Name: an integrated workflow of identification and quantification on fdr control-based untargeted metabolome
Uploaded: 2022-09-20T13:00:00
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....

<ol>
	<li>Misra, B. B., Fahrmann, J. F., Grapov, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Review+of+emerging+metabolomic+tools+and+resources:+2015-2016.">Review of emerging metabolomic tools and resources: 2015-2016.</a> Electrophoresis. 38 (18), 2257-2274 (2017).</li><li>Idle, J. R., Gonzalez, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomics.">Metabolomics.</a> Cell Metabolism. 6 (5), 348-351 (2007).</li><li>Fiehn, O., Town, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomics+&#8212;+the+link+between+genotypes+and+phenotypes.">Metabolomics &#8212; the link between genotypes and phenotypes.</a> Functional Genomics. , 155-171 (2002).</li><li>Town, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Functional Genomics. , (2002).</li><li>Dettmer, K., Aronov, P. A., Hammock, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectrometry-based+metabolomics.">Mass spectrometry-based metabolomics.</a> Mass Spectrometry Reviews. 26 (1), 51-78 (2007).</li><li>Vinayavekhin, N., Saghatelian, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Untargeted+metabolomics.">Untargeted metabolomics.</a> Current Protocols in Molecular Biology. , 1-24 (2010).</li><li>Chaleckis, R., Meister, I., Zhang, P., Wheelock, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Challenges,+progress+and+promises+of+metabolite+annotation+for+LC-MS-based+metabolomics.">Challenges, progress and promises of metabolite annotation for LC-MS-based metabolomics.</a> Current Opinion in Biotechnology. 55, 44-50 (2019).</li><li>Palmer, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FDR-controlled+metabolite+annotation+for+high-resolution+imaging+mass+spectrometry.">FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.</a> Nature Methods. 14 (1), 57-60 (2017).</li><li>Scheubert, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Significance+estimation+for+large+scale+metabolomics+annotations+by+spectral+matching.">Significance estimation for large scale metabolomics annotations by spectral matching.</a> Nature Communications. 8 (1), 1494 (2017).</li><li>Schrimpe-Rutledge, A. C., Codreanu, S. G., Sherrod, S. D., McLean, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Untargeted+metabolomics+strategies-challenges+and+emerging+directions.">Untargeted metabolomics strategies-challenges and emerging directions.</a> Journal of the American Society for Mass Spectrometry. 27 (12), 1897-1905 (2016).</li><li>Bla&#382;enovi&#263;, I., Kind, T., Ji, J., Fiehn, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Software+tools+and+approaches+for+compound+identification+of+LC-MS/MS+data+in+metabolomics.">Software tools and approaches for compound identification of LC-MS/MS data in metabolomics.</a> Metabolites. 8 (2), (2018).</li><li>Katajamaa, M., Miettinen, J., Oresic, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MZmine:+toolbox+for+processing+and+visualization+of+mass+spectrometry+based+molecular+profile+data.">MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.</a> Bioinformatics. 22 (5), 634-636 (2006).</li><li>Tsugawa, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hydrogen+rearrangement+rules:+computational+MS/MS+fragmentation+and+structure+elucidation+using+MS-FINDER+software.">Hydrogen rearrangement rules: computational MS/MS fragmentation and structure elucidation using MS-FINDER software.</a> Analytical chemistry. 88 (16), 7946-7958 (2016).</li><li>Wang, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CFM-ID+4.0:+More+accurate+ESI-MS/MS+spectral+prediction+and+compound+identification.">CFM-ID 4.0: More accurate ESI-MS/MS spectral prediction and compound identification.</a> Analytical Chemistry. 93 (34), 11692-11700 (2021).</li><li>Ruttkies, C., Schymanski, E. L., Wolf, S., Hollender, J., Neumann, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetFrag+relaunched:+incorporating+strategies+beyond+in+silico+fragmentation.">MetFrag relaunched: incorporating strategies beyond in silico fragmentation.</a> Journal of Cheminformatics. 8, 3 (2016).</li><li>Delabriere, A., Warmer, P., Brennsteiner, V., Zamboni, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SLAW:+A+scalable+and+self-optimizing+processing+workflow+for+untargeted+LC-MS.">SLAW: A scalable and self-optimizing processing workflow for untargeted LC-MS.</a> Analytical chemistry. 93 (45), 15024-15032 (2021).</li><li>Wang, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Target-decoy-based+false+discovery+rate+estimation+for+large-scale+metabolite+identification.">Target-decoy-based false discovery rate estimation for large-scale metabolite identification.</a> Journal of Proteome Research. 17 (7), 2328-2334 (2018).</li><li>Li, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XY-Meta:+a+high-efficiency+search+engine+for+large-scale+metabolome+annotation+with+accurate+FDR+estimation.">XY-Meta: a high-efficiency search engine for large-scale metabolome annotation with accurate FDR estimation.</a> Analytical Chemistry. 92 (8), 5701-5707 (2020).</li><li>Wen, B., Mei, Z., Zeng, C., Liu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=metaX:+a+flexible+and+comprehensive+software+for+processing+metabolomics+data.">metaX: a flexible and comprehensive software for processing metabolomics data.</a> BMC Bioinformatics. 18 (1), 183 (2017).</li><li>Aberg, K. M., Torgrip, R. J. O., Kolmert, J., Schuppe-Koistinen, I., Lindberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Feature+detection+and+alignment+of+hyphenated+chromatographic-mass+spectrometric+data.+Extraction+of+pure+ion+chromatograms+using+Kalman+tracking.">Feature detection and alignment of hyphenated chromatographic-mass spectrometric data. Extraction of pure ion chromatograms using Kalman tracking.</a> Journal of Chromatography. A. 1192 (1), 139-146 (2008).</li><li>Liu, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Addressing+the+batch+effect+issue+for+LC/MS+metabolomics+data+in+data+preprocessing.">Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing.</a> Scientific Reports. 10 (1), 13856 (2020).</li><li>Han, W., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+and+minimizing+batch+effects+in+metabolomics.">Evaluating and minimizing batch effects in metabolomics.</a> Mass Spectrometry Reviews. 41 (3), 421-442 (2022).</li><li>Fei, F., Bowdish, D. M. E., McCarry, B. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+and+simultaneous+coverage+of+lipid+and+polar+metabolites+for+endogenous+cellular+metabolomics+using+HILIC-TOF-MS.">Comprehensive and simultaneous coverage of lipid and polar metabolites for endogenous cellular metabolomics using HILIC-TOF-MS.</a> Analytical and Bioanalytical Chemistry. 406 (15), 3723-3733 (2014).</li><li>Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., Siuzdak, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=XCMS:+processing+mass+spectrometry+data+for+metabolite+profiling+using+nonlinear+peak+alignment,+matching,+and+identification.">XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.</a> Analytical Chemistry. 78 (3), 779-787 (2006).</li><li>Giacomoni, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Workflow4Metabolomics:+a+collaborative+research+infrastructure+for+computational+metabolomics.">Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.</a> Bioinformatics. 31 (9), 1493-1495 (2015).</li><li>Chang, H. -. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=iMet-Q:+A+user-friendly+tool+for+label-free+metabolomics+quantitation+using+dynamic+peak-width+determination.">iMet-Q: A user-friendly tool for label-free metabolomics quantitation using dynamic peak-width determination.</a> PloS One. 11 (1), 0146112 (2016).</li><li>Wang, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sharing+and+community+curation+of+mass+spectrometry+data+with+Global+Natural+Products+Social+Molecular+Networking.">Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.</a> Nature Biotechnology. 34 (8), 828-837 (2016).</li><li>Schmid, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ion+identity+molecular+networking+for+mass+spectrometry-based+metabolomics+in+the+GNPS+environment.">Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment.</a> Nature Communications. 12 (1), 3832 (2021).</li><li>Kessner, D., Chambers, M., Burke, R., Agus, D., Mallick, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=ProteoWizard:+open+source+software+for+rapid+proteomics+tools+development.">ProteoWizard: open source software for rapid proteomics tools development.</a> Bioinformatics. 24 (21), 2534-2536 (2008).</li><li>Johnson, S. R., Lange, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Open-access+metabolomics+databases+for+natural+product+research:+present+capabilities+and+future+potential.">Open-access metabolomics databases for natural product research: present capabilities and future potential.</a> Frontiers in Bioengineering and Biotechnology. 3, 22 (2015).</li><li>Horai, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MassBank:+a+public+repository+for+sharing+mass+spectral+data+for+life+sciences.">MassBank: a public repository for sharing mass spectral data for life sciences.</a> Journal of Mass Spectrometry: JMS. 45 (7), 703-714 (2010).</li><li>Rawlinson, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+clustering+of+MS/MS+spectra+from+the+firefly+metabolome+identifies+new+lucibufagin+compounds.">Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.</a> Scientific Reports. 10 (1), 6043 (2020).</li></ol>

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 sp...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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 t...

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 ...

This protocol enables qualitative and quantitative analysis of untargeted metabolome based on FDR quality control which effectively reduces the false positives of metabolite identification. This workflow integrates XY-Meta that uses the target-decoy strategy to evaluate FDR more accurately for metabolome identification in the qualitative analysis module. This measure can effectively filter false positive results of untargeted metabolome identification, which improves the robustness of discovering biomarkers or key molecules.We expect that researchers can understand and master the target-decoy strategy for FDR control and should try to strictly run this pipeline several times with the default parameters of the protocol. Begin by going to the GNPS database webpage and click browse datasets. Search the key word in the title column, then click the dataset ID number.Download the dataset using FTP and save the raw data in the respective folder. To convert the format of raw data, first install the ProteoWizard software. Under the ProteoWizard installation path, type msconvert.exe, followed by the specific parameters to convert the raw data format to mzXML format. Again, using msconvert. exe convert this data to MGF format and store them in the MGF folder.To prepare the reference spectral library for the metabolites, go to the GNPS webpage. Search the keyword NIST, click view for the details and download the library. Save the library in the database folder.Download the XY-Meta program. Find the parameter configuration file under the config folder and change its contents as described in the text protocol. Set the type of adducts as a list in the adduct folder.Perform the metabolite identification and false discovery rate control using the command XY-Meta.exe. Download and install the metaX software package. Then edit the samplelist.txt file to specify the sample and its corresponding mass spectrometry data as described in the text protocol. Use the R file provided with the text protocol to run the script for quantification of Mock and wild-type groups using the metaX software. Check the output folder in which the results of the quantitative analysis are stored such as the PCA plot.Next, modify the parameters in the R script to annotate the peaks in qualitative and quantitative analysis using metabolite identifications in order to integrate both the results and run the R script. Box plots of quantified metabolites showed that the general distribution of healthy and disease samples was similar with low fluctuation of the mean values. Only 3.39%of the metabolites had more than 30%of the missing values.Principle component analysis plot of the samples from both the groups showed that metaX remarkably increased the proportion of the metabolites with CV less than 0.3. A Venn diagram of differentially detected metabolites from three statistical test methods revealed 119 common metabolites. Retention time and mass by charge distribution of all annotated metabolites were plotted with a false discovery rate of less than 0.01, showing six significant and differentially detected metabolites.It is important to limit the time for workflow testing. Remember, not to select too many samples for analysis and keep at least two samples in each group. This technique enables quality control of metabolized identification from data independent acquisition data constructing a more robust reference spectrum spectral library using the spectrum matching results based on FDR control.

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

Research

JoVE Journal

Biochemistry

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages for identifying biologically active chemical structures that can modify complex phenotypes such as lifespan. Described here is a simple protocol for producing hundreds of 96-well culture plates with fairly consistent numbers of C. elegans in each well. Next, we specified how to use these cultures to screen thousands of chemicals for effects on the lifespan of the nematode C. elegans. This protocol makes use of temperature sensitive sterile strains, agar plate conditions, and simple animal handling to facilitate the rapid and high throughput production of synchronized animal cultures for screening.

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Here we describe a simple protocol for rapidly producing hundreds of nematode growth media agar, 96-well culture plates with consistent numbers of Caenorhhabditis elegans per well. These cultures are useful for the phenotypic screening of whole organisms. We focus here on using these cultures to screen chemicals for pro-longevity effects.

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages ...

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; however, the number of identified targets for Cdk1, particularly in human cells is still low. The identification of Cdk1-specific phosphorylation sites is important, as they provide mechanistic insights into how Cdk1 controls the cell cycle. Cell cycle regulation is critical for faithful chromosome segregation, and defects in this complicated process lead to chromosomal aberrations and cancer.
Here, we describe an in vitro kinase assay that is used to identify Cdk1-specific phosphorylation sites. In this assay, a purified protein is phosphorylated in vitro by commercially available human Cdk1/cyclin B. Successful phosphorylation is confirmed by SDS-PAGE, and phosphorylation sites are subsequently identified by mass spectrometry. We also describe purification protocols that yield highly pure and homogeneous protein preparations suitable for the kinase assay, and a binding assay for the functional verification of the identified phosphorylation sites, which probes the interaction between a classical nuclear localization signal (cNLS) and its nuclear transport receptor karyopherin &#945;. To aid with experimental design, we review approaches for the prediction of Cdk1-specific phosphorylation sites from protein sequences. Together these protocols present a very powerful approach that yields Cdk1-specific phosphorylation sites and enables mechanistic studies into how Cdk1 controls the cell cycle. Since this method relies on purified proteins, it can be applied to any model organism and yields reliable results, especially when combined with cell functional studies.

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is activated in the G2 phase of the cell cycle and regulates many cellular pathways. Here, we present a protocol for an in vitro kinase assay with Cdk1, which allows the identification of Cdk1-specific phosphorylation sites for establishing cellular targets of this important kinase.

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; ...

Quantification of Hypopigmentation Activity In Vitro

This study presents laboratory methods for the quantification of hypopigmentation activity in vitro. Melanin, the major pigment in melanocytes, is synthesized in response to multiple cellular and environmental factors. Melanin protects skin cells from ultraviolet damage, but also has biophysical and biochemical functions. Excessive production or accumulation of melanin in melanocytes can cause dermatological problems, such as freckles, dark spots, melasma, and moles. Therefore, the control of melanogenesis with hypopigmentation agents is important in individuals with clinical or cosmetic needs. Melanin is primarily synthesized in the melanosomes of melanocytes in a complex biochemical process called melanogenesis, which is influenced by extrinsic and intrinsic factors, such as hormones, inflammation, age, and ultraviolet light exposure. We describe three methods to determine the hypopigmentation activity of chemicals or natural substances in melanocytes: measurement of the 1) cellular tyrosinase activity and 2) melanin content, and 3) staining and quantifying cellular melanin with image analysis.
In melanogenesis, tyrosinase catalyzes the rate-limiting step that converts L-tyrosine into 3,4-dihydroxyphenylalanine (L-DOPA) and then into dopaquinone. Therefore, the inhibition of tyrosinase is a primary hypopigmentation mechanism. In cultured melanocytes, tyrosinase activity can be quantified by adding L-DOPA as a substrate and measuring dopaquinone production by spectrophotometry. Melanogenesis can also be measured by quantifying the melanin content. The melanin-containing cellular fraction is extracted with NaOH and melanin is quantified spectrophotometrically. Finally, the melanin content can be quantified by image analysis following Fontana-Masson staining of melanin. Although the results of these in vitro assays may not always be reproduced in human skin, these methods are widely used in melanogenesis research, especially as the initial step to identify potential hypopigmentation activity. These methods can also be used to assess melanocyte activity, growth, and differentiation. Consistent results with the three different methods ensure the validity of the effects.

We describe three experimental methods for evaluating the hypopigmentation activity of chemicals in vitro: quantification of 1) cellular tyrosinase activity and 2) melanin content, and 3) measurement of melanin by cellular melanin staining and image analysis.

This study presents laboratory methods for the quantification of hypopigmentation activity in vitro. Melanin, the major pigment in melanocytes, is ...

Quantification of Coenzyme A in Cells and Tissues

Emerging research has revealed that the cellular coenzyme A (CoA) supply can become limiting with a detrimental impact on growth, metabolism and survival. Measurement of cellular CoA is a challenge due to its relatively low abundance and the dynamic conversion of free CoA to CoA thioesters that, in turn, participate in numerous metabolic reactions. A method is described that navigates through potential pitfalls during sample preparation to yield an assay with a broad linear range of detection that is suitable for use in many biomedical laboratories.

This method describes sample preparation from cultured cells and animal tissues, extraction and derivatization of coenzyme A in the samples, followed by high pressure liquid chromatography for purification and quantification of the derivatized coenzyme A by absorbance or fluorescence detection.

Emerging research has revealed that the cellular coenzyme A (CoA) supply can become limiting with a detrimental impact on growth, metabolism and survival. ...

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism&#39;s biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

A method for the untargeted analysis of wheat grain metabolites and lipids is presented. The protocol includes an acetonitrile metabolite extraction method and reversed phase liquid chromatography-mass spectrometry methodology, with acquisition in positive and negative electrospray ionization modes.

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management ...

Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

Fragment screening is a technique that helps to identify promising starting points for ligand design. Given that crystals of the target protein are available and display reproducibly high-resolution X-ray diffraction properties, crystallography is among the most preferred methods for fragment screening because of its sensitivity. Additionally, it is the only method providing detailed 3D information of the binding mode of the fragment, which is vital for subsequent rational compound evolution. The routine use of the method depends on the availability of suitable fragment libraries, dedicated means to handle large numbers of samples, state-of-the-art synchrotron beamlines for fast diffraction measurements and largely automated solutions for the analysis of the results.
Here, the complete practical workflow and the included tools on how to conduct crystallographic fragment screening (CFS) at the Helmholtz-Zentrum Berlin (HZB) are presented. Preceding this workflow, crystal soaking conditions as well as data collection strategies are optimized for reproducible crystallographic experiments. Then, typically in a one to two-day procedure, a 96-membered CFS-focused library provided as dried ready-to-use plates is employed to soak 192 crystals, which are then flash-cooled individually. The final diffraction experiments can be performed within one day at the robot-mounting supported beamlines BL14.1 and BL14.2 at the BESSY&#160; II electron storage ring operated by the HZB in Berlin-Adlershof (Germany). Processing of the crystallographic data, refinement of the protein structures, and hit identification is fast and largely automated using specialized software pipelines on dedicated servers, requiring little user input.
Using the CFS workflow at the HZB enables routine screening experiments. It increases the chances for successful identification of fragment hits as starting points to develop more potent binders, useful for pharmacological or biochemical applications.

Crystallographic fragment screening at the Helmholtz-Zentrum Berlin is performed using a workflow with dedicated compound libraries, crystal handling tools, fast data collection facilities&#160;and largely automated data analysis. The presented protocol intends to maximize the output of such experiments to provide promising starting points for downstream structure-based ligand design.

Fragment screening is a technique that helps to identify promising starting points for ligand design. Given that crystals of the target protein are ...

Evaluating the Effect of Pesticides on the Larvae of the Solitary Bees

Current ecological risk assessments of pesticides on pollinators have primarily considered only laboratory conditions. For the larvae of solitary bees, ingestion of provisions contaminated with pesticides may increase the mortality rate of the larvae, decrease the collection rate and the population of adult solitary bees in the next year from a demographic perspective. But there are limited studies on the effects of pesticides on the larvae of solitary bees. Therefore, understanding how pesticides influence the larvae of solitary bees should be considered an integral part of pesticide ecological risk assessment. This study presents a method to expose the larvae of solitary bee, Osmia excavata, to lethal or sublethal doses of pesticide, tracking larval weight gain, developmental duration, eclosion ability, and food consumption efficiency conversion of ingested food. To demonstrate the effectiveness of this method, the larvae of O. excavata were fed with provisions containing acute lethal and sublethal doses of chlorpyrifos. Then, the above indexes of the treated larvae were investigated. This technique helps to predict and mitigate the risk of pesticides to pollinators.

The present protocol explains a method to feed pesticide-contaminated provisions to the larvae of the solitary bees, Osmia excavata. The procedure examines the ecotoxicity of the pesticide to the larvae of the solitary bees.

Current ecological risk assessments of pesticides on pollinators have primarily considered only laboratory conditions. For the larvae of solitary bees, ...

Quantification of Subcellular Glycogen Distribution in Skeletal Muscle Fibers using Transmission Electron Microscopy

With the use of transmission electron microscopy, high-resolution images of fixed samples containing individual muscle fibers can be obtained. This enables quantifications of ultrastructural aspects such as volume fractions, surface area to volume ratios, morphometry, and physical contact sites of different subcellular structures. In the 1970s, a protocol for enhanced staining of glycogen in cells was developed and paved the way for a string of studies on the subcellular localization of glycogen and glycogen particle size using transmission electron microscopy. While most analyses interpret glycogen as if it is homogeneously distributed within the muscle fibers, providing only a single value (e.g., an average concentration), transmission electron microscopy has revealed that glycogen is stored as discrete glycogen particles located in distinct subcellular compartments. Here, the step-by-step protocol from tissue collection to the quantitative determination of the volume fraction and particle diameter of glycogen in the distinct subcellular compartments of individual skeletal muscle fibers is described. Considerations on how to 1) collect and stain tissue specimens, 2) perform image analyses and data handling, 3) evaluate the precision of estimates, 4) discriminate between muscle fiber types, and 5) methodological pitfalls and limitations are included.

A modified post-fixation procedure increases the contrast of glycogen particles in tissue. This paper provides a step-by-step protocol describing how to handle the tissue, conduct the imaging, and use stereological methods to obtain unbiased and quantitative data on fiber type-specific subcellular glycogen distribution in skeletal muscle.

With the use of transmission electron microscopy, high-resolution images of fixed samples containing individual muscle fibers can be obtained. This ...

Deciphering Molecular Mechanism of Histone Assembly by DNA Curtain Technique

Chromatin is a higher-order structure that packages eukaryotic DNA. Chromatin undergoes dynamic alterations according to the cell cycle phase and in response to environmental stimuli. These changes are essential for genomic integrity, epigenetic regulation, and DNA metabolic reactions such as replication, transcription, and repair. Chromatin assembly is crucial for chromatin dynamics and is catalyzed by histone chaperones. Despite extensive studies, the mechanisms by which histone chaperones enable chromatin assembly remains elusive. Moreover, the global features of nucleosomes organized by histone chaperones are poorly understood. To address these problems, this work describes a unique single-molecule imaging technique named DNA curtain, which facilitates the investigation of the molecular details of nucleosome assembly by histone chaperones. DNA curtain is a hybrid technique that combines lipid fluidity, microfluidics, and total internal reflection fluorescence microscopy (TIRFM) to provide a universal platform for real-time imaging of diverse protein-DNA interactions.Using DNA curtain, the histone chaperone function of Abo1, the Schizosaccharomyces pombe bromodomain-containing AAA+ ATPase, is investigated, and the molecular mechanism underlying histone assembly of Abo1 is revealed. DNA curtain provides a unique approach for studying chromatin dynamics.

DNA curtain, a high-throughput single-molecule imaging technique, provides a platform for real-time visualization of diverse protein-DNA interactions. The present protocol utilizes the DNA curtain technique to investigate the biological role and molecular mechanism of Abo1, a Schizosaccharomyces pombe bromodomain-containing AAA+ ATPase.

Chromatin is a higher-order structure that packages eukaryotic DNA. Chromatin undergoes dynamic alterations according to the cell cycle phase and in ...