and in-depth method that enables extraction of metabolites from biological fluids with the option of subsequently analyzing up to thousands of compounds, or just the compound classes of interest.

Metabolomics is an emerging field which enables profiling of samples from living organisms in order to obtain insight into biological processes. A vital ...

Bioengineering

Preparation of Drosophila Larval Samples for Gas Chromatography-Mass Spectrometry (GC-MS)-based Metabolomics

Recent advances in the field of metabolomics have established the fruit fly Drosophila melanogaster as a powerful genetic model for studying animal metabolism. By combining the vast array of Drosophila genetic tools with the ability to survey large swaths of intermediary metabolism, a metabolomics approach can reveal complex interactions between diet, genotype, life-history events, and environmental cues. In addition, metabolomics studies can discover novel enzymatic mechanisms and uncover previously unknown connections between seemingly disparate metabolic pathways. In order to facilitate more widespread use of this technology among the Drosophila community, here we provide a detailed protocol that describes how to prepare Drosophila larval samples for gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis. Our protocol includes descriptions of larval sample collection, metabolite extraction, chemical derivatization, and GC-MS analysis. Successful completion of this protocol will allow users to measure the relative abundance of small polar metabolites, including amino acids, sugars, and organic acids involved in glycolysis and the TCA cycles.

Preparation of Drosophila Larval Samples for Gas Chromatography-Mass Spectrometry GC-MS-based Metabolomics

This protocol describes how to prepare Drosophila larvae for GC-MS-based metabolomic analysis.

Recent advances in the field of metabolomics have established the fruit fly Drosophila melanogaster as a powerful genetic model for studying animal ...

Developmental Biology

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

Biology

Low-Input Metabolic Studies in Mice Cell Types

Targeted Metabolomics on Rare Primary Cells

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule intermediates. However, obtaining accurate and reliable measurements of cellular metabolism, particularly in rare cell types like hematopoietic stem cells, has traditionally required pooling cells from multiple animals. A protocol now enables researchers to measure metabolites in rare cell types using only one mouse per sample while generating multiple replicates for more abundant cell types. This reduces the number of animals that are required for a given project. The protocol presented here involves several key differences over traditional metabolomics protocols, such as using 5 g/L NaCl as a sheath fluid, sorting directly into acetonitrile, and utilizing targeted quantification with rigorous use of internal standards, allowing for more accurate and comprehensive measurements of cellular metabolism. Despite the time required for the isolation of single cells, fluorescent staining, and sorting, the protocol can preserve differences among cell types and drug treatments to a large extent.

Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells 

Here, we present a protocol to accurately and reliably measure metabolites in rare cell types. Technical improvements, including a modified sheath fluid for cell sorting and the generation of relevant blank samples, enable a comprehensive quantification of metabolites with an input of only 5000 cells per sample.

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule ...

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

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