View source for Metabolomics

{{Short description|Scientific study of chemical processes involving metabolites}}
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[[File:The central dogma of biology showing the flow of information from DNA to the phenotype. Associated with each stage is the corresponding systems biology tool from genomics to genomics to metabolomics.png|thumb|The central [[principle]] of biology showing the flow of information from [[DNA]] to the [[phenotype]]. Associated with each stage is the corresponding systems biology tool, from [[genomics]] to metabolomics.]]

'''Metabolomics''' is the scientific study of chemical processes involving [[metabolite]]s, 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.<ref>{{cite journal | vauthors = Daviss B  |date= April 2005 |title=Growing pains for metabolomics |journal=The Scientist |volume=19 |issue=8 |pages=25–28 |url=http://www.the-scientist.com/article/display/15427/ | archive-url = https://web.archive.org/web/20081013014601/http://www.the-scientist.com/article/display/15427/ | archive-date = 13 October 2008 }}</ref> The [[metabolome]] represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.<ref>{{cite journal | vauthors = Jordan KW, Nordenstam J, Lauwers GY, Rothenberger DA, Alavi K, Garwood M, Cheng LL | title = Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy | journal = Diseases of the Colon and Rectum | volume = 52 | issue = 3 | pages = 520–525 | date = March 2009 | pmid = 19333056 | pmc = 2720561 | doi = 10.1007/DCR.0b013e31819c9a2c }}</ref> [[Messenger RNA]] (mRNA), [[gene expression]] data, and [[proteomics|proteomic]] analyses reveal the set of [[gene product]]s 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,<ref name="Villate_2021">{{cite journal | vauthors = Villate A, San Nicolas M, Gallastegi M, Aulas PA, Olivares M, Usobiaga A, Etxebarria N, Aizpurua-Olaizola O | title = Review: Metabolomics as a prediction tool for plants performance under environmental stress | journal = Plant Science | volume = 303 | article-number = 110789 | date = February 2021 | pmid = 33487364 | doi = 10.1016/j.plantsci.2020.110789 | bibcode = 2021PlnSc.30310789V | s2cid = 230533604 }}</ref> and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism.<ref name="Hollywood_2006">{{cite journal | vauthors = Hollywood K, Brison DR, Goodacre R | title = Metabolomics: current technologies and future trends | journal = Proteomics | volume = 6 | issue = 17 | pages = 4716–4723 | date = September 2006 | pmid = 16888765 | doi = 10.1002/pmic.200600106 | s2cid = 14631544 }}</ref> There are indeed quantifiable correlations between the metabolome and the other cellular ensembles ([[genome]], [[transcriptome]], [[proteome]], and [[lipidome]]), which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances.<ref>{{cite journal | vauthors = Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM | title = Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines | journal = International Journal of Molecular Sciences | volume = 23 | issue = 7 | page = 3867 | date = March 2022 | pmid = 35409231 | pmc = 8998886 | doi = 10.3390/ijms23073867 | doi-access = free }}</ref> One of the ultimate challenges of [[systems biology]] is to integrate metabolomics with all other [[Omics|-omics]] information to provide a better understanding of cellular biology.

== History ==
The concept that individuals might have a "metabolic profile" that could be reflected in the makeup of their biological fluids was introduced by Roger Williams in the late 1940s,<ref>{{cite journal | vauthors = Gates SC, Sweeley CC | title = Quantitative metabolic profiling based on gas chromatography | journal = Clinical Chemistry | volume = 24 | issue = 10 | pages = 1663–1673 | date = October 1978 | pmid = 359193 | doi = 10.1093/clinchem/24.10.1663 }}</ref> who used [[paper chromatography]] to suggest characteristic metabolic patterns in urine and saliva were associated with diseases such as [[schizophrenia]]. However, it was only through technological advancements in the 1960s and 1970s that it became feasible to quantitatively (as opposed to qualitatively) measure metabolic profiles.<ref>{{cite journal | vauthors = Preti, George | title = Metabolomics comes of age? | journal = The Scientist | volume = 19 | issue = 11 | page = 8 | date = June 6, 2005 | url=https://www.the-scientist.com/letter/metabolomics-comes-of-age-48727}}</ref> The term "metabolic profile" was introduced by Horning, ''et al.'' in 1971 after they demonstrated that [[gas chromatography-mass spectrometry]] (GC-MS) could be used to measure compounds present in human urine and tissue extracts.<ref name=VDGarticle>{{cite journal | vauthors = van der Greef J, Smilde AK | title = Symbiosis of chemometrics and metabolomics: past, present, and future. | journal = Journal of Chemometrics | date = May 2005 | volume = 19 | issue = 5–7 | pages = 376–386 | doi = 10.1002/cem.941 | s2cid = 122419960 | doi-access = free }}</ref><ref name="pmid2603028">{{cite journal | vauthors = Shapiro I, Kavkalo DN, Petrova GV, Ganzin AP | title = [Angioleiomyoma of the large-intestinal mesentery complicated by diffuse peritonitis] | language = Russian | journal = Sovetskaia Meditsina | volume = | issue = 9 | page = 116 | date = 1989 | pmid = 2603028 | doi = | url = }}</ref> The Horning group, along with that of [[Linus Pauling]] and [[Arthur B. Robinson]] led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s.<ref>{{cite journal | vauthors = Griffiths WJ, Wang Y | title = Mass spectrometry: from proteomics to metabolomics and lipidomics | journal = Chemical Society Reviews | volume = 38 | issue = 7 | pages = 1882–1896 | date = July 2009 | pmid = 19551169 | doi = 10.1039/b618553n | s2cid = 12237358 }}</ref>

Concurrently, [[Nuclear magnetic resonance spectroscopy|NMR spectroscopy]], which was discovered in the 1940s, was also undergoing rapid advances. In 1974, Seeley et al. demonstrated the utility of using NMR to detect metabolites in unmodified biological samples.<ref>{{cite journal | vauthors = Hoult DI, Busby SJ, Gadian DG, Radda GK, Richards RE, Seeley PJ | title = Observation of tissue metabolites using 31P nuclear magnetic resonance | journal = Nature | volume = 252 | issue = 5481 | pages = 285–287 | date = November 1974 | pmid = 4431445 | doi = 10.1038/252285a0 | s2cid = 4291661 | bibcode = 1974Natur.252..285H }}</ref> This first study on muscle highlighted the value of NMR in that it was determined that 90% of cellular [[Adenosine triphosphate|ATP]] is complexed with magnesium. As sensitivity has improved with the evolution of higher magnetic field strengths and [[magic angle spinning]], NMR continues to be a leading analytical tool to investigate metabolism.<ref name=VDGarticle/><ref name=JKNnature>{{cite journal | vauthors = Nicholson JK, Lindon JC | title = Systems biology: Metabonomics | journal = Nature | volume = 455 | issue = 7216 | pages = 1054–1056 | date = October 2008 | pmid = 18948945 | doi = 10.1038/4551054a | s2cid = 4411723 | bibcode = 2008Natur.455.1054N }}</ref> Recent efforts to utilize NMR for metabolomics have been largely driven by the laboratory of [[Jeremy K. Nicholson]] at [[Birkbeck College, University of London]] and later at [[Imperial College London]]. In 1984, Nicholson showed <sup>1</sup>H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data.<ref name="pmid12537357">{{cite journal | vauthors = Holmes E, Antti H | title = Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra | journal = The Analyst | volume = 127 | issue = 12 | pages = 1549–1557 | date = December 2002 | pmid = 12537357 | doi = 10.1039/b208254n | bibcode = 2002Ana...127.1549H }}</ref><ref>{{cite journal | vauthors = Lenz EM, Wilson ID | title = Analytical strategies in metabonomics | journal = Journal of Proteome Research | volume = 6 | issue = 2 | pages = 443–458 | date = February 2007 | pmid = 17269702 | doi = 10.1021/pr0605217 }}</ref>

In 1994 and 1996, [[Mass spectrometry#Liquid chromatography|liquid chromatography mass spectrometry]] metabolomics experiments<ref>{{cite journal | vauthors = Lerner RA, Siuzdak G, Prospero-Garcia O, Henriksen SJ, Boger DL, Cravatt BF | title = Cerebrodiene: a brain lipid isolated from sleep-deprived cats | journal = Proceedings of the National Academy of Sciences of the United States of America | volume = 91 | issue = 20 | pages = 9505–9508 | date = September 1994 | pmid = 7937797 | pmc = 44841 | doi = 10.1073/pnas.91.20.9505 | doi-access = free | bibcode = 1994PNAS...91.9505L }}</ref><ref>{{cite journal | vauthors = Cravatt BF, Prospero-Garcia O, Siuzdak G, Gilula NB, Henriksen SJ, Boger DL, Lerner RA | title = Chemical characterization of a family of brain lipids that induce sleep | journal = Science | volume = 268 | issue = 5216 | pages = 1506–1509 | date = June 1995 | pmid = 7770779 | doi = 10.1126/science.7770779 | bibcode = 1995Sci...268.1506C }}</ref> were performed by [[Gary Siuzdak]] while working with [[Richard Lerner]] (then president of the [[Scripps Research|Scripps Research Institute]]) and [[Benjamin Cravatt III|Benjamin Cravatt]], to analyze the [[Cerebrospinal fluid|cerebral spinal fluid]] from sleep deprived animals. One molecule of particular interest, [[oleamide]], was observed and later shown to have sleep inducing properties. This work is one of the earliest such experiments combining liquid chromatography and mass spectrometry in metabolomics.

In 2005, the first metabolomics [[tandem mass spectrometry]] database, [[METLIN]],<ref name="Smith_2005">{{cite journal | vauthors = Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G | title = METLIN: a metabolite mass spectral database | journal = Therapeutic Drug Monitoring | volume = 27 | issue = 6 | pages = 747–751 | date = December 2005 | pmid = 16404815 | doi = 10.1097/01.ftd.0000179845.53213.39 | s2cid = 14774455 }}</ref><ref name="Guijas_2018">{{cite journal | vauthors = Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, Koellensperger G, Huan T, Uritboonthai W, Aisporna AE, Wolan DW, Spilker ME, Benton HP, Siuzdak G | title = METLIN: A Technology Platform for Identifying Knowns and Unknowns | journal = Analytical Chemistry | volume = 90 | issue = 5 | pages = 3156–3164 | date = March 2018 | pmid = 29381867 | pmc = 5933435 | doi = 10.1021/acs.analchem.7b04424 | bibcode = 2018AnaCh..90.3156G }}</ref><ref>{{Cite journal |last=Aisporna |first=Aries |last2=Webb |first2=Bill |last3=Uritboonthai |first3=Winnie |last4=Hoang |first4=Linh |last5=Billings |first5=Elizabeth M. |last6=Hoang |first6=Corey |last7=Plumb |first7=Robert |last8=Siuzdak |first8=Gary |date=2025-10-28 |title=Adaptive Modeling of Tandem Mass Spectrometry Data: Creation of the METLIN 960K MRM Database |url=https://doi.org/10.1021/acs.analchem.5c04639 |journal=Analytical Chemistry |doi=10.1021/acs.analchem.5c04639 |issn=0003-2700|doi-access=free }}</ref> for characterizing human metabolites was developed in the [[Gary Siuzdak|Siuzdak]] laboratory at the [[Scripps Research|Scripps Research Institute]]. METLIN has since grown and as of December, 2023, METLIN contains MS/MS experimental data on over 960,000 molecular standards and other chemical entities,<ref>{{Cite web |date=2023-12-12 |title=The Analytical Scientist Innovation Awards 2023 |url=https://theanalyticalscientist.com/techniques-tools/the-analytical-scientist-innovation-awards-2023 |access-date=2023-12-14 |website=The Analytical Scientist |language=en}}</ref> each compound having experimental tandem mass spectrometry data generated from molecular standards at multiple collision energies and in positive and negative ionization modes. METLIN is the largest repository of tandem mass spectrometry data of its kind. The dedicated academic journal Metabolomics first appeared in 2005, founded by its current editor-in-chief [[Roy Goodacre]].

In 2005, the [[Gary Siuzdak|Siuzdak]] lab was engaged in identifying metabolites associated with [[sepsis]] and in an effort to address the issue of statistically identifying the most relevant dysregulated metabolites across hundreds of LC/MS datasets, the first algorithm was developed to allow for the nonlinear alignment of mass spectrometry metabolomics data. Called XCMS,<ref name="Smith_2006">{{cite journal | vauthors = Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G | title = XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification | journal = Analytical Chemistry | volume = 78 | issue = 3 | pages = 779–787 | date = February 2006 | pmid = 16448051 | doi = 10.1021/ac051437y | bibcode = 2006AnaCh..78..779S }}</ref> it has since (2012)<ref name="Tautenhahn_2012">{{cite journal | vauthors = Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G | title = XCMS Online: a web-based platform to process untargeted metabolomic data | journal = Analytical Chemistry | volume = 84 | issue = 11 | pages = 5035–5039 | date = June 2012 | pmid = 22533540 | pmc = 3703953 | doi = 10.1021/ac300698c | bibcode = 2012AnaCh..84.5035T }}</ref> been developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.

On 23 January 2007, the [[Human Metabolome Project]], led by [[David S. Wishart]], completed the first draft of the human metabolome, consisting of a database of approximately 2,500 metabolites, 1,200 drugs and 3,500 food components.<ref name=Wishart2007>{{cite HMDB|1.0}}</ref><ref>{{cite HMDB|2.0}}</ref> Similar projects have been underway in several plant species, most notably ''[[Medicago truncatula]]''<ref>{{cite journal | vauthors = Farag MA, Huhman DV, Dixon RA, Sumner LW | title = Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures | journal = Plant Physiology | volume = 146 | issue = 2 | pages = 387–402 | date = February 2008 | pmid = 18055588 | pmc = 2245840 | doi = 10.1104/pp.107.108431 }}</ref> and ''[[Arabidopsis thaliana]]''<ref>{{Cite web|url=http://plantmetabolomics.vrac.iastate.edu/ver2/datasets/overview.php|title=www.Plantmetabolomics.org|date=Nov 7, 2012|archive-url=https://web.archive.org/web/20121107010432/http://plantmetabolomics.vrac.iastate.edu/ver2/datasets/overview.php|access-date=May 20, 2020|archive-date=2012-11-07}}</ref> for several years.

As late as mid-2010, metabolomics was still considered an "emerging field".<ref name=Morrow2010>{{Cite news | vauthors = Morrow Jr KJ | date = 1 April 2010 | access-date = 28 June 2010 | title = Mass Spec Central to Metabolomics | periodical = Genetic Engineering & Biotechnology News | volume = 30 | issue = 7 | page = 1 | url = http://www.genengnews.com/gen-articles/mass-spec-central-to-metabolomics/3229/ | archive-url = https://web.archive.org/web/20110812181848/http://www.genengnews.com/keywordsandtools/print/1/13148/ | archive-date = 12 August 2011 | url-status = live }}</ref> Further, it was noted that further progress in the field depended in large part, through addressing otherwise "irresolvable technical challenges", by technical evolution of [[mass spectrometry]] instrumentation.<ref name=Morrow2010/>

In 2015, real-time metabolome profiling was demonstrated for the first time.<ref>{{Cite web|url=https://phys.org/news/2015-09-real-time-analysis-metabolic-products.html|title=Real-time analysis of metabolic products|website=phys.org|access-date=May 20, 2020}}</ref>

== Metabolome ==
[[File:Human_metabolome_project.png|thumb|right|The human metabolome project]]
{{See also|Metabolome|Human Metabolome Database}}
The [[metabolome]] refers to the complete set of small-molecule (<1.5 kDa)<ref name=Wishart2007/> metabolites (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites) to be found within a biological sample, such as a single organism.<ref>{{cite journal | vauthors = Oliver SG, Winson MK, Kell DB, Baganz F | title = Systematic functional analysis of the yeast genome | journal = Trends in Biotechnology | volume = 16 | issue = 9 | pages = 373–378 | date = September 1998 | pmid = 9744112 | doi = 10.1016/S0167-7799(98)01214-1 }}</ref><ref>{{cite journal | vauthors = Griffin JL, Vidal-Puig A | title = Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding? | journal = Physiological Genomics | volume = 34 | issue = 1 | pages = 1–5 | date = June 2008 | pmid = 18413782 | doi = 10.1152/physiolgenomics.00009.2008 | s2cid = 9416755 }}</ref> The word was coined in analogy with [[transcriptomics]] and [[proteomics]]; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method.

In January 2007, scientists at the [[University of Alberta]] and the [[University of Calgary]] completed the first draft of the human metabolome. The [[Human Metabolome Database]] (HMDB) is perhaps the most extensive public metabolomic spectral database to date<ref>HMDB 4.0 – the human metabolome database in 2018.</ref> and is a freely available electronic database (www.hmdb.ca) containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. The database is designed to contain or link three kinds of data:
# Chemical data,
# Clinical data and
# Molecular biology/biochemistry data.
The database contains 220,945 metabolite entries including both water-soluble and lipid soluble metabolites. Additionally, 8,610 protein sequences (enzymes and transporters) are linked to these metabolite entries. Each MetaboCard entry contains 130 data fields with 2/3 of the information being devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data.<ref>{{cite journal | vauthors = Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Dizon R, Sayeeda Z, Tian S, Lee BL, Berjanskii M, Mah R, Yamamoto M, Jovel J, Torres-Calzada C, Hiebert-Giesbrecht M, Lui VW, Varshavi D, Varshavi D, Allen D, Arndt D, Khetarpal N, Sivakumaran A, Harford K, Sanford S, Yee K, Cao X, Budinski Z, Liigand J, Zhang L, Zheng J, Mandal R, Karu N, Dambrova M, Schiöth HB, Greiner R, Gautam V | title = HMDB 5.0: the Human Metabolome Database for 2022 | journal = Nucleic Acids Research | volume = 50 | issue = D1 | pages = D622–D631 | date = January 2022 | pmid = 34986597 | pmc = 8728138 | doi = 10.1093/nar/gkab1062 }}</ref> The version 3.5 of the [[Human Metabolome Database|HMDB]] contains >16,000 endogenous metabolites, >1,500 drugs and >22,000 food constituents or food metabolites.<ref name=ref_23161693>{{cite journal | vauthors = Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A | title = HMDB 3.0—The Human Metabolome Database in 2013 | journal = Nucleic Acids Research | volume = 41 | issue = Database issue | pages = D801–D807 | date = January 2013 | pmid = 23161693 | pmc = 3531200 | doi = 10.1093/nar/gks1065 }}</ref> This information, available at the Human Metabolome Database and based on analysis of information available in the current scientific literature, is far from complete.<ref>{{cite journal | vauthors = Pearson H | title = Meet the human metabolome | journal = Nature | volume = 446 | issue = 7131 | page = 8 | date = March 2007 | pmid = 17330009 | doi = 10.1038/446008a | s2cid = 2235062 | doi-access = free | bibcode = 2007Natur.446....8P }}</ref> In contrast, much more is known about the metabolomes of other organisms. For example, over 50,000 metabolites have been characterized from the plant kingdom, and many thousands of metabolites have been identified and/or characterized from single plants.<ref>{{cite journal | vauthors = De Luca V, St Pierre B | title = The cell and developmental biology of alkaloid biosynthesis | journal = Trends in Plant Science | volume = 5 | issue = 4 | pages = 168–173 | date = April 2000 | pmid = 10740298 | doi = 10.1016/S1360-1385(00)01575-2 }}</ref><ref>{{cite journal | vauthors = Griffin JL, Shockcor JP | title = Metabolic profiles of cancer cells | journal = Nature Reviews. Cancer | volume = 4 | issue = 7 | pages = 551–561 | date = July 2004 | pmid = 15229480 | doi = 10.1038/nrc1390 | s2cid = 527894 }}</ref>

Each type of cell and tissue has a unique metabolic 'fingerprint' that can elucidate organ or tissue-specific information. Bio-specimens used for metabolomics analysis include but not limit to plasma, serum, urine, saliva, feces, muscle, sweat, exhaled breath and gastrointestinal fluid.<ref name="Ulaszewska_2019">{{cite journal | vauthors = Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, Brennan L, Brunius C, Bub A, Capozzi F, Cialiè Rosso M, Cordero CE, Daniel H, Durand S, Egert B, Ferrario PG, Feskens EJ, Franceschi P, Garcia-Aloy M, Giacomoni F, Giesbertz P, González-Domínguez R, Hanhineva K, Hemeryck LY, Kopka J, Kulling SE, Llorach R, Manach C, Mattivi F, Migné C, Münger LH, Ott B, Picone G, Pimentel G, Pujos-Guillot E, Riccadonna S, Rist MJ, Rombouts C, Rubert J, Skurk T, Sri Harsha PS, Van Meulebroek L, Vanhaecke L, Vázquez-Fresno R, Wishart D, Vergères G | title = Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies | journal = Molecular Nutrition & Food Research | volume = 63 | issue = 1 | pages = e1800384 | date = January 2019 | article-number = 1800384 | pmid = 30176196 | doi = 10.1002/mnfr.201800384 | doi-access = free | hdl = 11572/214273 | hdl-access = free }}</ref> The ease of collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole.<ref name="Nicholson 2003 668–676">{{cite journal | vauthors = Nicholson JK, Wilson ID | title = Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism | journal = Nature Reviews. Drug Discovery | volume = 2 | issue = 8 | pages = 668–676 | date = August 2003 | pmid = 12904817 | doi = 10.1038/nrd1157 | s2cid = 23743031 }}</ref> [[Genome]] can tell what could happen, [[transcriptome]] can tell what appears to be happening, [[proteome]] can tell what makes it happen and metabolome can tell what has happened and what is happening.<ref name="Dettmer_2007"/>

== Metabolites ==
{{Microbiomes|related}}
[[Metabolite]]s are the substrates, intermediates and products of [[metabolism]]. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1.5 [[Dalton (unit)|kDa]] in size.<ref name=Wishart2007 /> However, there are exceptions to this depending on the sample and detection method. For example, macromolecules such as [[lipoprotein]]s and [[albumin]] are reliably detected in [[Nuclear magnetic resonance|NMR]]-based metabolomics studies of blood plasma.<ref>{{cite journal | vauthors = Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC | title = 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma | journal = Analytical Chemistry | volume = 67 | issue = 5 | pages = 793–811 | date = March 1995 | pmid = 7762816 | doi = 10.1021/ac00101a004 | bibcode = 1995AnaCh..67..793N }}</ref> In plant-based metabolomics, it is common to refer to "primary" and "secondary" metabolites.<ref name="Villate_2021" /> A primary metabolite is directly involved in the normal growth, development, and reproduction. A [[secondary metabolite]] is not directly involved in those processes, but usually has important [[Ecology|ecological]] function. Examples include [[antibiotics]] and [[pigment]]s.<ref>{{cite journal | vauthors = Bentley R | title = Secondary metabolite biosynthesis: the first century | journal = Critical Reviews in Biotechnology | volume = 19 | issue = 1 | pages = 1–40 | year = 1999 | pmid = 10230052 | doi = 10.1080/0738-859991229189 }}</ref> By contrast, in human-based metabolomics, it is more common to describe metabolites as being either [[endogenous]] (produced by the host organism) or [[exogenous]].<ref>{{cite journal | vauthors = Nordström A, O'Maille G, Qin C, Siuzdak G | title = Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum | journal = Analytical Chemistry | volume = 78 | issue = 10 | pages = 3289–3295 | date = May 2006 | pmid = 16689529 | pmc = 3705959 | doi = 10.1021/ac060245f }}</ref><ref>{{cite journal | vauthors = Lin W, Conway LP, Block A, Sommi G, Vujasinovic M, Löhr JM, Globisch D | title = Sensitive mass spectrometric analysis of carbonyl metabolites in human urine and fecal samples using chemoselective modification | journal = The Analyst | volume = 145 | issue = 11 | pages = 3822–3831 | date = June 2020 | pmid = 32393929 | doi = 10.1039/D0AN00150C | doi-access = free | bibcode = 2020Ana...145.3822L }}</ref> Metabolites of foreign substances such as drugs are termed xenometabolites.<ref>{{cite journal | vauthors = Crockford DJ, Maher AD, Ahmadi KR, Barrett A, Plumb RS, Wilson ID, Nicholson JK | title = 1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies | journal = Analytical Chemistry | volume = 80 | issue = 18 | pages = 6835–6844 | date = September 2008 | pmid = 18700783 | doi = 10.1021/ac801075m | bibcode = 2008AnaCh..80.6835C }}</ref>

The [[metabolome]] derives from a large network of [[metabolic]] reactions, where outputs from one [[enzymatic]] [[chemical reaction]] are inputs to other chemical reactions. Such systems have been described as [[hypercycle (chemistry)|hypercycle]]s.{{Citation needed|date=January 2010}}

== Metabonomics ==
<!-- This section is linked from redirect "[[Metabonomics]]" -->
Metabonomics is defined as "the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification". The word origin is from the Greek ''μεταβολή'' meaning change and ''nomos'' meaning a rule set or set of laws.<ref>{{cite journal | vauthors = Nicholson JK | title = Global systems biology, personalized medicine and molecular epidemiology | journal = Molecular Systems Biology | volume = 2 | issue = 1 | article-number = 52 | year = 2006 | pmid = 17016518 | pmc = 1682018 | doi = 10.1038/msb4100095 }}</ref> This approach was pioneered by Jeremy Nicholson at [[Murdoch University]] and has been used in toxicology, disease diagnosis and a number of other fields. Historically, the metabonomics approach was one of the first methods to apply the scope of systems biology to studies of metabolism.<ref>{{cite journal | vauthors = Nicholson JK, Lindon JC, Holmes E | title = 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data | journal = Xenobiotica; the Fate of Foreign Compounds in Biological Systems | volume = 29 | issue = 11 | pages = 1181–1189 | date = November 1999 | pmid = 10598751 | doi = 10.1080/004982599238047 }}</ref><ref>{{cite journal | vauthors = Nicholson JK, Connelly J, Lindon JC, Holmes E | title = Metabonomics: a platform for studying drug toxicity and gene function | journal = Nature Reviews. Drug Discovery | volume = 1 | issue = 2 | pages = 153–161 | date = February 2002 | pmid = 12120097 | doi = 10.1038/nrd728 | s2cid = 17881327 }}</ref><ref>{{cite journal | vauthors = Holmes E, Wilson ID, Nicholson JK | title = Metabolic phenotyping in health and disease | journal = Cell | volume = 134 | issue = 5 | pages = 714–717 | date = September 2008 | pmid = 18775301 | doi = 10.1016/j.cell.2008.08.026 | s2cid = 6677621 | doi-access = free }}</ref>

There has been some disagreement over the exact differences between 'metabolomics' and 'metabonomics'. The difference between the two terms is not related to choice of analytical platform: although metabonomics is more associated with [[Nuclear magnetic resonance spectroscopy|NMR spectroscopy]] and metabolomics with [[mass spectrometry]]-based techniques, this is simply because of usages amongst different groups that have popularized the different terms. While there is still no absolute agreement, there is a growing consensus that 'metabolomics' places a greater emphasis on metabolic profiling at a cellular or organ level and is primarily concerned with normal endogenous metabolism. 'Metabonomics' extends metabolic profiling to include information about perturbations of metabolism caused by environmental factors (including diet and toxins), disease processes, and the involvement of extragenomic influences, such as [[gut microflora]]. This is not a trivial difference; metabolomic studies should, by definition, exclude metabolic contributions from extragenomic sources, because these are external to the system being studied. However, in practice, within the field of human disease research there is still a large degree of overlap in the way both terms are used, and they are often in effect synonymous.<ref name = DGRref>{{cite journal | vauthors = Robertson DG | title = Metabonomics in toxicology: a review | journal = Toxicological Sciences | volume = 85 | issue = 2 | pages = 809–822 | date = June 2005 | pmid = 15689416 | doi = 10.1093/toxsci/kfi102 | doi-access = free }}</ref>

== Exometabolomics ==
{{Main|Exometabolomics}}
Exometabolomics, or "metabolic footprinting", is the study of extracellular metabolites. It uses many techniques from other subfields of metabolomics, and has applications in [[biofuel]] development, [[bioprocessing]], determining drugs' [[mechanism of action]], and studying intercellular interactions.<ref name="Silva">{{cite journal | vauthors = Silva LP, Northen TR | title = Exometabolomics and MSI: deconstructing how cells interact to transform their small molecule environment | journal = Current Opinion in Biotechnology | volume = 34 | pages = 209–216 | date = August 2015 | pmid = 25855407 | doi = 10.1016/j.copbio.2015.03.015 | publisher = Elsevier BV | doi-access = free }}</ref>

== Analytical technologies ==
[[File:Key_stages_of_a_metabolomics_study.png|thumb|right|Key stages of a metabolomics study]]
The typical workflow of metabolomics studies is shown in the figure. First, samples are collected from tissue, plasma, urine, saliva, cells, etc. Next, metabolites are extracted often with the addition of internal standards and derivatization.<ref name="Dettmer_2007">{{cite journal | vauthors = Dettmer K, Aronov PA, Hammock BD | title = Mass spectrometry-based metabolomics | journal = Mass Spectrometry Reviews | volume = 26 | issue = 1 | pages = 51–78 | date = 2007 | pmid = 16921475 | pmc = 1904337 | doi = 10.1002/mas.20108 | bibcode = 2007MSRv...26...51D }}</ref> During sample analysis, metabolites are quantified ([[liquid chromatography]] or [[gas chromatography]] coupled with [[Mass spectrometry|MS]] and/or [[Nuclear magnetic resonance|NMR]] spectroscopy).<ref>{{cite journal | vauthors = Lu W, Su X, Klein MS, Lewis IA, Fiehn O, Rabinowitz JD | title = Metabolite Measurement: Pitfalls to Avoid and Practices to Follow | journal = Annual Review of Biochemistry | volume = 86 | issue = 1 | pages = 277–304 | date = June 2017 | pmid = 28654323 | pmc = 5734093 | doi = 10.1146/annurev-biochem-061516-044952 }}</ref> The raw output data can be used for metabolite feature extraction and further processed before statistical analysis (such as [[principal component analysis]], PCA). Many bioinformatic tools and software are available to identify associations with disease states and outcomes, determine significant correlations, and characterize metabolic signatures with existing biological knowledge.<ref name="pmid23157406">{{cite journal | vauthors = Rasmiena AA, Ng TW, Meikle PJ | title = Metabolomics and ischaemic heart disease | journal = Clinical Science | volume = 124 | issue = 5 | pages = 289–306 | date = March 2013 | pmid = 23157406 | doi = 10.1042/CS20120268 }}</ref>

=== Separation methods ===
Initially, analytes in a metabolomic sample comprise a highly complex mixture. This complex mixture can be simplified prior to detection by separating some analytes from others. Separation achieves various goals: analytes which cannot be resolved by the detector may be separated in this step; in MS analysis, [[Ion suppression in liquid chromatography–mass spectrometry|ion suppression]] is reduced; the retention time of the analyte serves as information regarding its identity. This separation step is not mandatory and is often omitted in NMR and "shotgun" based approaches such as [[shotgun lipidomics]].

[[Gas chromatography]] (GC), especially when interfaced with mass spectrometry ([[Gas chromatography-mass spectrometry|GC-MS]]), is a widely used separation technique for metabolomic analysis. GC offers very high chromatographic resolution, and can be used in conjunction with a [[flame ionization detector]] (GC/FID) or a mass spectrometer (GC-MS). The method is especially useful for identification and quantification of small and volatile molecules.<ref>{{Cite web|url=https://www.thermofisher.com/dk/en/home/industrial/mass-spectrometry/mass-spectrometry-learning-center/gas-chromatography-mass-spectrometry-gc-ms-information.html|title=Gas Chromatography Mass Spectrometry (GC-MS) Information | work = Thermo Fisher Scientific - US |access-date=2018-09-26}}</ref> However, a practical limitation of GC is the requirement of chemical derivatization for many biomolecules as only volatile chemicals can be analysed without derivatization. In cases where greater resolving power is required, two-dimensional chromatography ([[GCxGC]]) can be applied.

[[High performance liquid chromatography]] (HPLC) has emerged as the most common separation technique for metabolomic analysis. With the advent of [[electrospray ionization]], HPLC was coupled to MS. In contrast with [[Gas-liquid chromatography|GC]], HPLC has lower chromatographic resolution, but requires no derivatization for polar molecules, and separates molecules in the liquid phase. Additionally HPLC has the advantage that a much wider range of analytes can be measured with a higher sensitivity than GC methods.<ref>{{cite journal | vauthors = Gika HG, Theodoridis GA, Wingate JE, Wilson ID | title = Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine | journal = Journal of Proteome Research | volume = 6 | issue = 8 | pages = 3291–3303 | date = August 2007 | pmid = 17625818 | doi = 10.1021/pr070183p }}</ref>

[[Capillary electrophoresis]] (CE) has a higher theoretical separation efficiency than HPLC (although requiring much more time per separation), and is suitable for use with a wider range of metabolite classes than is GC. As for all electrophoretic techniques, it is most appropriate for charged analytes.<ref>{{cite journal | vauthors = Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T | title = Quantitative metabolome analysis using capillary electrophoresis mass spectrometry | journal = Journal of Proteome Research | volume = 2 | issue = 5 | pages = 488–494 | date = September 2003 | pmid = 14582645 | doi = 10.1021/pr034020m }}</ref>

In direct-infusion mass spectrometry (DI-MS), sample is directly introduced into the spectrometer and separation steps are skipped. DI-MS can be employed to perform single cell metabolic analysis of human cells.<ref>{{cite journal |last1=Tang |first1=Huaqi |last2=Ali |first2=Ahmed |last3=Abdelazem |first3=Eman |last4=Ottenhoff |first4=Tom H. M. |last5=Heeren |first5=Ron M. A. |last6=Mashaghi |first6=Alireza |title=Random forest and live single-cell metabolomics reveal metabolic profiles of human macrophages upon polarization |journal=Biotechnology and Bioengineering |date=August 2023 |volume=120 |issue=8 |pages=2314–2325 |doi=10.1002/bit.28494|pmid=37424521 |bibcode=2023BiotB.120.2314T |hdl=1887/3633712 |hdl-access=free }}</ref>

=== Detection methods ===
[[Mass spectrometry]] (MS) is used to identify and quantify metabolites after optional separation by [[Gas chromatography|GC]], [[High-performance liquid chromatography|HPLC]], or [[Capillary electrophoresis|CE]]. [[GC-MS]] was the first hyphenated technique to be developed. Identification leverages the distinct patterns in which analytes fragment. These patterns can be thought of as a mass spectral fingerprint. Libraries exist that allow identification of a metabolite according to this [[fragmentation pattern]] {{Example needed|date=April 2019}}. MS is both sensitive and can be very specific. There are also a number of techniques which use MS as a stand-alone technology: the sample is infused directly into the mass spectrometer with no prior separation, and the MS provides sufficient selectivity to both separate and to detect metabolites.

For analysis by mass spectrometry, the analytes must be imparted with a charge and transferred to the gas phase. [[Electron ionization]] (EI) is the most common ionization technique applied to GC separations as it is amenable to low pressures. EI also produces fragmentation of the analyte, both providing structural information while increasing the complexity of the data and possibly obscuring the molecular ion. [[Atmospheric-pressure chemical ionization]] (APCI) is an atmospheric pressure technique that can be applied to all the above separation techniques. APCI is a gas phase ionization method, which provides slightly more aggressive ionization than ESI which is suitable for less polar compounds. [[Electrospray ionization]] (ESI) is the most common ionization technique applied in LC/MS. This soft ionization is most successful for polar molecules with ionizable functional groups. Another commonly used soft ionization technique is [[Secondary electrospray ionization|secondary electrospray ionization (SESI)]].

In the 2000s, surface-based mass analysis has seen a resurgence, with new MS technologies focused on increasing sensitivity, minimizing background, and reducing sample preparation. The ability to analyze metabolites directly from biofluids and tissues continues to challenge current MS technology, largely because of the limits imposed by the complexity of these samples, which contain thousands to tens of thousands of metabolites. Among the technologies being developed to address this challenge is Nanostructure-Initiator MS (NIMS),<ref>{{cite journal | vauthors = Northen TR, Yanes O, Northen MT, Marrinucci D, Uritboonthai W, Apon J, Golledge SL, Nordström A, Siuzdak G | title = Clathrate nanostructures for mass spectrometry | journal = Nature | volume = 449 | issue = 7165 | pages = 1033–1036 | date = October 2007 | pmid = 17960240 | doi = 10.1038/nature06195 | s2cid = 4404703 | bibcode = 2007Natur.449.1033N }}</ref><ref>{{cite journal | vauthors = Woo HK, Northen TR, Yanes O, Siuzdak G | title = Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis | journal = Nature Protocols | volume = 3 | issue = 8 | pages = 1341–1349 | date = July 2008 | pmid = 18714302 | doi = 10.1038/NPROT.2008.110 | s2cid = 20620548 | url = https://zenodo.org/record/896641 }}</ref> a desorption/ ionization approach that does not require the application of matrix and thereby facilitates small-molecule (i.e., metabolite) identification. [[MALDI]] is also used; however, the application of a MALDI matrix can add significant background at {{Nowrap|< 1000 Da}} that complicates analysis of the low-mass range (i.e., metabolites). In addition, the size of the resulting matrix crystals limits the spatial resolution that can be achieved in tissue imaging. Because of these limitations, several other matrix-free desorption/ionization approaches have been applied to the analysis of biofluids and tissues.

[[Secondary ion mass spectrometry]] (SIMS) was one of the first matrix-free desorption/ionization approaches used to analyze metabolites from biological samples.{{citation needed|date=February 2016}} SIMS uses a high-energy primary ion beam to desorb and generate secondary ions from a surface. The primary advantage of SIMS is its high spatial resolution (as small as 50&nbsp;nm), a powerful characteristic for tissue imaging with MS. However, SIMS has yet to be readily applied to the analysis of biofluids and tissues because of its limited sensitivity at {{Nowrap|>500 Da}} and analyte fragmentation generated by the high-energy primary ion beam. [[Desorption electrospray ionization]] (DESI) is a matrix-free technique for analyzing biological samples that uses a charged solvent spray to desorb ions from a surface. Advantages of DESI are that no special surface is required and the analysis is performed at ambient pressure with full access to the sample during acquisition. A limitation of DESI is spatial resolution because "focusing" the charged solvent spray is difficult. However, a recent development termed [[Laser Ablation Electrospray Ionization|laser ablation ESI]] (LAESI) is a promising approach to circumvent this limitation.{{citation needed|date=February 2016}} Most recently, ion trap techniques such as [[orbitrap]] mass spectrometry are also applied to metabolomics research.<ref>{{cite journal | vauthors = Ghaste M, Mistrik R, Shulaev V | title = Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics | journal = International Journal of Molecular Sciences | volume = 17 | issue = 6 | page = 816 | date = May 2016 | pmid = 27231903 | pmc = 4926350 | doi = 10.3390/ijms17060816 | doi-access = free }}</ref>

[[Nuclear magnetic resonance spectroscopy|Nuclear magnetic resonance (NMR) spectroscopy]] is the only detection technique which does not rely on separation of the analytes, and the sample can thus be recovered for further analyses. All kinds of small molecule metabolites can be measured simultaneously - in this sense, NMR is close to being a universal detector. The main advantages of NMR are high analytical reproducibility and simplicity of sample preparation. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques.<ref>{{cite journal | vauthors = Griffin JL | title = Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis | journal = Current Opinion in Chemical Biology | volume = 7 | issue = 5 | pages = 648–654 | date = October 2003 | pmid = 14580571 | doi = 10.1016/j.cbpa.2003.08.008 }}</ref><ref>{{cite journal | vauthors = Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, Nicholson JK | title = Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts | journal = Nature Protocols | volume = 2 | issue = 11 | pages = 2692–2703 | year = 2007 | pmid = 18007604 | doi = 10.1038/nprot.2007.376 | s2cid = 205463871 }}</ref>

Although NMR and MS are the most widely used modern-day techniques for detection, there are other methods in use. These include [[Fourier-transform ion cyclotron resonance]],<ref>{{cite journal | vauthors = Habchi B, Alves S, Jouan-Rimbaud Bouveresse D, Appenzeller B, Paris A, Rutledge DN, Rathahao-Paris E | title = Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality | journal = Analytical and Bioanalytical Chemistry | volume = 410 | issue = 2 | pages = 483–490 | date = January 2018 | pmid = 29167936 | doi = 10.1007/s00216-017-0738-3 | s2cid = 3769892 }}</ref> [[ion-mobility spectrometry]],<ref>{{cite journal | vauthors = King AM, Mullin LG, Wilson ID, Coen M, Rainville PD, Plumb RS, Gethings LA, Maker G, Trengove R | title = Development of a rapid profiling method for the analysis of polar analytes in urine using HILIC-MS and ion mobility enabled HILIC-MS | journal = Metabolomics | volume = 15 | issue = 2 | article-number = 17 | date = January 2019 | pmid = 30830424 | pmc = 6342856 | doi = 10.1007/s11306-019-1474-9 }}</ref> electrochemical detection (coupled to HPLC), [[Raman spectroscopy]] and radiolabel (when combined with thin-layer chromatography).{{Citation needed|date=January 2010}}

{| class="wikitable sortable" style="width: 100%; text-align: left; font-size: smaller; vertical-align: top;"
|+Table 1. Comparison of most common used metabolomics methods
|-
! Technology
! Sensitivity ([[Detection limit|LOD]])
! Sample volume
! Compatible with gases
! Compatible with liquids
! Compatible with solids
! Start-up cost
! Can be used in metabolite imaging (MALDI or DESI)
! Advantages
! Disadvantages
|-
| GC-MS
| 0.5 μM
| 0.1-0.2 mL
| {{Yes}}
| {{Yes}}
| {{No}}
| <$300,000
| {{No}}
| <ul>
<li>Quantitative (with calibration)</li>
<li>Large body of software and databases for metabolite identification</li>
<li>Detects most organic and some inorganic molecules</li>
<li>Excellent separation reproductibility</li>
</ul>
| <ul>
<li>Destructive (not recoverable)</li>
<li>Requires sample separation</li>
<li>Slow (20&mdash;40 min per sample)</li>
</ul>
|-
| LC-MS
| 0.5 nM
| 10&mdash;100 μL
| {{No}}
| {{Yes}}
| {{Yes}}
| >$300,000
| {{Yes}}
| <ul>
<li>Very flexible technology</li>
<li>Detects most organic and some inorganic molecules</li>
</ul>
| <ul>
<li>Destructive (not recoverable)</li>
<li>Not very quantitative</li>
<li>Slow (15&mdash;40 min per sample)</li>
<li>Usually requires separation</li>
</ul>
|-
| NMR spectroscopy
| 5 μM
| 10&mdash;100 μL
| {{No}}
| {{Yes}}
| {{Yes}}
| >US$1 million
| {{Yes}}
| <ul>
<li>Very flexible technology</li>
<li>Detects most organic and some inorganic molecules</li>
</ul>
| <ul>
<li>Large instrument footprint</li>
<li>Cannot detect or identify salts and inorganic ions</li>
<li>Cannot detect non-protonated compounds</li>
<li>Requires large sample volumes (0.1&mdash;0.5 mL)</li>
</ul>
|}

== Statistical methods ==
The data generated in metabolomics usually consist of measurements performed on subjects under various conditions. These measurements may be digitized spectra, or a list of metabolite features. In its simplest form, this generates a matrix with rows corresponding to subjects and columns corresponding with metabolite features (or vice versa).<ref name="VDGarticle"/> Several statistical programs are currently available for analysis of both NMR and [[mass spectrometry]] data. A great number of free software are already available for the analysis of metabolomics data shown in the table. Some statistical tools listed in the table were designed for NMR data analyses were also useful for MS data.<ref name="Sugimoto_2012">{{cite journal | vauthors = Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M | title = Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis | journal = Current Bioinformatics | volume = 7 | issue = 1 | pages = 96–108 | date = March 2012 | pmid = 22438836 | pmc = 3299976 | doi = 10.2174/157489312799304431 }}</ref> For mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design.<ref name="Spicer_2017">{{cite journal | vauthors = Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C | title = Navigating freely-available software tools for metabolomics analysis | journal = Metabolomics | volume = 13 | issue = 9 | article-number = 106 | date = 2017 | pmid = 28890673 | pmc = 5550549 | doi = 10.1007/s11306-017-1242-7 }}</ref>

Once metabolite data matrix is determined, unsupervised data reduction techniques (e.g. PCA) can be used to elucidate patterns and connections. In many studies, including those evaluating drug-toxicity and some disease models, the metabolites of interest are not known ''a priori''. This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. The most common of these methods includes [[principal component analysis]] (PCA) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation.<ref name="Nicholson 2003 668–676"/> When analyzed in the lower-dimensional PCA space, clustering of samples with similar metabolic fingerprints can be detected. PCA algorithms aim to replace all correlated variables with a much smaller number of uncorrelated variables (referred to as principal components (PCs)) and retain most of the information in the original dataset.<ref>{{cite journal | vauthors = Ren S, Hinzman AA, Kang EL, Szczesniak RD, Lu LJ | title = Computational and statistical analysis of metabolomics data. | journal = Metabolomics | date = December 2015 | volume = 11 | issue = 6 | pages = 1492–513 | doi = 10.1007/s11306-015-0823-6 | s2cid = 15712363 }}</ref> This clustering can elucidate patterns and assist in the determination of disease biomarkers – metabolites that correlate most with class membership.

Linear models are commonly used for metabolomics data, but are affected by [[multicollinearity]]. On the other hand, [[multivariate statistics]] are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is [[Partial least squares regression|Projection to Latent Structures (PLS) regression]] and its classification version PLS-DA. Other [[data mining]] methods, such as [[random forest]], [[Support vector machine|support-vector machines]], etc. are received increasing attention for untargeted metabolomics data analysis.<ref>{{cite journal | vauthors = Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, Goodacre R | title = A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding | journal = Analytica Chimica Acta | volume = 879 | pages = 10–23 | date = June 2015 | pmid = 26002472 | doi = 10.1016/j.aca.2015.02.012 | bibcode = 2015AcAC..879...10G | url = http://usir.salford.ac.uk/id/eprint/41364/1/1-s2.0-S0003267015001889-main.pdf }}</ref> In the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as [[Student's t-test]], [[Analysis of variance|ANOVA]] or mixed models) and only these with sufficient small p-values are considered relevant.<ref name="Ulaszewska_2019" /> However, correction strategies should be used to reduce false discoveries when [[Multiple comparisons problem|multiple comparisons]] are conducted since there is no standard method for measuring the total amount of metabolites directly in untargeted metabolomics.<ref>{{Cite web |title=Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing |url=https://www.metabolon.com/resources/publications/clinical-research-testing-untargeted-metabolomics/ |access-date=2022-11-08 |website=Metabolon |language=en-US}}</ref> For [[multivariate analysis]], models should always be validated to ensure that the results can be generalized.

== Machine learning and data mining ==
[[Machine learning]] is a powerful tool that can be used in metabolomics analysis. Recently, scientists have developed retention time prediction software. These tools allow researchers to apply artificial intelligence to the retention time prediction of small molecules in complex mixture, such as human plasma, plant extracts, foods, or microbial cultures. Retention time prediction increases the identification rate in liquid chromatography and can lead to an improved biological interpretation of metabolomics data.<ref>{{cite journal | vauthors = Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O | title = Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics | journal = Analytical Chemistry | volume = 92 | issue = 11 | pages = 7515–7522 | date = June 2020 | pmid = 32390414 | pmc = 8715951 | doi = 10.1021/acs.analchem.9b05765 | bibcode = 2020AnaCh..92.7515B }}</ref>

== Key applications ==
[[Toxicity]] assessment/[[toxicology]] by metabolic profiling (especially of urine or blood plasma samples) detects the physiological changes caused by toxic insult of a chemical (or mixture of chemicals). In many cases, the observed changes can be related to specific syndromes, e.g. a specific lesion in liver or kidney. This is of particular relevance to pharmaceutical companies wanting to test the toxicity of potential [[medication|drug]] candidates: if a compound can be eliminated before it reaches [[clinical trial]]s on the grounds of adverse toxicity, it saves the enormous expense of the trials.<ref name=DGRref/>

For [[functional genomics]], metabolomics can be an excellent tool for determining the [[phenotype]] caused by a genetic manipulation, such as gene deletion or insertion. Sometimes this can be a sufficient goal in itself—for instance, to detect any phenotypic changes in a genetically modified plant intended for human or animal consumption. More exciting is the prospect of predicting the function of unknown [[gene]]s by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from [[model organism]]s such as ''[[Saccharomyces cerevisiae]]'' and ''[[Arabidopsis thaliana]]''. The [[Benjamin Cravatt III|Cravatt laboratory]] at the [[Scripps Research|Scripps Research Institute]] has recently applied this technology to [[mammal]]ian systems, identifying the ''N''-acyltaurines as previously uncharacterized endogenous substrates for the enzyme [[fatty acid amide hydrolase]] (FAAH) and the monoalkylglycerol ethers (MAGEs) as endogenous substrates for the uncharacterized [[hydrolase]] [[KIAA1363]].<ref>{{cite journal | vauthors = Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF | title = Assignment of endogenous substrates to enzymes by global metabolite profiling | journal = Biochemistry | volume = 43 | issue = 45 | pages = 14332–14339 | date = November 2004 | pmid = 15533037 | doi = 10.1021/bi0480335 }}</ref><ref>{{cite journal | vauthors = Chiang KP, Niessen S, Saghatelian A, Cravatt BF | title = An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling | journal = Chemistry & Biology | volume = 13 | issue = 10 | pages = 1041–1050 | date = October 2006 | pmid = 17052608 | doi = 10.1016/j.chembiol.2006.08.008 | doi-access = free }}</ref>

Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes.<ref>{{cite journal | vauthors = Goering AW, McClure RA, Doroghazi JR, Albright JC, Haverland NA, Zhang Y, Ju KS, Thomson RJ, Metcalf WW, Kelleher NL | title = Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer | journal = ACS Central Science | volume = 2 | issue = 2 | pages = 99–108 | date = February 2016 | pmid = 27163034 | pmc = 4827660 | doi = 10.1021/acscentsci.5b00331 }}</ref> This bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.

[[Fluxomics]] is a further development of metabolomics. The disadvantage of metabolomics is that it only provides the user with abundances or concentrations of metabolites, while fluxomics determines the reaction rates of metabolic reactions and can trace metabolites in a biological system over time.

[[Nutrigenomics]] is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general, in a given body fluid, a metabolome is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non-nutrients. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism.<ref>{{cite journal | vauthors = Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B | title = Metabolomics in human nutrition: opportunities and challenges | journal = The American Journal of Clinical Nutrition | volume = 82 | issue = 3 | pages = 497–503 | date = September 2005 | pmid = 16155259 | doi = 10.1093/ajcn/82.3.497 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Christopher KB | title = Nutritional metabolomics in critical illness | journal = Current Opinion in Clinical Nutrition and Metabolic Care | volume = 21 | issue = 2 | pages = 121–125 | date = March 2018 | pmid = 29251691 | pmc = 5826639 | doi = 10.1097/MCO.0000000000000451 }}</ref> Thanks to recent cost reductions, metabolomics has now become accessible for companion animals, such as pregnant dogs.<ref>{{cite journal | vauthors = Arlt SP, Ottka C, Lohi H, Hinderer J, Lüdeke J, Müller E, Weber C, Kohn B, Bartel A | title = Metabolomics during canine pregnancy and lactation | journal = PLOS ONE | volume = 18 | issue = 5 | article-number = e0284570 | date = 2023-05-10 | pmid = 37163464 | pmc = 10171673 | doi = 10.1371/journal.pone.0284570 | veditors = Mükremin Ö | bibcode = 2023PLoSO..1884570A | doi-access = free }}</ref><ref>{{cite journal | vauthors = Ottka C, Vapalahti K, Arlt SP, Bartel A, Lohi H | title = The metabolic differences of anestrus, heat, pregnancy, pseudopregnancy, and lactation in 800 female dogs | journal = Frontiers in Veterinary Science | volume = 10 | article-number = 1105113 | date = 2023-02-02 | pmid = 36816179 | pmc = 9932911 | doi = 10.3389/fvets.2023.1105113 | doi-access = free }}</ref>

[[Volatolomics]] is a development of metabolomics that studies [[Volatile organic compound|volatile organic compounds]] (VOCs) emitted by a [[Biological system|biological systems]].

Plant metabolomics is designed to study the overall changes in metabolites of plant samples and then conduct deep data mining and chemometric analysis. Specialized metabolites are considered components of plant defense systems biosynthesized in response to biotic and abiotic stresses.<ref>{{cite journal | vauthors = Cotrim GD, Silva DM, Graça JP, Oliveira Junior A, Castro C, Zocolo GJ, Lannes LS, Hoffmann-Campo CB | title = Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability | journal = Phytochemistry | volume = 205 | issue = | article-number = 113472 | date = January 2023 | pmid = 36270412 | doi = 10.1016/j.phytochem.2022.113472 | doi-access = | bibcode = 2023PChem.205k3472C }}</ref> Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops.<ref>{{cite journal | vauthors = Schauer N, Fernie AR | title = Plant metabolomics: towards biological function and mechanism | journal = Trends in Plant Science | volume = 11 | issue = 10 | pages = 508–516 | date = October 2006 | pmid = 16949327 | doi = 10.1016/j.tplants.2006.08.007 | bibcode = 2006TPS....11..508S | doi-access = }}</ref>

== See also ==
{{Portal|Biology|Technology|Medicine}}
* [[Epigenomics]]
* [[Fluxomics]]
* [[Genomics]]
* [[Lipidomics]]
* [[Molecular epidemiology]]
* [[Molecular medicine]]
* [[Molecular pathology]]
* [[Precision medicine]]
* [[Proteomics]]
* [[Transcriptomics technologies|Transcriptomics]]
* [[XCMS Online]], a bioinformatics software designed for statistical analysis of mass spectrometry data

== References ==
{{Reflist}}

== Further reading ==
{{refbegin|30em}}
* {{Cite journal | vauthors = Bundy JG, Davey MP, Viant MR |year=2009 |title=Environmental metabolomics: A critical review and future perspectives |journal=Metabolomics |volume=5 |pages=3–21 |doi=10.1007/s11306-008-0152-0|s2cid=22179989 }}
* {{cite journal | vauthors = Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A | title = Metabolomics: available results, current research projects in breast cancer, and future applications | journal = Journal of Clinical Oncology | volume = 25 | issue = 19 | pages = 2840–2846 | date = July 2007 | pmid = 17502626 | doi = 10.1200/JCO.2006.09.7550 | url = http://lab.bcb.iastate.edu/projects/plantmetabolomics/ | archive-url = https://web.archive.org/web/20080120072634/http://lab.bcb.iastate.edu/projects/plantmetabolomics/ | archive-date = 2008-01-20 }}
* {{cite journal | vauthors = Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R | title = Metabolic fingerprinting as a diagnostic tool | journal = Pharmacogenomics | volume = 8 | issue = 9 | pages = 1243–1266 | date = September 2007 | pmid = 17924839 | doi = 10.2217/14622416.8.9.1243 }}
* {{cite journal | vauthors = Ellis DI, Goodacre R | title = Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy | journal = The Analyst | volume = 131 | issue = 8 | pages = 875–885 | date = August 2006 | pmid = 17028718 | doi = 10.1039/b602376m | bibcode = 2006Ana...131..875E }}
* {{cite journal | vauthors = Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN | title = Stable isotope-resolved metabolomics and applications for drug development | journal = Pharmacology & Therapeutics | volume = 133 | issue = 3 | pages = 366–391 | date = March 2012 | pmid = 22212615 | pmc = 3471671 | doi = 10.1016/j.pharmthera.2011.12.007 }}
* {{cite journal | vauthors = Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, Mahendraker T, Williams M, Neumann S, Rocca-Serra P, Maguire E, González-Beltrán A, Sansone SA, Griffin JL, Steinbeck C | title = MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data | journal = Nucleic Acids Research | volume = 41 | issue = Database issue | pages = D781–D786 | date = January 2013 | pmid = 23109552 | pmc = 3531110 | doi = 10.1093/nar/gks1004 }}
* {{Cite book | vauthors = Tomita M, Nishioka T |year=2005 |title=Metabolomics: The Frontier of Systems Biology |publisher=Springer |isbn=4-431-25121-9}}
* {{Cite book | vauthors = Weckwerth W |year=2006 |title=Metabolomics: Methods And Protocols (Methods in Molecular Biology) |publisher=Humana Press |isbn=1-588-29561-3 |oclc=493824826 |url-access=registration |url=https://archive.org/details/metabolomicsmeth00weck_0 }}
{{refend}}

== External links ==
{{Wiktionary}}
{{Wikibooks}}
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* [http://www.hmdb.ca/ Human Metabolome Database (HMDB)]
* [http://metlin.scripps.edu/ METLIN]
* [https://web.archive.org/web/20101228002337/http://metlin.scripps.edu/xcms/ XCMS]
* [https://sourceforge.net/projects/lcmstats/ LCMStats]
* [http://www.ebi.ac.uk/metabolights/ Metabolights]
* [http://metabolomics.info NIH Common Fund Metabolomics Consortium]
* [http://metabolomicsworkbench.org Metabolomics Workbench]
* [http://gmd.mpimp-golm.mpg.de/ Golm Metabolome Database]
* [https://www.metabolon.com/ Metabolon]

{{Genomics}}
{{Authority control}}

[[Category:Metabolism]]
[[Category:Systems biology]]
[[Category:Omics]]