Metabolomics is the scientific study of chemical processes involving metabolites, the small molecule intermediates and products of 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. 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. One of the challenges of systems biology and functional genomics is to integrate genomics, transcriptomic, proteomic, and metabolomic information to provide a better understanding of cellular biology.
The beginning of metabolomics traces back to 2000-1500 B.C. At this time, Ancient Chinese doctors used ants for the evaluation of urine of patients to detect whether the urine contained high levels of glucose, and hence detect diabetes. In the Middle Ages, "urine charts" were used to link the colours, tastes and smells of urine to various medical conditions, which are metabolic in origin. In 300 B.C., the ancient Greeks first use body fluids (called humor at the time) to predict disease, which shows the early steps towards metabolomics. Later in 131 A.D., Galen developed a system of pathology that combined the humoral theories of Hippocrates with the Pythagorean theory. Galen’s theory was unchallenged and remained standard until the 17th century.
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, 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. 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. 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.
Concurrently, 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. This first study on muscle highlighted the value of NMR in that it was determined that 90% of cellular 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. Recent[when?] 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 1H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data.
In 2005, the first metabolomics web database, METLIN, for characterizing human metabolites was developed in the Siuzdak laboratory at The Scripps Research Institute and contained over 10,000 metabolites and tandem mass spectral data. As of September 2015[update], METLIN contains over 240,000 metabolites as well as the largest repository of tandem mass spectrometry data in metabolomics.
On 23 January 2007, the Human Metabolome Project, led by Dr. David Wishart of the University of Alberta, Canada, completed the first draft of the human metabolome, consisting of a database of approximately 2500 metabolites, 1200 drugs and 3500 food components. Similar projects have been underway in several plant species, most notably Medicago truncatula and Arabidopsis thaliana for several years.
As late as mid-2010, metabolomics was still considered an "emerging field". 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.
In 2015, real-time metabolome profiling was demonstrated for the first time.
Metabolome refers to the complete set of small-molecule 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. 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. The first metabolite database(called METLIN) for searching m/z values from mass spectrometry data was developed by scientists at The Scripps Research Institute in 2005. 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. The HMDB stores more than 40,000 different metabolite entries. They catalogued approximately 2500 metabolites, 1200 drugs and 3500 food components that can be found in the human body, as reported in the literature. This information, available at the Human Metabolome Database (www.hmdb.ca) and based on analysis of information available in the current scientific literature, is far from complete. 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.
Each type of cell and tissue has a unique metabolic ‘fingerprint’ that can elucidate organ or tissue-specific information, while the study of biofluids can give more generalized though less specialized information. Commonly used biofluids are urine and plasma, as they can be obtained non-invasively or relatively non-invasively, respectively. 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.
Metabolites are the intermediates and products of metabolism. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1 kDa in size. However, there are exceptions to this depending on the sample and detection method. For example, macromolecules such as lipoproteins and albumin are reliably detected in NMR-based metabolomics studies of blood plasma. In plant-based metabolomics, it is common to refer to "primary" and "secondary" metabolites. 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 ecological function. Examples include antibiotics and pigments. By contrast, in human-based metabolomics, it is more common to describe metabolites as being either endogenous (produced by the host organism) or exogenous. Metabolites of foreign substances such as drugs are termed xenometabolites.
The metabolome forms 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 hypercycles.
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. This approach was pioneered by Jeremy Nicholson at Imperial College London 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.
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 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.
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.
The typical workflow of metabolomics studies is shown in the figure. First, samples are collected from tissue, plasma, urine, saliva, cells, etc. Next, metabolites extracted often with the addition of internal standards and derivatization. During sample analysis, metabolites are quantified (LC or GC coupled with MS and/or NMR spectroscopy). The raw output data can be used for metabolite identification and further processed before statistical analysis (such as 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.
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 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 (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 lionization detector (GC/FID) or a mass spectrometer (GC-MS). The method is especially useful for identification and quantification of small and volatile molecules. 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-dimentional 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 GC, HPLC has lower chromatographic resolution, but requires no derivitization for polar molecules, has no MW limitations, 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.
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.
Mass spectrometry (MS) is used to identify and to quantify metabolites after optional separation by GC, HPLC (LC-MS), or CE. GC-MS was the first hyphenated technique to be developed. Identification leverages the distinct patterns in which analytes fragment which can be thought of as a mass spectral fingerprint; libraries exist that allow identification of a metabolite according to this fragmentation pattern. 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 applies 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 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.
Surface-based mass analysis has seen a resurgence in the past decade, 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), 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 <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. 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 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 >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 ESI (LAESI) is a promising approach to circumvent this limitation.
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. Comparison of most common used metabolomics methods is shown in the table.
Although NMR and MS are the most widely used, modern day techniques other methods of detection that have been used. These include ion-mobility spectrometry, electrochemical detection (coupled to HPLC), Raman spectroscopy and radiolabel (when combined with thin-layer chromatography).
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 levels. In its simplest form this generates a matrix with rows corresponding to subjects and columns corresponding with metabolite levels. Several statistical programs are currently available for analysis of both NMR and mass spectrometry data. A great number of free software packages are already available for the analysis of metabolomics data shows in the table. Some statistical tools listed in the table were designed for NMR data analyses were also useful for MS data. For mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass and sometimes retention time depending on the experimental design. The first comprehensive software to analyze global mass spectrometry-based metabolomics datasets was developed by the Siuzdak laboratory at The Scripps Research Institute in 2006. This software, called XCMS, is freely available, has over 20,000 downloads since its inception in 2006, and is one of the most widely cited mass spectrometry-based metabolomics software programs in scientific literature. XCMS has now been surpassed in usage by a cloud-based version of XCMS called XCMS Online. It is available through an open source software project Bioconductor (http://www.bioconductor.org/) or METLIN metabolomics database (http://metlin.scripps.edu/download/). The software is capable of non-linear retention time alignment, peak picking, and relative quantitation and works with universal netCDF file format.  XCMS can identify hundreds of endogenous metabolites/ biomarkers and calculated a nonlinear retention time correction profiles for each sample. The advantages of XCMS were the software incorporated new nonlinear retention time alignment, matched filtration, peak detection and matching. Other popular metabolomics programs for mass spectral analysis are MZmine, MetAlign, MathDAMP, which also compensate for retention time deviation during sample analysis. MZmine was first introduced as an open-source software toolbox for LC-MS data processing in 2005. Nowadays, the MZmine 2, the latest version was completely redesigned to support the modularity. Furthermore, the latest version of MetAlign was introduced in 2007. MetAlign can calculate and fully use the accurate mass in data reduction and for alignment. LCMStats (https://sourceforge.net/projects/lcmstats/) is another R package for detailed analysis of liquid chromatography mass spectrometry(LCMS)data and is helpful in identification of co-eluting ions especially isotopologues from a complicated metabolic profile. It combines xcms package functions and can be used to apply many statistical functions for correcting detector saturation using coates correction and creating heat plots. Metabolomics data may also be analyzed by statistical projection (chemometrics) methods such as principal components analysis and partial least squares regression.
Once metabolic composition is determined, data reduction techniques 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. 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 by a much smaller number of uncorrelated variables (referred to as principal components (PCs)) and retain most of the information in the original dataset. This clustering can elucidate patterns and assist in the determination of disease biomarkers - metabolites that correlate most with class membership.
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 drug candidates: if a compound can be eliminated before it reaches clinical trials on the grounds of adverse toxicity, it saves the enormous expense of the trials.
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 genes by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from model organisms such as Saccharomyces cerevisiae and Arabidopsis thaliana. The Cravatt laboratory at The Scripps Research Institute has recently applied this technology to mammalian 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.
Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes. 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 steady-state level information, 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 a metabolome in a given body fluid 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.
- Daviss, Bennett (April 2005). "Growing pains for metabolomics". The Scientist. 19 (8): 25–28.
- Jordan, Kate W.; Nordenstam, Johan; Lauwers, Gregory Y.; Rothenberger, David A.; Alavi, Karim; Garwood, Michael; Cheng, Leo L. (2009). "Metabolomic Characterization of Human Rectal Adenocarcinoma with Intact Tissue Magnetic Resonance Spectroscopy". Diseases of the Colon & Rectum. 52 (3): 520–525. doi:10.1007/DCR.0b013e31819c9a2c. ISSN 0012-3706. PMC 2720561. PMID 19333056.
- Metabolomics: current technologies and future trends
- Van der greef and Smilde, J Chemomet, (2005) 19:376-386
- Nicholson JK, Lindon JC (October 2008). "Systems biology: Metabonomics". Nature. 455 (7216): 1054–6. Bibcode:2008Natur.455.1054N. doi:10.1038/4551054a. PMID 18948945.
- Metabolomics: Current technologies and future trends
- Gates, Sweeley; Sweeley, CC (1978). "Quantitative metabolic profiling based on gas chromatography". Clin Chem. 24 (10): 1663–73. PMID 359193.
- Preti, George. "Metabolomics comes of age?" The Scientist, 19:8, June 6, 2005.
- Novotny; Soini, Helena A.; Mechref, Yehia; et al. (2008). "Biochemical individuality reflected in chromatographic, electrophoretic and mass-spectrometric profiles". J Chromatogr B. 866: 26–47. doi:10.1016/j.jchromb.2007.10.007. PMC 2603028.
- Griffiths W.J.; Wang Y. (2009). "Mass spectrometry: From proteomics to metabolomics and lipidomics". Chem Soc Rev. 38 (7): 1882–96. doi:10.1039/b618553n. PMID 19551169.
- Hoult DI, Busby SJ, Gadian DG, Radda GK, Richards RE, Seeley PJ (November 1974). "Observation of tissue metabolites using 31P nuclear magnetic resonance". Nature. 252 (5481): 285–7. Bibcode:1974Natur.252..285H. doi:10.1038/252285a0. PMID 4431445.
- Holmes E and Antti H (2002) Analyst 127:1549-57
- Lenz EM, Wilson ID (2007). "Analytical strategies in metabonomics". J Proteome Res. 6 (2): 443–58. doi:10.1021/pr0605217. PMID 17269702.
- Smith CA, I'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (December 2005). "METLIN: a metabolite mass spectral database" (PDF). Ther Drug Monit. 27 (6): 747–51. doi:10.1097/01.ftd.0000179845.53213.39. PMID 16404815.
- Wishart DS, Tzur D, Knox C, et al. (January 2007). "HMDB: the Human Metabolome Database". Nucleic Acids Research. 35 (Database issue): D521–6. doi:10.1093/nar/gkl923. PMC 1899095. PMID 17202168.
- Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, Hau DD, Psychogios N, Dong E, Bouatra S, Mandal R, Sinelnikov I, Xia J, Jia L, Cruz JA, Lim E, Sobsey CA, Shrivastava S, Huang P, Liu P, Fang L, Peng J, Fradette R, Cheng D, Tzur D, Clements M, Lewis A, De Souza A, Zuniga A, Dawe M, Xiong Y, Clive D, Greiner R, Nazyrova A, Shaykhutdinov R, Li L, Vogel HJ, Forsythe I (2009). "HMDB: a knowledgebase for the human metabolome". Nucleic Acids Research. 37 (Database issue): D603–10. doi:10.1093/nar/gkn810. PMC 2686599. PMID 18953024.
- Farag, M. A.; Huhman, D. V.; Dixon, R. A.; Sumner, L. W. (2007). "Metabolomics Reveals Novel Pathways and Differential Mechanistic and Elicitor-Specific Responses in Phenylpropanoid and Isoflavonoid Biosynthesis in Medicago truncatula Cell Cultures". Plant Physiology. 146 (2): 387–402. doi:10.1104/pp.107.108431. PMC 2245840. PMID 18055588.
- Arabidopsis Metabolomics Consortium
- Morrow Jr., Ph.D., K. John (1 April 2010). "Mass Spec Central to Metabolomics". Genetic Engineering & Biotechnology News. 30 (7). p. 1. Archived from the original on 28 June 2010. Retrieved 28 June 2010.
- Real-time analysis of metabolic products
- Oliver SG, Winson MK, Kell DB, Baganz F (September 1998). "Systematic functional analysis of the yeast genome". Trends in Biotechnology. 16 (9): 373–8. doi:10.1016/S0167-7799(98)01214-1. PMID 9744112.
- Griffin JL, Vidal-Puig A (June 2008). "Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding?". Physiol. Genomics. 34 (1): 1–5. doi:10.1152/physiolgenomics.00009.2008. PMID 18413782.
- HMDB 3.0 – the human metabolome database in 2013.
- Pearson H (March 2007). "Meet the human metabolome". Nature. 446 (7131): 8. Bibcode:2007Natur.446....8P. doi:10.1038/446008a. PMID 17330009.
- De Luca V, St Pierre B (April 2000). "The cell and developmental biology of alkaloid biosynthesis". Trends Plant Sci. 5 (4): 168–73. doi:10.1016/S1360-1385(00)01575-2. PMID 10740298.
- Griffin JL, Shockcor JP (July 2004). "Metabolic profiles of cancer cells". Nat. Rev. Cancer. 4 (7): 551–61. doi:10.1038/nrc1390. PMID 15229480.
- Holmes, E.; I.D. Wilson; J.K. Nicholson (5 September 2008). "Metabolic phenotyping in health and disease". Cell. 134 (5): 714–717. doi:10.1016/j.cell.2008.08.026. PMID 18775301.
- Nicholson, J.K.; I.D. Wilson (2003). "Understanding 'global' systems biology: metabonomics and the continuum of metabolism". Nat Rev Drug Discov. 2 (8): 668–676. doi:10.1038/nrd1157.
- Samuelsson LM, Larsson DG (October 2008). "Contributions from metabolomics to fish research". Mol Biosyst. 4 (10): 974–9. doi:10.1039/b804196b. PMID 19082135.
- Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC (March 1995). "750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma". Anal. Chem. 67 (5): 793–811. doi:10.1021/ac00101a004. PMID 7762816.
- Bentley R (1999). "Secondary metabolite biosynthesis: the first century". Crit. Rev. Biotechnol. 19 (1): 1–40. doi:10.1080/0738-859991229189. PMID 10230052.
- Nordström A, O'Maille G, Qin C, Siuzdak G (May 2006). "Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum". Anal. Chem. 78 (10): 3289–95. doi:10.1021/ac060245f. PMC 3705959. PMID 16689529.
- Crockford DJ, Maher AD, Ahmadi KR, et al. (September 2008). "1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies". Anal. Chem. 80 (18): 6835–44. doi:10.1021/ac801075m. PMID 18700783.
- Nicholson JK (2006). "Global systems biology, personalized medicine and molecular epidemiology". Mol. Syst. Biol. 2 (1): 52. doi:10.1038/msb4100095. PMC 1682018. PMID 17016518.
- Nicholson JK, Lindon JC, Holmes E (November 1999). "'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data". Xenobiotica. 29 (11): 1181–9. doi:10.1080/004982599238047. PMID 10598751.
- Nicholson JK, Connelly J, Lindon JC, Holmes E (February 2002). "Metabonomics: a platform for studying drug toxicity and gene function". Nat Rev Drug Discov. 1 (2): 153–61. doi:10.1038/nrd728. PMID 12120097.
- Holmes E, Wilson ID, Nicholson JK (September 2008). "Metabolic phenotyping in health and disease". Cell. 134 (5): 714–7. doi:10.1016/j.cell.2008.08.026. PMID 18775301.
- Robertson DG (June 2005). "Metabonomics in toxicology: a review". Toxicol. Sci. 85 (2): 809–22. doi:10.1093/toxsci/kfi102. PMID 15689416.
- Silva, Leslie P; Northen, Trent R (2015). "Exometabolomics and MSI". Current Opinion in Biotechnology. Elsevier BV. 34: 209–216. doi:10.1016/j.copbio.2015.03.015.
- Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass Spectrom Rev 26, 51–78 (2007).
- Rasmiena, A. A., Ng, T. W. & Meikle, P. J. Metabolomics and ischaemic heart disease. Clinical Science 124, 289–306 (2013).
- Ogbaga, Chukwuma C.; Stepien, Piotr; Dyson, Beth C.; Rattray, Nicholas J. W.; Ellis, David I.; Goodacre, Royston; Johnson, Giles N. (6 May 2016). "Biochemical Analyses of Sorghum Varieties Reveal Differential Responses to Drought". PLOS ONE. 11 (5): e0154423. Bibcode:2016PLoSO..1154423O. doi:10.1371/journal.pone.0154423. PMC 4859509. PMID 27153323.
- "Gas Chromatography Mass Spectrometry (GC-MS) Information | Thermo Fisher Scientific - US". www.thermofisher.com. Retrieved 2018-09-26.
- Gika HG, Theodoridis GA, Wingate JE, Wilson ID (August 2007). "Within-day reproducibility of an LC-MS-based method for metabonomic analysis: application to human urine". J. Proteome Res. 6 (8): 3291–303. doi:10.1021/pr070183p. PMID 17625818.
- Soga T, Ohashi Y, Ueno Y (September 2003). "Quantitative metabolome analysis using capillary electrophoresis mass spectrometry". J. Proteome Res. 2 (5): 488–494. doi:10.1021/pr034020m. PMID 14582645.
- Northen T.R; Yanes O; Northen M; Marrinucci D; Uritboonthai W; Apon J; Golledge S; Nordstrom A; Siuzdak G (October 2007). "Clathrate nanostructures for mass spectrometry". Nature. 449 (7165): 1033–6. Bibcode:2007Natur.449.1033N. doi:10.1038/nature06195. PMID 17960240.
- Woo H, Northen TR, Yanes O, Siuzdak G (July 2008). "Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis". Nature Protocols. 3 (8): 1341–9. doi:10.1038/NPROT.2008.110. PMID 18714302.
- Griffin JL (October 2003). "Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis". Curr Opin Chem Biol. 7 (5): 648–54. doi:10.1016/j.cbpa.2003.08.008. PMID 14580571.
- Beckonert O, Keun HC, Ebbels TM, et al. (2007). "Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts". Nat Protoc. 2 (11): 2692–703. doi:10.1038/nprot.2007.376. PMID 18007604.
- Sugimoto, M., Kawakami, M., Robert, M., Soga, T. & Tomita, M. Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis. Curr Bioinform 7, 96–108 (2012).
- Smith CA, Want EJ, O'Maille G, Abagyan R, Siuzdak G (February 2006). "XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification". Anal Chem. 78 (3): 779–87. doi:10.1021/ac051437y. PMID 16448051.
- Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (April 2012). "XCMS Online: a web-based platform to process untargeted metabolomic data". Anal Chem. 84 (11): 5035–5039. doi:10.1021/ac300698c. PMC 3703953. PMID 22533540.
- Patti GJ, Tautenhahn R, Rinehart D, Cho K, Shriver L, Manchester M, Nikolskiy I, Johnson C, Mahieu N, Siuzdak G (2013). "A View from Above: Cloud Plots to Visualize Global Metabolomic Data". Anal Chem. 85 (2): 798–804. doi:10.1021/ac3029745. PMC 3716252.
- MASS SPECTROMETRY-BASED METABOLOMICS
- Katajamaa M, Miettinen J, Oresic M (March 2006). "MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data". Bioinformatics. 22 (5): 634–36. doi:10.1093/bioinformatics/btk039. PMID 16403790.
- Lommen A (April 2009). "MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data processing". Anal Chem. 81 (8): 3079–86. doi:10.1021/ac900036d. PMID 19301908.
- Baran R, Kochi H, Saito N, Suematsu M, Soga T, Nishioka T, Robert M, Tomita M (December 2006). "MathDAMP: a package for differential analysis of metabolite profiles". BMC Bioinformatics. 7: 530. doi:10.1186/1471-2105-7-530. PMC 1764210. PMID 17166258.
- Lommen, A. MetAlign: Interface-Driven, Versatile Metabolomics Tool for Hyphenated Full-Scan Mass Spectrometry Data Preprocessing. Anal. Chem. 81, 3079–3086 (2009).
- Singh S. "LCMStats: an R package for detailed analysis of LCMS data".
- Trygg J, Holmes E, Lundstedt T (February 2007). "Chemometrics in metabonomics". J. Proteome Res. 6 (2): 469–79. doi:10.1021/pr060594q. PMID 17269704.
- Computational and statistical analysis of metabolomics data
- Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF (November 2004). "Assignment of endogenous substrates to enzymes by global metabolite profiling". Biochemistry. 43 (45): 14332–9. doi:10.1021/bi0480335. PMID 15533037.
- Chiang KP, Niessen S, Saghatelian A, Cravatt BF (October 2006). "An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling". Chem. Biol. 13 (10): 1041–50. doi:10.1016/j.chembiol.2006.08.008. PMID 17052608.
- Goering, Anthony W.; McClure, Ryan A.; Doroghazi, James R.; Albright, Jessica C.; Haverland, Nicole A.; Zhang, Yongbo; Ju, Kou-San; Thomson, Regan J.; Metcalf, William W. (2016-02-24). "Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer". ACS Central Science. 2 (2): 99–108. doi:10.1021/acscentsci.5b00331. ISSN 2374-7943.
- Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B (September 2005). "Metabolomics in human nutrition: opportunities and challenges". Am. J. Clin. Nutr. 82 (3): 497–503. PMID 16155259.
- Tomita M, Nishioka T (2005). Metabolomics: The Frontier of Systems Biology. Springer. ISBN 4-431-25121-9.
- Weckwerth, Wolfram (2006). Metabolomics: Methods And Protocols (Methods in Molecular Biology). Humana Press. ISBN 1-588-29561-3. OCLC 493824826.
- Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN (March 2012). "Stable isotope-resolved metabolomics and applications for drug development". Pharmacol. Ther. 133 (3): 366–91. doi:10.1016/j.pharmthera.2011.12.007. PMC 3471671. PMID 22212615.
- Ellis DI, Goodacre R (2006). "Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy" (PDF). Analyst. 131 (8): 875–885. Bibcode:2006Ana...131..875E. doi:10.1039/b602376m. PMID 17028718.
- Claudino WM, Quatronne A, Pestrim M, Biganzoli L, Bertini, Di Leo A (2007). "Metabolomics: Available results, current research projects in breast cancer, and future applications". J Clin Oncol. 25 (19): 2840–6. doi:10.1200/JCO.2006.09.7550. PMID 17502626. Archived from the original on 2008-01-20.
- Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R (2007). "Metabolic fingerprinting as a diagnostic tool" (PDF). Pharmacogenomics. 8 (9): 1243–1266. doi:10.2217/146224184.108.40.2063. PMID 17924839.
- Bundy JG, Davey MP, Viant, MR (2009). "Environmental metabolomics: A critical review and future perspectives". Metabolomics. 5: 3–21. doi:10.1007/s11306-008-0152-0.
- Kenneth Haug; Reza M. Salek; Pablo Conesa; Janna Hastings; Paula de Matos; Mark Rijnbeek; Tejasvi Mahendrakar; Mark Williams; Steffen Neumann; Philippe Rocca-Serra; Eamonn Maguire; Alejandra González-Beltrán; Susanna-Assunta Sansone; Julian L. Griffin; Christoph Steinbeck. (2013). "MetaboLights-- an open-access general-purpose repository for metabolomics studies and associated meta-data". Nucleic Acids Res. 41 (Database issue): D781–D786. doi:10.1093/nar/gks1004. PMC 3531110. PMID 23109552.
|Look up metabolomics in Wiktionary, the free dictionary.|
|Wikibooks has more on the topic of: Metabolomics|
- Metabolism at Curlie (based on DMOZ)
- Metabolomics Workbench
- Mass Bank of North America (Mona) Database
- metabolomics research
- metabolomics blog - Metabolon
- Human Metabolome Database (HMDB)
- Golm Metabolome Database
- Global map of metabolomics labs