METLIN

Last updated
METLIN Database
Content
Descriptionrepository of chemical entity information as well as tandem mass spectrometry data
Contact
Research center The Scripps Research Institute
LaboratorySiuzdak laboratory at The Scripps Research Institute
Release date2005
Access
Website metlin.scripps.edu

The METLIN Metabolite and Chemical Entity Database [1] [2] [3] is the largest repository of experimental tandem mass spectrometry [4] and neutral loss [5] data acquired from standards. The tandem mass spectrometry data on over 930,000 molecular standards (as of December, 2023) [6] [7] [8] [9] [10] is provided to facilitate the identification of chemical entities from tandem mass spectrometry experiments. In addition to the identification of known molecules, it is also useful for identifying unknowns [3] using its similarity searching technology. [11] All tandem mass spectrometry data comes from the experimental analysis of standards at multiple collision energies and in both positive and negative ionization modes.

Contents

METLIN [12] serves as a data management system to assist in metabolite and chemical entity identification by providing public access to its repository of comprehensive MS/MS and neutral loss data. [7] [3] [5] METLIN's annotated list of molecular standards include metabolites and other chemical entities, searching METLIN can be done based on a molecule's tandem mass spectrometry data, neutral loss masses, precursor mass, chemical formula, and structure within the METLIN website. Each molecule is linked to outside resources such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) for further reference and inquiry. The METLIN database was developed and is maintained solely by the Siuzdak laboratory at The Scripps Research Institute.

Constantly evolving

Since its initial implementation in the early 2000s, [2] the freely available METLIN website has collected comments and suggestions for improvements from users in the biotechnology, pharmaceutical and academic communities ultimately resulting in functionally useful technology for metabolomics as well as hundreds of thousands of other molecular entities. [7] The METLIN interface allows researchers to readily search the database and characterize metabolites and other compounds through features such as accurate mass, single and multiple fragment searching, neutral loss and full spectrum search capabilities. The similarity searching feature introduced in 2008 [11] was designed to expedite the identification process of unknown molecules.

Also, METLIN has been used to create a novel multiple reaction monitoring (MRM) library of precursor to fragment ion transitions. [13] The METLIN-MRM transition repository for small-molecule quantitative tandem mass spectrometry was designed to facilitate data sharing across different instruments and laboratories. [13]

The METLIN database is implemented in the cloud to enable users throughout the world. [7] [12] In addition to expanding the tandem mass spectrometry database, METLIN is designed to search tandem mass spectrometry data, precursor mass, chemical formulas, compound names among other search capabilities. METLIN has also been implemented with cognitive computing applications. [14] The tandem MS high-resolution ESI-QTOF MS/MS data on now over 930,000 distinct chemical entities, includes mass spectral collision-induced dissociation data at four different collision energies, in both positive and negative ionization modes. [7] [9] [15]

Related Research Articles

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<span class="mw-page-title-main">Tandem mass spectrometry</span> Type of mass spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is a technique in instrumental analysis where two or more stages of analysis using one or more mass analyzer are performed with an additional reaction step in between these analyses to increase their abilities to analyse chemical samples. A common use of tandem MS is the analysis of biomolecules, such as proteins and peptides.

<span class="mw-page-title-main">Gas chromatography–mass spectrometry</span> Analytical method

Gas chromatography–mass spectrometry (GC–MS) is an analytical method that combines the features of gas-chromatography and mass spectrometry to identify different substances within a test sample. Applications of GC–MS include drug detection, fire investigation, environmental analysis, explosives investigation, food and flavor analysis, and identification of unknown samples, including that of material samples obtained from planet Mars during probe missions as early as the 1970s. GC–MS can also be used in airport security to detect substances in luggage or on human beings. Additionally, it can identify trace elements in materials that were previously thought to have disintegrated beyond identification. Like liquid chromatography–mass spectrometry, it allows analysis and detection even of tiny amounts of a substance.

<span class="mw-page-title-main">Lipidomics</span>

Lipidomics is the large-scale study of pathways and networks of cellular lipids in biological systems The word "lipidome" is used to describe the complete lipid profile within a cell, tissue, organism, or ecosystem and is a subset of the "metabolome" which also includes other major classes of biological molecules. Lipidomics is a relatively recent research field that has been driven by rapid advances in technologies such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, fluorescence spectroscopy, dual polarisation interferometry and computational methods, coupled with the recognition of the role of lipids in many metabolic diseases such as obesity, atherosclerosis, stroke, hypertension and diabetes. This rapidly expanding field complements the huge progress made in genomics and proteomics, all of which constitute the family of systems biology.

<span class="mw-page-title-main">Metabolomics</span> Scientific study of chemical processes involving metabolites

Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. Specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. Messenger RNA (mRNA), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are indeed quantifiable correlations between the metabolome and the other cellular ensembles, which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology.

<span class="mw-page-title-main">Metabolome</span>

The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.

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<span class="mw-page-title-main">Liquid chromatography–mass spectrometry</span> Analytical chemistry technique

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<span class="mw-page-title-main">Fragmentation (mass spectrometry)</span>

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<span class="mw-page-title-main">Surface-assisted laser desorption/ionization</span>

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<span class="mw-page-title-main">Single-cell analysis</span> Testbg biochemical processes and reactions in an individual cell

In the field of cellular biology, single-cell analysis and subcellular analysis is the study of genomics, transcriptomics, proteomics, metabolomics and cell–cell interactions at the single cell level. The concept of single-cell analysis originated in the 1970s. Before the discovery of heterogeneity, single-cell analysis mainly referred to the analysis or manipulation of an individual cell in a bulk population of cells at a particular condition using optical or electronic microscope. To date, due to the heterogeneity seen in both eukaryotic and prokaryotic cell populations, analyzing a single cell makes it possible to discover mechanisms not seen when studying a bulk population of cells. Technologies such as fluorescence-activated cell sorting (FACS) allow the precise isolation of selected single cells from complex samples, while high throughput single cell partitioning technologies, enable the simultaneous molecular analysis of hundreds or thousands of single unsorted cells; this is particularly useful for the analysis of transcriptome variation in genotypically identical cells, allowing the definition of otherwise undetectable cell subtypes. The development of new technologies is increasing our ability to analyze the genome and transcriptome of single cells, as well as to quantify their proteome and metabolome. Mass spectrometry techniques have become important analytical tools for proteomic and metabolomic analysis of single cells. Recent advances have enabled quantifying thousands of protein across hundreds of single cells, and thus make possible new types of analysis. In situ sequencing and fluorescence in situ hybridization (FISH) do not require that cells be isolated and are increasingly being used for analysis of tissues.

The Yeast Metabolome Database (YMDB) is a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in or produced by Saccharomyces cerevisiae. The YMDB was designed to facilitate yeast metabolomics research, specifically in the areas of general fermentation as well as wine, beer and fermented food analysis. YMDB supports the identification and characterization of yeast metabolites using NMR spectroscopy, GC-MS spectrometry and Liquid chromatography–mass spectrometry. The YMDB contains two kinds of data: 1) chemical data and 2) molecular biology/biochemistry data. The chemical data includes 2027 metabolite structures with detailed metabolite descriptions along with nearly 4000 NMR, GC-MS and LC/MS spectra.

<span class="mw-page-title-main">Desorption/ionization on silicon</span> Soft laser desorption method

Desorption/ionization on silicon (DIOS) is a soft laser desorption method used to generate gas-phase ions for mass spectrometry analysis. DIOS is considered the first surface-based surface-assisted laser desorption/ionization (SALDI-MS) approach. Prior approaches were accomplished using nanoparticles in a matrix of glycerol, while DIOS is a matrix-free technique in which a sample is deposited on a nanostructured surface and the sample desorbed directly from the nanostructured surface through the adsorption of laser light energy. DIOS has been used to analyze organic molecules, metabolites, biomolecules and peptides, and, ultimately, to image tissues and cells.

<span class="mw-page-title-main">Gary Siuzdak</span> American chemist

Gary Siuzdak is an American chemist best known for his work in the field of metabolomics, activity metabolomics, and mass spectrometry. His lab discovered indole-3-propionic acid as a gut bacteria derived metabolite in 2009. He is currently the Professor and Director of The Center for Metabolomics and Mass Spectrometry at Scripps Research in La Jolla, California. Siuzdak has also made contributions to virus analysis, viral structural dynamics, as well as developing mass spectrometry imaging technology using nanostructured surfaces. The Siuzdak lab is also responsible for creating the research tools eXtensible Computational Mass Spectrometry (XCMS), METLIN, METLIN Neutral Loss and Q-MRM. As of January 2021, the XCMS/METLIN platform has over 50,000 registered users.

<span class="mw-page-title-main">XCMS Online</span> Bioinformatics software

XCMS Online is a cloud version of the original eXtensible Computational Mass Spectrometry (XCMS) technology, created by the Siuzdak Lab at Scripps Research. XCMS introduced the concept of nonlinear retention time alignment that allowed for the statistical assessment of the detected peaks across LCMS and GCMS datasets. XCMS Online was designed to facilitate XCMS analyses through a cloud portal and as a more straightforward way to analyze, visualize and share untargeted metabolomic data. Further to this, the combination of XCMS and METLIN allows for the identification of known molecules using METLIN's tandem mass spectrometry data, and enables the identification of unknown via similarity searching of tandem mass spectrometry data. XCMS Online has also become a systems biology tool for integrating different omic data sets. As of January 2021, the XCMSOnline - METLIN platform has over 44,000 registered users. XCMS - METLIN was recognized in 2023 as the year's top analytical innovation.

<span class="mw-page-title-main">Emma Schymanski</span> Chemist

Emma Schymanski is chemist known for her work identifying unknown organic compounds, particularly pollutants, and is an advocate for open science.

SIRIUS is a Java-based open-source software for the identification of small molecules from fragmentation mass spectrometry data without the use of spectral libraries. It combines the analysis of isotope patterns in MS1 spectra with the analysis of fragmentation patterns in MS2 spectra. SIRIUS is the umbrella application comprising CSI:FingerID, CANOPUS, COSMIC and ZODIAC.

References

  1. ↑ Xue, Jingchuan; Guijas, Carlos; Benton, H. Paul; Warth, Benedikt; Siuzdak, Gary (October 2020). "METLIN MS 2 molecular standards database: a broad chemical and biological resource". Nature Methods. 17 (10): 953–954. doi:10.1038/s41592-020-0942-5. ISSN   1548-7105. PMC   8802982 . PMID   32839599.
  2. 1 2 Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, et al. (December 2005). "METLIN: a metabolite mass spectral database". Therapeutic Drug Monitoring. 27 (6): 747–51. doi:10.1097/01.ftd.0000179845.53213.39. PMID   16404815. S2CID   14774455.
  3. 1 2 3 Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, et al. (March 2018). "METLIN: A Technology Platform for Identifying Knowns and Unknowns". Analytical Chemistry. 90 (5): 3156–3164. doi:10.1021/acs.analchem.7b04424. PMC   5933435 . PMID   29381867.
  4. ↑ Hoang, Corey; Uritboonthai, Winnie; Hoang, Linh; Billings, Elizabeth M.; Aisporna, Aries; Nia, Farshad A.; Derks, Rico J. E.; Williamson, James R.; Giera, Martin; Siuzdak, Gary (2024-03-26). "Tandem Mass Spectrometry across Platforms". Analytical Chemistry. doi:10.1021/acs.analchem.3c05576. ISSN   0003-2700.
  5. 1 2 Aisporna, Aries; Benton, H. Paul; Chen, Andy; Derks, Rico J. E.; Galano, Jean Marie; Giera, Martin; Siuzdak, Gary (2022-03-02). "Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in METLIN". Journal of the American Society for Mass Spectrometry. 33 (3): 530–534. doi:10.1021/jasms.1c00343. ISSN   1044-0305. PMC   10131246 . PMID   35174708. S2CID   246902725.
  6. ↑ Giera, Martin; Yanes, Oscar; Siuzdak, Gary (2022-01-04). "Metabolite discovery: Biochemistry's scientific driver". Cell Metabolism. 34 (1): 21–34. doi: 10.1016/j.cmet.2021.11.005 . hdl: 1887/3250578 . ISSN   1550-4131. PMID   34986335. S2CID   245729571.
  7. 1 2 3 4 5 Xue J, Guijas C, Benton HP, Warth B, Siuzdak G (August 2020). "2 molecular standards database: a broad chemical and biological resource". Nature Methods. 17 (10): 953–954. doi:10.1038/s41592-020-0942-5. PMC   8802982 . PMID   32839599.
  8. ↑ Guijas, Carlos; To, Andrew; Montenegro-Burke, J. Rafael; Domingo-Almenara, Xavier; Alipio-Gloria, Zaida; Kok, Bernard P.; Saez, Enrique; Alvarez, Nicole H.; Johnson, Kristen A.; Siuzdak, Gary (August 2022). "Drug-Initiated Activity Metabolomics Identifies Myristoylglycine as a Potent Endogenous Metabolite for Human Brown Fat Differentiation". Metabolites. 12 (8): 749. doi: 10.3390/metabo12080749 . ISSN   2218-1989. PMC   9415469 . PMID   36005620.
  9. 1 2 "The Analytical Scientist Innovation Awards 2023". The Analytical Scientist. 2023-12-12. Retrieved 2023-12-14.
  10. ↑ Heim, Wilasinee; Aisporna, Aries; Hoang, Linh; Benton, H. Paul; Siuzdak, Gary (2023), Ivanisevic, Julijana; Giera, Martin (eds.), "METLIN Tandem Mass Spectrometry and Neutral Loss Databases for the Identification of Microbial Natural Products and Other Chemical Entities", A Practical Guide to Metabolomics Applications in Health and Disease: From Samples to Insights into Metabolism, Cham: Springer International Publishing, pp. 105–124, doi:10.1007/978-3-031-44256-8_5, ISBN   978-3-031-44256-8 , retrieved 2024-03-27
  11. 1 2 Benton HP, Wong DM, Trauger SA, Siuzdak G (August 2008). "XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization". Analytical Chemistry. 80 (16): 6382–9. doi:10.1021/ac800795f. PMC   2728033 . PMID   18627180.
  12. 1 2 Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (September 2012). "An accelerated workflow for untargeted metabolomics using the METLIN database". Nature Biotechnology. 30 (9): 826–8. doi:10.1038/nbt.2348. PMC   3666346 . PMID   22965049.
  13. 1 2 Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Ivanisevic, Julijana; Thomas, Aurelien; Sidibé, Jonathan; Teav, Tony; Guijas, Carlos; Aisporna, Aries E.; Rinehart, Duane; Hoang, Linh; Nordström, Anders (September 2018). "XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules". Nature Methods. 15 (9): 681–684. doi:10.1038/s41592-018-0110-3. ISSN   1548-7105. PMC   6629029 . PMID   30150755.
  14. ↑ Majumder, Erica L.-W.; Billings, Elizabeth M.; Benton, H. Paul; Martin, Richard L.; Palermo, Amelia; Guijas, Carlos; Rinschen, Markus M.; Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Tagtow, Bradley A.; Plumb, Robert S. (2021-01-22). "Cognitive analysis of metabolomics data for systems biology". Nature Protocols. 16 (3): 1376–1418. doi:10.1038/s41596-020-00455-4. ISSN   1754-2189. OSTI   1774918. PMC   10357461 . PMID   33483720. S2CID   231687415.
  15. ↑ Heim, Wilasinee; Aisporna, Aries; Hoang, Linh; Benton, H. Paul; Siuzdak, Gary (2023), Ivanisevic, Julijana; Giera, Martin (eds.), "METLIN Tandem Mass Spectrometry and Neutral Loss Databases for the Identification of Microbial Natural Products and Other Chemical Entities", A Practical Guide to Metabolomics Applications in Health and Disease: From Samples to Insights into Metabolism, Learning Materials in Biosciences, Cham: Springer International Publishing, pp. 105–124, doi:10.1007/978-3-031-44256-8_5, ISBN   978-3-031-44256-8 , retrieved 2024-03-01
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