Metabolomics - Knowledge (XXG)

33: 1542:(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 with 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. 1084:-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 1698:

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. Thanks to recent cost reductions, metabolomics has now become accessible for companion animals, such as pregnant dogs.

221:. METLIN has since grown and as of December, 2023, METLIN contains MS/MS experimental data on over 930,000 molecular standards and other chemical entities, 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 1276:(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 1123: 1284:(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 325:(HMDB) is perhaps the most extensive public metabolomic spectral database to date 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: 5742: 937: 1182:(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-dimensional chromatography ( 1728: 1714: 1261:

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.

5730: 386: 1088:. 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. 1742: 32: 1524:

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

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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

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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

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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

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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

1238:. 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. 1159:

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

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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. The ease of

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contains >16,000 endogenous metabolites, >1,500 drugs and >22,000 food constituents or food metabolites. This information, available at the Human Metabolome Database and based on analysis of information available in the current scientific literature, is far from complete. In contrast, much

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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. Metabolomics

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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,

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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

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being produced in the cell, data that represents one aspect of cellular function. Conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. There are

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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

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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

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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, it has since (2012) been

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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. For mass spectrometry data, software is available that identifies

151:, 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 1697:

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

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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

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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.

1245:(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. 1206:(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. 301:

refers to the complete set of small-molecule (<1.5 kDa) 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

1200:, 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. 4284:

Habchi B, Alves S, Jouan-Rimbaud Bouveresse D, Appenzeller B, Paris A, Rutledge DN, et al. (January 2018). "Potential of dynamically harmonized Fourier transform ion cyclotron resonance cell for high-throughput metabolomics fingerprinting: control of data quality".

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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

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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

280: 310:; 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. 263:

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

1249:(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. 345:

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.

128:. 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, 3580:

Crockford DJ, Maher AD, Ahmadi KR, Barrett A, Plumb RS, Wilson ID, et al. (September 2008). "1H NMR and UPLC-MS(E) statistical heterospectroscopy: characterization of drug metabolites (xenometabolome) in epidemiological studies".

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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

1004:-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 5665: 3665:

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".

1151:, 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. 248:, 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. Similar projects have been underway in several plant species, most notably 4510:

Gromski PS, Muhamadali H, Ellis DI, Xu Y, Correa E, Turner ML, et al. (June 2015). "A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding".

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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

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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.

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Beckonert O, Keun HC, Ebbels TM, Bundy J, Holmes E, Lindon JC, et al. (2007). "Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts".

1253:(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 1702:

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.

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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.

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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".

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Villate A, San Nicolas M, Gallastegi M, Aulas PA, Olivares M, Usobiaga A, et al. (February 2021). "Review: Metabolomics as a prediction tool for plants performance under environmental stress".

171:. In 1984, Nicholson showed H NMR spectroscopy could potentially be used to diagnose diabetes mellitus, and later pioneered the application of pattern recognition methods to NMR spectroscopic data. 3962: 1164:

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

67:. 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 1569:, etc. are received increasing attention for untargeted metabolomics data analysis. In the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as 5306: 120:

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

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Griffin JL (October 2003). "Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis".

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Holmes E, Antti H (December 2002). "Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra".

202:, 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. 2800:"Metabolomics reveals novel pathways and differential mechanistic and elicitor-specific responses in phenylpropanoid and isoflavonoid biosynthesis in Medicago truncatula cell cultures" 4936:

Cotrim GD, Silva DM, Graça JP, Oliveira Junior A, Castro C, Zocolo GJ, et al. (January 2023). "Glycine max (L.) Merr. (Soybean) metabolome responses to potassium availability".

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Cravatt BF, Prospero-Garcia O, Siuzdak G, Gilula NB, Henriksen SJ, Boger DL, et al. (June 1995). "Chemical characterization of a family of brain lipids that induce sleep".

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Exometabolomics, or "metabolic footprinting", is the study of extracellular metabolites. It uses many techniques from other subfields of metabolomics, and has applications in

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are high analytical reproducibility and simplicity of sample preparation. Practically, however, it is relatively insensitive compared to mass spectrometry-based techniques.

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Gika HG, Theodoridis GA, Wingate JE, Wilson ID (August 2007). "Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine".

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Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF (November 2004). "Assignment of endogenous substrates to enzymes by global metabolite profiling".

159:, NMR continues to be a leading analytical tool to investigate metabolism. Recent efforts to utilize NMR for metabolomics have been largely driven by the laboratory of 2967:

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?".

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or mixed models) and only these with sufficient small p-values are considered relevant. However, correction strategies should be used to reduce false discoveries when

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Soga T, Ohashi Y, Ueno Y, Naraoka H, Tomita M, Nishioka T (September 2003). "Quantitative metabolome analysis using capillary electrophoresis mass spectrometry".

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Hoult DI, Busby SJ, Gadian DG, Radda GK, Richards RE, Seeley PJ (November 1974). "Observation of tissue metabolites using 31P nuclear magnetic resonance".

1538:. This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. The most common of these methods includes 349:

collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole.

4697:"Metabologenomics: Correlation of Microbial Gene Clusters with Metabolites Drives Discovery of a Nonribosomal Peptide with an Unusual Amino Acid Monomer" 1178:), is a widely used separation technique for metabolomic analysis. GC offers very high chromatographic resolution, and can be used in conjunction with a 4156:"Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics" 1303: 4056:

Northen TR, Yanes O, Northen MT, Marrinucci D, Uritboonthai W, Apon J, et al. (October 2007). "Clathrate nanostructures for mass spectrometry".

1246: 3484:"Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum" 2847: 1295: 1077: 148: 5114:

Ellis DI, Goodacre R (August 2006). "Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy".

104:), which can be used to predict metabolite abundances in biological samples from, for example mRNA abundances. One of the ultimate challenges of 1530:

molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design.

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represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.

4547:"Metabolomics in the clinic: A review of the shared and unique features of untargeted metabolomics for clinical research and clinical testing" 5702: 968: 1147:

spectroscopy). The raw output data can be used for metabolite feature extraction and further processed before statistical analysis (such as

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Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, et al. (December 2005). "METLIN: a metabolite mass spectral database".

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Nicholson JK, Connelly J, Lindon JC, Holmes E (February 2002). "Metabonomics: a platform for studying drug toxicity and gene function".

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Nicholson JK, Wilson ID (August 2003). "Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism".

674: 3412:

Nicholson JK, Foxall PJ, Spraul M, Farrant RD, Lindon JC (March 1995). "750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma".

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by comparison with the metabolic perturbations caused by deletion/insertion of known genes. Such advances are most likely to come from

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are conducted since there is no standard method for measuring the total amount of metabolites directly in untargeted metabolomics. For

5387: 4546: 4330:"Development of a rapid profiling method for the analysis of polar analytes in urine using HILIC-MS and ion mobility enabled HILIC-MS" 2586: 1281: 5269: 5246: 5049: 1254: 669: 4109:"Nanostructure-initiator mass spectrometry: a protocol for preparing and applying NIMS surfaces for high-sensitivity mass analysis" 3533:"Sensitive mass spectrometric analysis of carbonyl metabolites in human urine and fecal samples using chemoselective modification" 4475:

Ren S, Hinzman AA, Kang EL, Szczesniak RD, Lu LJ (December 2015). "Computational and statistical analysis of metabolomics data".

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in size. However, there are exceptions to this depending on the sample and detection method. For example, macromolecules such as

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to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.

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Although NMR and MS are the most widely used modern-day techniques for detection, there are other methods in use. These include

5734: 5680: 1273: 754: 136:(GC-MS) could be used to measure compounds present in human urine and tissue extracts. The Horning group, along with that of 1525:

with metabolite features (or vice versa). Several statistical programs are currently available for analysis of both NMR and

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Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R (September 2007). "Metabolic fingerprinting as a diagnostic tool".

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devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data. The version 3.5 of the

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is complexed with magnesium. As sensitivity has improved with the evolution of higher magnetic field strengths and

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For analysis by mass spectrometry, the analytes must be imparted with a charge and transferred to the gas phase.

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Ulaszewska MM, Weinert CH, Trimigno A, Portmann R, Andres Lacueva C, Badertscher R, et al. (January 2019).

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Oliver SG, Winson MK, Kell DB, Baganz F (September 1998). "Systematic functional analysis of the yeast genome".

1855:"Metabolomic characterization of human rectal adenocarcinoma with intact tissue magnetic resonance spectroscopy" 5773: 5670: 5515: 5447: 1643: 961: 920: 915: 623: 241: 206: 5019:

Bundy JG, Davey MP, Viant MR (2009). "Environmental metabolomics: A critical review and future perspectives".

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Hollywood K, Brison DR, Goodacre R (September 2006). "Metabolomics: current technologies and future trends".

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Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, et al. (March 2018).

1550: 1250: 1193: 168: 5196:"MetaboLights--an open-access general-purpose repository for metabolomics studies and associated meta-data" 4887:"The metabolic differences of anestrus, heat, pregnancy, pseudopregnancy, and lactation in 800 female dogs" 4656:"An enzyme that regulates ether lipid signaling pathways in cancer annotated by multidimensional profiling" 2907: 5696: 5612: 5415: 5380: 3839:"Exometabolomics and MSI: deconstructing how cells interact to transform their small molecule environment" 1775: 1566: 1288:(LAESI) is a promising approach to circumvent this limitation. Most recently, ion trap techniques such as 1047: 848: 152: 1553:

are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is

5675: 5629: 1582: 1235: 1192:(HPLC) has emerged as the most common separation technique for metabolomic analysis. With the advent of 1132: 858: 659: 502: 318: 314: 4695:

Goering AW, McClure RA, Doroghazi JR, Albright JC, Haverland NA, Zhang Y, et al. (February 2016).

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Griffiths WJ, Wang Y (July 2009). "Mass spectrometry: from proteomics to metabolomics and lipidomics".

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to suggest characteristic metabolic patterns in urine and saliva were associated with diseases such as

5050:"Metabolomics: available results, current research projects in breast cancer, and future applications" 4979:

Schauer N, Fernie AR (October 2006). "Plant metabolomics: towards biological function and mechanism".

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Arlt SP, Ottka C, Lohi H, Hinderer J, Lüdeke J, Müller E, et al. (2023-05-10). Mükremin Ö (ed.).

1020:. By contrast, in human-based metabolomics, it is more common to describe metabolites as being either 5435: 5420: 5123: 4945: 4065: 3544: 3368: 3130: 2460: 2405: 2323: 2272: 2221: 2042:

Gates SC, Sweeley CC (October 1978). "Quantitative metabolic profiling based on gas chromatography".

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De Luca V, St Pierre B (April 2000). "The cell and developmental biology of alkaloid biosynthesis".

1853:

Jordan KW, Nordenstam J, Lauwers GY, Rothenberger DA, Alavi K, Garwood M, et al. (March 2009).

988:. Within the context of metabolomics, a metabolite is usually defined as any molecule less than 1.5 144:

led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s.

5768: 5645: 5619: 5477: 5464: 5425: 1785: 1625: 1570: 1242: 1111: 875: 738: 526: 471: 461: 250: 195: 160: 516: 5599: 5410: 5036: 4492: 4310: 4266: 4136: 4089: 3775: 3726: 3334: 3234: 3156: 2992: 2519: 2296: 2245: 2194: 2128: 1973: 1930: 1790: 1780: 1673:(FAAH) and the monoalkylglycerol ethers (MAGEs) as endogenous substrates for the uncharacterized 1311: 1218: 1171: 1165: 1136: 1068: 868: 728: 532: 141: 3257:"Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies" 361:

can tell what makes it happen and metabolome can tell what has happened and what is happening.

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Lerner RA, Siuzdak G, Prospero-Garcia O, Henriksen SJ, Boger DL, Cravatt BF (September 1994).

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Haug K, Salek RM, Conesa P, Hastings J, de Matos P, Rijnbeek M, et al. (January 2013).

5172: 5164: 5131: 5094: 5061: 5028: 4988: 4953: 4908: 4898: 4857: 4847: 4806: 4798: 4757: 4716: 4708: 4667: 4628: 4591: 4583: 4520: 4484: 4447: 4439: 4398: 4390: 4349: 4341: 4294: 4250: 4214: 4177: 4167: 4120: 4073: 4030: 3995: 3936: 3899: 3891: 3850: 3809: 3757: 3710: 3675: 3637: 3629: 3590: 3552: 3503: 3495: 3456: 3421: 3384: 3376: 3318: 3278: 3268: 3218: 3183: 3138: 3089: 3081: 3040: 3032: 2976: 2941: 2819: 2811: 2770: 2762: 2721: 2713: 2667: 2659: 2620: 2558: 2550: 2503: 2468: 2423: 2413: 2366: 2331: 2280: 2229: 2178: 2118: 2051: 2014: 2004: 1957: 1914: 1874: 1866: 1658: 1593: 733: 707: 598: 494: 466: 395: 245: 218: 187: 4379:"Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis" 4328:

King AM, Mullin LG, Wilson ID, Coen M, Rainville PD, Plumb RS, et al. (January 2019).

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particular relevance to pharmaceutical companies wanting to test the toxicity of potential

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is also used; however, the application of a MALDI matrix can add significant background at

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developed as an online tool and as of 2019 (with METLIN) has over 30,000 registered users.

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indeed quantifiable correlations between the metabolome and the other cellular ensembles (

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Rasmiena AA, Ng TW, Meikle PJ (March 2013). "Metabolomics and ischaemic heart disease".

3548: 3372: 3134: 2464: 2409: 2327: 2276: 2225: 5778: 5746: 5584: 5487: 5430: 5258: 5220: 5195: 5177: 5152: 4913: 4886: 4811: 4786: 4721: 4696: 4596: 4571: 4452: 4427: 4403: 4378: 4354: 4329: 4182: 4155: 3904: 3879: 3642: 3617: 3508: 3483: 3389: 3356: 3094: 3069: 3045: 3020: 2980: 2824: 2799: 2775: 2750: 2749:

Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al. (1 January 2009).

2726: 2701: 2672: 2647: 2563: 2538: 2507: 2019: 1993:"Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines" 1992: 1879: 1854: 1808:, a bioinformatics software designed for statistical analysis of mass spectrometry data 1719: 1638: 1618: 1085: 843: 723: 628: 183: 3187: 2945: 2700:

Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. (January 1, 2007).

1839: 1067:

meaning a rule set or set of laws. This approach was pioneered by Jeremy Nicholson at

5762: 5559: 5505: 5469: 5296: 5168: 4572:"Retip: Retention Time Prediction for Compound Annotation in Untargeted Metabolomics" 4270: 3531:

Lin W, Conway LP, Block A, Sommi G, Vujasinovic M, Löhr JM, et al. (June 2020).

2428: 2393: 2132: 1934: 1694: 1562: 1107: 989: 895: 890: 648: 613: 354: 137: 125: 93: 72: 5040: 4957: 4496: 4140: 3730: 3338: 3068:

Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. (January 2013).

2523: 2198: 1977: 5510: 5442: 5048:

Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A (July 2007).

4672: 4655: 4314: 4093: 3779: 3160: 2996: 2300: 2249: 1918: 1585:, models should always be validated to ensure that the results can be generalized. 863: 783: 749: 437: 408: 229: 222: 214: 179: 84: 4992: 3238: 3019:

Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, et al. (January 2022).

2055: 4852: 4802: 4744:

Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B (September 2005).

4587: 3855: 3838: 2554: 1870: 1669:-acyltaurines as previously uncharacterized endogenous substrates for the enzyme 1621:

on the grounds of adverse toxicity, it saves the enormous expense of the trials.

5522: 5454: 5151:

Fan TW, Lorkiewicz PK, Sellers K, Moseley HN, Higashi RM, Lane AN (March 2012).

1805: 1755: 1558: 1013: 993: 833: 562: 422: 417: 4903: 4712: 4394: 4218: 3762: 3745: 2398:

Proceedings of the National Academy of Sciences of the United States of America

1991:

Cavicchioli MV, Santorsola M, Balboni N, Mercatelli D, Giorgi FM (March 2022).

1713: 1217:(MS) is used to identify and quantify metabolites after optional separation by 385: 5607: 5554: 5544: 5539: 5098: 5032: 4524: 4488: 4443: 4345: 4298: 3460: 1795: 1770: 1727: 1709: 1614: 1609: 1032: 1028:. Metabolites of foreign substances such as drugs are termed xenometabolites. 1021: 985: 981: 885: 838: 799: 778: 643: 590: 537: 403: 377: 307: 298: 288: 80: 68: 64: 60: 5279: 5065: 2357:

Lenz EM, Wilson ID (February 2007). "Analytical strategies in metabonomics".

271:

In 2015, real-time metabolome profiling was demonstrated for the first time.

48:. Associated with each stage is the corresponding systems biology tool, from 5666:

Matrix-assisted laser desorption ionization-time of flight mass spectrometer

5534: 5335: 5153:"Stable isotope-resolved metabolomics and applications for drug development" 4762: 4745: 3814: 3797: 3679: 2472: 2418: 1760: 1687: 1674: 1629: 1046:

are inputs to other chemical reactions. Such systems have been described as

1040: 1036: 1025: 824: 694: 603: 557: 45: 37: 5229: 5186: 5143: 5106: 5073: 5000: 4965: 4922: 4871: 4820: 4771: 4730: 4681: 4640: 4605: 4532: 4461: 4412: 4363: 4306: 4262: 4254: 4226: 4191: 4132: 4124: 4085: 4042: 4007: 3948: 3913: 3864: 3823: 3771: 3722: 3687: 3651: 3602: 3566: 3517: 3468: 3398: 3330: 3292: 3273: 3256: 3230: 3209:

Griffin JL, Shockcor JP (July 2004). "Metabolic profiles of cancer cells".

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Linear models are commonly used for metabolomics data, but are affected by

17: 5211: 3618:"Global systems biology, personalized medicine and molecular epidemiology" 3447:

Bentley R (1999). "Secondary metabolite biosynthesis: the first century".

3433: 3085: 2953: 2815: 2648:"XCMS Online: a web-based platform to process untargeted metabolomic data" 2480: 2437: 2241: 2155: 5396: 4172: 2766: 2717: 2263:

Nicholson JK, Lindon JC (October 2008). "Systems biology: Metabonomics".

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and analyte fragmentation generated by the high-energy primary ion beam.

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is not directly involved in those processes, but usually has important

997: 794: 4632: 4428:"Navigating freely-available software tools for metabolomics analysis" 4034: 3999: 3594: 3499: 3380: 2663: 2624: 2370: 5340: 5315: 5135: 2335: 2284: 2233: 2182: 1662: 770: 350: 233: 210: 89: 63:, the small molecule substrates, intermediates, and products of cell 3878:

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3322: 3222: 3143: 3118: 2123: 2106: 5350: 4108: 3880:"Metabolite Measurement: Pitfalls to Avoid and Practices to Follow" 3714: 2539:"METLIN: A Technology Platform for Identifying Knowns and Unknowns" 112:

information to provide a better understanding of cellular biology.

5260:

Metabolomics: Methods And Protocols (Methods in Molecular Biology)

1388:

Large body of software and databases for metabolite identification

1262: 1230: 1183: 1121: 278: 198:

from sleep deprived animals. One molecule of particular interest,

109: 31: 4377:

Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M (March 2012).

5355: 4654:

Chiang KP, Niessen S, Saghatelian A, Cravatt BF (October 2006).

3668:

Xenobiotica; the Fate of Foreign Compounds in Biological Systems

1634: 5369: 4746:"Metabolomics in human nutrition: opportunities and challenges" 2394:"Cerebrodiene: a brain lipid isolated from sleep-deprived cats" 1314:

and radiolabel (when combined with thin-layer chromatography).

4885:

Ottka C, Vapalahti K, Arlt SP, Bartel A, Lohi H (2023-02-02).

4570:

Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O (June 2020).

1617:

candidates: if a compound can be eliminated before it reaches

41: 5360: 5325: 4426:

Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C (2017).

1318:

Table 1. Comparison of most common used metabolomics methods

1292:

mass spectrometry are also applied to metabolomics research.

1628:, metabolomics can be an excellent tool for determining the 213:, for characterizing human metabolites was developed in the 27:

Scientific study of chemical processes involving metabolites

5365: 5345: 2646:

Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G (June 2012).

232:

lab was engaged in identifying metabolites associated with

3963:"Gas Chromatography Mass Spectrometry (GC-MS) Information" 2798:

Farag MA, Huhman DV, Dixon RA, Sumner LW (February 2008).

3304: 3302: 2146:

Shapiro I, Kavkalo DN, Petrova GV, Ganzin AP (1989). "".

1174:(GC), especially when interfaced with mass spectrometry ( 4791:

Current Opinion in Clinical Nutrition and Metabolic Care

59:

is the scientific study of chemical processes involving

321:

completed the first draft of the human metabolome. The

5320: 3482:

Nordström A, O'Maille G, Qin C, Siuzdak G (May 2006).

4836:"Metabolomics during canine pregnancy and lactation" 4107:

Woo HK, Northen TR, Yanes O, Siuzdak G (July 2008).

3744:

Holmes E, Wilson ID, Nicholson JK (September 2008).

5689: 5638: 5598: 5486: 5403: 5257: 3250: 3248: 3021:"HMDB 5.0: the Human Metabolome Database for 2022" 1505:Cannot detect or identify salts and inorganic ions 1348:Can be used in metabolite imaging (MALDI or DESI) 984:are the substrates, intermediates and products of 3009:HMDB 4.0 – the human metabolome database in 2018. 2587:"The Analytical Scientist Innovation Awards 2023" 1495:Detects most organic and some inorganic molecules 1443:Detects most organic and some inorganic molecules 1391:Detects most organic and some inorganic molecules 3070:"HMDB 3.0—The Human Metabolome Database in 2013" 2751:"HMDB: a knowledgebase for the human metabolome" 1555:Projection to Latent Structures (PLS) regression 1511:Requires large sample volumes (0.1—0.5 mL) 40:of biology showing the flow of information from 3350: 3348: 1310:, electrochemical detection (coupled to HPLC), 4787:"Nutritional metabolomics in critical illness" 2695: 2693: 2691: 1900: 1898: 5381: 5239:Metabolomics: The Frontier of Systems Biology 3746:"Metabolic phenotyping in health and disease" 2871: 2869: 1557:and its classification version PLS-DA. Other 1296:Nuclear magnetic resonance (NMR) spectroscopy 962: 8: 2882:Genetic Engineering & Biotechnology News 2100: 2098: 2096: 108:is to integrate metabolomics with all other 5661:Matrix-assisted laser desorption ionization 4160:International Journal of Molecular Sciences 4154:Ghaste M, Mistrik R, Shulaev V (May 2016). 1997:International Journal of Molecular Sciences 1196:, HPLC was coupled to MS. In contrast with 1114:, and studying intercellular interactions. 178:metabolomics experiments were performed by 5729: 5388: 5374: 5366: 4750:The American Journal of Clinical Nutrition 2908:"Real-time analysis of metabolic products" 1316: 1270:to the analysis of biofluids and tissues. 969: 955: 368: 5219: 5176: 4912: 4902: 4861: 4851: 4810: 4761: 4720: 4671: 4595: 4451: 4402: 4353: 4181: 4171: 3903: 3854: 3813: 3791: 3789: 3761: 3641: 3556: 3507: 3388: 3355:Dettmer K, Aronov PA, Hammock BD (2007). 3282: 3272: 3142: 3093: 3044: 2823: 2774: 2725: 2671: 2562: 2427: 2417: 2122: 2018: 2008: 1878: 1304:Fourier-transform ion cyclotron resonance 5305:) is being considered for deletion. See 1661:has recently applied this technology to 1255:secondary electrospray ionization (SESI) 1247:Atmospheric-pressure chemical ionization 5346:NIH Common Fund Metabolomics Consortium 3261:Molecular Nutrition & Food Research 2884:. Vol. 30, no. 7. p. 1. 2105:van der Greef J, Smilde AK (May 2005). 1818: 376: 357:can tell what appears to be happening, 176:liquid chromatography mass spectrometry 4287:Analytical and Bioanalytical Chemistry 3798:"Metabonomics in toxicology: a review" 3357:"Mass spectrometry-based metabolomics" 1508:Cannot detect non-protonated compounds 1190:High performance liquid chromatography 165:Birkbeck College, University of London 5703:European Molecular Biology Laboratory 3896:10.1146/annurev-biochem-061516-044952 2702:"HMDB: the Human Metabolobe Database" 1394:Excellent separation reproductibility 132:in 1971 after they demonstrated that 7: 3837:Silva LP, Northen TR (August 2015). 335:Molecular biology/biochemistry data. 134:gas chromatography-mass spectrometry 4207:Current Opinion in Chemical Biology 2888:from the original on 12 August 2011 2878:"Mass Spec Central to Metabolomics" 1024:(produced by the host organism) or 313:In January 2007, scientists at the 2981:10.1152/physiolgenomics.00009.2008 2508:10.1097/01.ftd.0000179845.53213.39 1282:Desorption electrospray ionization 1126:Key stages of a metabolomics study 1039:reactions, where outputs from one 25: 5309:to help reach a consensus. › 3449:Critical Reviews in Biotechnology 1456:Slow (15—40 min per sample) 1407:Slow (20—40 min per sample) 5741: 5740: 5728: 5321:Human Metabolome Database (HMDB) 5169:10.1016/j.pharmthera.2011.12.007 3843:Current Opinion in Biotechnology 1859:Diseases of the Colon and Rectum 1828:"Growing pains for metabolomics" 1740: 1726: 1712: 1589:Machine learning and data mining 936: 935: 384: 205:In 2005, the first metabolomics 5681:Chromosome conformation capture 5157:Pharmacology & Therapeutics 4958:10.1016/j.phytochem.2022.113472 4891:Frontiers in Veterinary Science 1385:Quantitative (with calibration) 1274:Secondary ion mass spectrometry 4673:10.1016/j.chembiol.2006.08.008 3703:Nature Reviews. Drug Discovery 3311:Nature Reviews. Drug Discovery 2077:Preti, George (June 6, 2005). 1919:10.1016/j.plantsci.2020.110789 1: 5709:National Institutes of Health 5237:Tomita M, Nishioka T (2005). 5206:(Database issue): D781–D786. 4993:10.1016/j.tplants.2006.08.007 4785:Christopher KB (March 2018). 3967:Thermo Fisher Scientific - US 3884:Annual Review of Biochemistry 3188:10.1016/S1360-1385(00)01575-2 3080:(Database issue): D801–D807. 2946:10.1016/S0167-7799(98)01214-1 2876:Morrow Jr KJ (1 April 2010). 2850:. Nov 7, 2012. Archived from 1450:Destructive (not recoverable) 1401:Destructive (not recoverable) 5054:Journal of Clinical Oncology 4853:10.1371/journal.pone.0284570 4803:10.1097/MCO.0000000000000451 4588:10.1021/acs.analchem.9b05765 4023:Journal of Proteome Research 3988:Journal of Proteome Research 3856:10.1016/j.copbio.2015.03.015 2555:10.1021/acs.analchem.7b04424 2359:Journal of Proteome Research 2079:"Metabolomics comes of age?" 1871:10.1007/DCR.0b013e31819c9a2c 1540:principal component analysis 1149:principal component analysis 854:Microbial population biology 353:can tell what could happen, 283:The human metabolome project 5625:Structure-based drug design 3119:"Meet the human metabolome" 2848:"www.Plantmetabolomics.org" 2496:Therapeutic Drug Monitoring 2056:10.1093/clinchem/24.10.1663 1459:Usually requires separation 1012:function. Examples include 83:analyses reveal the set of 5795: 4904:10.3389/fvets.2023.1105113 4713:10.1021/acscentsci.5b00331 4395:10.2174/157489312799304431 4219:10.1016/j.cbpa.2003.08.008 3796:Robertson DG (June 2005). 3763:10.1016/j.cell.2008.08.026 1838:(8): 25–28. Archived from 1671:fatty acid amide hydrolase 1659:Scripps Research Institute 1502:Large instrument footprint 1404:Requires sample separation 1095: 481:Marine microbial symbiosis 286: 219:Scripps Research Institute 188:Scripps Research Institute 5724: 5715:Wellcome Sanger Institute 5099:10.2217/14622416.8.9.1243 5033:10.1007/s11306-008-0152-0 4525:10.1016/j.aca.2015.02.012 4489:10.1007/s11306-015-0823-6 4444:10.1007/s11306-017-1242-7 4346:10.1007/s11306-019-1474-9 4299:10.1007/s00216-017-0738-3 3622:Molecular Systems Biology 3461:10.1080/0738-859991229189 3361:Mass Spectrometry Reviews 1665:systems, identifying the 1308:ion-mobility spectrometry 1204:Capillary electrophoresis 1180:flame ionization detector 1035:forms a large network of 1000:are reliably detected in 323:Human Metabolome Database 293:Human Metabolome Database 5671:Microfluidic-based tools 5516:Human Connectome Project 5448:Human Microbiome Project 5356:Golm Metabolome Database 5307:templates for discussion 5066:10.1200/JCO.2006.09.7550 3849:. Elsevier BV: 209–216. 3117:Pearson H (March 2007). 2591:The Analytical Scientist 2171:Chemical Society Reviews 1644:Saccharomyces cerevisiae 1492:Very flexible technology 1440:Very flexible technology 1339:Compatible with liquids 921:Earth Microbiome Project 916:Human Microbiome Project 675:Accessible carbohydrates 242:Human Metabolome Project 240:On 23 January 2007, the 207:tandem mass spectrometry 5656:Electrospray ionization 5528:Human Epigenome Project 4981:Trends in Plant Science 4660:Chemistry & Biology 3680:10.1080/004982599238047 3176:Trends in Plant Science 2934:Trends in Biotechnology 2473:10.1126/science.7770779 2419:10.1073/pnas.91.20.9505 2150:(in Russian) (9): 116. 2111:Journal of Chemometrics 1826:Daviss B (April 2005). 1567:support-vector machines 1551:multivariate statistics 1342:Compatible with solids 1251:Electrospray ionization 1194:electrospray ionization 1118:Analytical technologies 186:(then president of the 169:Imperial College London 5697:DNA Data Bank of Japan 5613:Human proteome project 5416:Computational genomics 5351:Metabolomics Workbench 5200:Nucleic Acids Research 4513:Analytica Chimica Acta 4383:Current Bioinformatics 4255:10.1038/nprot.2007.376 4125:10.1038/NPROT.2008.110 3802:Toxicological Sciences 3274:10.1002/mnfr.201800384 3211:Nature Reviews. Cancer 3074:Nucleic Acids Research 3025:Nucleic Acids Research 2969:Physiological Genomics 2755:Nucleic Acids Research 2706:Nucleic Acids Research 1962:10.1002/pmic.200600106 1776:Molecular epidemiology 1336:Compatible with gases 1127: 1080:and metabolomics with 849:Biological dark matter 284: 53: 5676:Isotope affinity tags 5630:Expression proteomics 4763:10.1093/ajcn/82.3.497 3815:10.1093/toxsci/kfi102 3616:Nicholson JK (2006). 2816:10.1104/pp.107.108431 1583:multivariate analysis 1549:. On the other hand, 1453:Not very quantitative 1236:fragmentation pattern 1133:liquid chromatography 1125: 1110:, determining drugs' 859:Microbial cooperation 319:University of Calgary 315:University of Alberta 282: 196:cerebral spinal fluid 35: 5436:Human Genome Project 5421:Comparative genomics 5256:Weckwerth W (2006). 4576:Analytical Chemistry 4173:10.3390/ijms17060816 3583:Analytical Chemistry 3488:Analytical Chemistry 3414:Analytical Chemistry 3037:10.1093/nar/gkab1062 2652:Analytical Chemistry 2613:Analytical Chemistry 2543:Analytical Chemistry 2148:Sovetskaia Meditsina 2010:10.3390/ijms23073867 1650:Arabidopsis thaliana 1579:multiple comparisons 1006:secondary metabolite 820:Biomass partitioning 755:hologenome evolution 680:Flora (microbiology) 257:Arabidopsis thaliana 157:magic angle spinning 122:paper chromatography 5646:2-D electrophoresis 5620:Call-map proteomics 5478:Structural genomics 5465:Population genomics 5426:Functional genomics 5212:10.1093/nar/gks1004 5128:2006Ana...131..875E 4950:2023PChem.205k3472C 4701:ACS Central Science 4627:(45): 14332–14339. 4078:10.1038/nature06195 4070:2007Natur.449.1033N 4064:(7165): 1033–1036. 3549:2020Ana...145.3822L 3426:10.1021/ac00101a004 3373:2007MSRv...26...51D 3135:2007Natur.446....8P 3086:10.1093/nar/gks1065 2465:1995Sci...268.1506C 2459:(5216): 1506–1509. 2410:1994PNAS...91.9505L 2328:2002Ana...127.1549H 2277:2008Natur.455.1054N 2271:(7216): 1054–1056. 2226:1974Natur.252..285H 1842:on 13 October 2008. 1786:Molecular pathology 1626:functional genomics 1520:Statistical methods 1319: 1243:Electron ionization 1112:mechanism of action 1063:meaning change and 876:Metatranscriptomics 670:Initial acquisition 665:Microbial community 372:Part of a series on 260:for several years. 251:Medicago truncatula 182:while working with 161:Jeremy K. Nicholson 5600:Structural biology 5411:Cognitive genomics 3941:10.1042/CS20120268 3634:10.1038/msb4100095 3558:10.1039/D0AN00150C 2767:10.1093/nar/gkn810 2718:10.1093/nar/gkl923 2044:Clinical Chemistry 1791:Precision medicine 1781:Molecular medicine 1655:Cravatt laboratory 1484:>US$ 1 million 1317: 1312:Raman spectroscopy 1286:laser ablation ESI 1186:) can be applied. 1172:Gas chromatography 1166:shotgun lipidomics 1155:Separation methods 1137:gas chromatography 1128: 1069:Murdoch University 454:Marine microbiomes 285: 217:laboratory at the 174:In 1994 and 1996, 142:Arthur B. Robinson 54: 5756: 5755: 5651:Mass spectrometer 5460:Personal genomics 5060:(19): 2840–2846. 4666:(10): 1041–1050. 4633:10.1021/bi0480335 4582:(11): 7515–7522. 4249:(11): 2692–2703. 4035:10.1021/pr034020m 4000:10.1021/pr070183p 3674:(11): 1181–1189. 3595:10.1021/ac801075m 3589:(18): 6835–6844. 3543:(11): 3822–3831. 3500:10.1021/ac060245f 3494:(10): 3289–3295. 3381:10.1002/mas.20108 3031:(D1): D622–D631. 2761:(D1): D603–D610. 2712:(D1): D521–D526. 2664:10.1021/ac300698c 2658:(11): 5035–5039. 2625:10.1021/ac051437y 2404:(20): 9505–9508. 2371:10.1021/pr0605217 2322:(12): 1549–1557. 2220:(5481): 285–287. 2050:(10): 1663–1673. 1956:(17): 4716–4723. 1734:Technology portal 1561:methods, such as 1547:multicollinearity 1527:mass spectrometry 1517: 1516: 1466:NMR spectroscopy 1215:Mass spectrometry 1210:Detection methods 1082:mass spectrometry 1044:chemical reaction 979: 978: 569:Built environment 551:Other microbiomes 495:Human microbiomes 396:Plant microbiomes 332:Clinical data and 268:instrumentation. 266:mass spectrometry 194:, to analyze the 16:(Redirected from 5786: 5744: 5743: 5732: 5731: 5575:Pharmacogenomics 5570:Pharmacogenetics 5390: 5383: 5376: 5367: 5283: 5264:. Humana Press. 5263: 5252: 5233: 5223: 5190: 5180: 5147: 5136:10.1039/b602376m 5110: 5093:(9): 1243–1266. 5087:Pharmacogenomics 5081: 5076:. Archived from 5044: 5005: 5004: 4976: 4970: 4969: 4933: 4927: 4926: 4916: 4906: 4882: 4876: 4875: 4865: 4855: 4831: 4825: 4824: 4814: 4782: 4776: 4775: 4765: 4741: 4735: 4734: 4724: 4692: 4686: 4685: 4675: 4651: 4645: 4644: 4616: 4610: 4609: 4599: 4567: 4561: 4560: 4558: 4557: 4543: 4537: 4536: 4507: 4501: 4500: 4472: 4466: 4465: 4455: 4423: 4417: 4416: 4406: 4374: 4368: 4367: 4357: 4325: 4319: 4318: 4281: 4275: 4274: 4243:Nature Protocols 4237: 4231: 4230: 4202: 4196: 4195: 4185: 4175: 4151: 4145: 4144: 4119:(8): 1341–1349. 4113:Nature Protocols 4104: 4098: 4097: 4053: 4047: 4046: 4018: 4012: 4011: 3994:(8): 3291–3303. 3983: 3977: 3976: 3974: 3973: 3959: 3953: 3952: 3929:Clinical Science 3924: 3918: 3917: 3907: 3875: 3869: 3868: 3858: 3834: 3828: 3827: 3817: 3793: 3784: 3783: 3765: 3741: 3735: 3734: 3698: 3692: 3691: 3662: 3656: 3655: 3645: 3613: 3607: 3606: 3577: 3571: 3570: 3560: 3528: 3522: 3521: 3511: 3479: 3473: 3472: 3444: 3438: 3437: 3409: 3403: 3402: 3392: 3352: 3343: 3342: 3306: 3297: 3296: 3286: 3276: 3252: 3243: 3242: 3206: 3200: 3199: 3171: 3165: 3164: 3146: 3114: 3108: 3107: 3097: 3065: 3059: 3058: 3048: 3016: 3010: 3007: 3001: 3000: 2964: 2958: 2957: 2929: 2923: 2922: 2920: 2918: 2904: 2898: 2897: 2895: 2893: 2873: 2864: 2863: 2861: 2859: 2844: 2838: 2837: 2827: 2804:Plant Physiology 2795: 2789: 2788: 2778: 2746: 2740: 2739: 2729: 2697: 2686: 2685: 2675: 2643: 2637: 2636: 2608: 2602: 2601: 2599: 2598: 2583: 2577: 2576: 2566: 2549:(5): 3156–3164. 2534: 2528: 2527: 2491: 2485: 2484: 2448: 2442: 2441: 2431: 2421: 2389: 2383: 2382: 2354: 2348: 2347: 2336:10.1039/b208254n 2311: 2305: 2304: 2285:10.1038/4551054a 2260: 2254: 2253: 2234:10.1038/252285a0 2209: 2203: 2202: 2183:10.1039/b618553n 2177:(7): 1882–1896. 2166: 2160: 2159: 2143: 2137: 2136: 2126: 2117:(5–7): 376–386. 2102: 2091: 2090: 2074: 2068: 2067: 2039: 2033: 2032: 2022: 2012: 1988: 1982: 1981: 1945: 1939: 1938: 1902: 1893: 1892: 1882: 1850: 1844: 1843: 1823: 1750: 1745: 1744: 1743: 1736: 1731: 1730: 1722: 1717: 1716: 1601:Key applications 1594:Machine learning 1571:Student's t-test 1472:10—100 μL 1420:10—100 μL 1320: 1279: 1268: 1078:NMR spectroscopy 971: 964: 957: 944: 939: 938: 708:Marine holobiont 508:Fecal transplant 388: 369: 246:David S. Wishart 192:Benjamin Cravatt 149:NMR spectroscopy 52:to metabolomics. 21: 5794: 5793: 5789: 5788: 5787: 5785: 5784: 5783: 5774:Systems biology 5759: 5758: 5757: 5752: 5720: 5685: 5634: 5594: 5590:Transcriptomics 5580:Systems biology 5565:Paleopolyploidy 5501:Cheminformatics 5482: 5399: 5394: 5310: 5291: 5286: 5272: 5255: 5249: 5236: 5193: 5150: 5113: 5084: 5047: 5018: 5014: 5012:Further reading 5009: 5008: 4987:(10): 508–516. 4978: 4977: 4973: 4935: 4934: 4930: 4884: 4883: 4879: 4846:(5): e0284570. 4833: 4832: 4828: 4784: 4783: 4779: 4743: 4742: 4738: 4694: 4693: 4689: 4653: 4652: 4648: 4618: 4617: 4613: 4569: 4568: 4564: 4555: 4553: 4545: 4544: 4540: 4509: 4508: 4504: 4483:(6): 1492–513. 4474: 4473: 4469: 4425: 4424: 4420: 4376: 4375: 4371: 4327: 4326: 4322: 4283: 4282: 4278: 4239: 4238: 4234: 4204: 4203: 4199: 4153: 4152: 4148: 4106: 4105: 4101: 4055: 4054: 4050: 4020: 4019: 4015: 3985: 3984: 3980: 3971: 3969: 3961: 3960: 3956: 3926: 3925: 3921: 3877: 3876: 3872: 3836: 3835: 3831: 3795: 3794: 3787: 3743: 3742: 3738: 3700: 3699: 3695: 3664: 3663: 3659: 3615: 3614: 3610: 3579: 3578: 3574: 3530: 3529: 3525: 3481: 3480: 3476: 3446: 3445: 3441: 3411: 3410: 3406: 3354: 3353: 3346: 3323:10.1038/nrd1157 3308: 3307: 3300: 3267:(1): e1800384. 3254: 3253: 3246: 3223:10.1038/nrc1390 3208: 3207: 3203: 3173: 3172: 3168: 3144:10.1038/446008a 3116: 3115: 3111: 3067: 3066: 3062: 3018: 3017: 3013: 3008: 3004: 2966: 2965: 2961: 2931: 2930: 2926: 2916: 2914: 2906: 2905: 2901: 2891: 2889: 2875: 2874: 2867: 2857: 2855: 2846: 2845: 2841: 2797: 2796: 2792: 2748: 2747: 2743: 2699: 2698: 2689: 2645: 2644: 2640: 2610: 2609: 2605: 2596: 2594: 2585: 2584: 2580: 2536: 2535: 2531: 2493: 2492: 2488: 2450: 2449: 2445: 2391: 2390: 2386: 2356: 2355: 2351: 2313: 2312: 2308: 2262: 2261: 2257: 2211: 2210: 2206: 2168: 2167: 2163: 2145: 2144: 2140: 2124:10.1002/cem.941 2104: 2103: 2094: 2076: 2075: 2071: 2041: 2040: 2036: 1990: 1989: 1985: 1947: 1946: 1942: 1904: 1903: 1896: 1852: 1851: 1847: 1825: 1824: 1820: 1815: 1801:Transcriptomics 1748:Medicine portal 1746: 1741: 1739: 1732: 1725: 1718: 1711: 1708: 1639:model organisms 1619:clinical trials 1603: 1591: 1522: 1277: 1266: 1212: 1162:ion suppression 1157: 1120: 1100: 1098:Exometabolomics 1094: 1092:Exometabolomics 1056: 975: 934: 927: 926: 925: 910: 902: 901: 900: 829: 814: 806: 805: 804: 791: 773: 763: 762: 761: 745: 712: 703:Plant holobiont 697: 687: 686: 685: 684: 655: 593: 583: 582: 581: 565: 552: 544: 543: 542: 529: 512: 497: 487: 486: 485: 476: 456: 446: 445: 444: 433:soil microbiome 428:root microbiome 413: 398: 367: 304:transcriptomics 295: 277: 118: 106:systems biology 77:gene expression 28: 23: 22: 15: 12: 11: 5: 5792: 5790: 5782: 5781: 5776: 5771: 5761: 5760: 5754: 5753: 5751: 5750: 5738: 5725: 5722: 5721: 5719: 5718: 5712: 5706: 5700: 5693: 5691: 5687: 5686: 5684: 5683: 5678: 5673: 5668: 5663: 5658: 5653: 5648: 5642: 5640: 5639:Research tools 5636: 5635: 5633: 5632: 5627: 5622: 5617: 5616: 5615: 5604: 5602: 5596: 5595: 5593: 5592: 5587: 5585:Toxicogenomics 5582: 5577: 5572: 5567: 5562: 5557: 5552: 5547: 5542: 5537: 5532: 5531: 5530: 5520: 5519: 5518: 5508: 5503: 5498: 5492: 5490: 5488:Bioinformatics 5484: 5483: 5481: 5480: 5475: 5467: 5462: 5457: 5452: 5451: 5450: 5440: 5439: 5438: 5431:Genome project 5428: 5423: 5418: 5413: 5407: 5405: 5401: 5400: 5395: 5393: 5392: 5385: 5378: 5370: 5364: 5363: 5358: 5353: 5348: 5343: 5338: 5333: 5328: 5323: 5318: 5294: 5290: 5289:External links 5287: 5285: 5284: 5270: 5253: 5247: 5234: 5191: 5163:(3): 366–391. 5148: 5122:(8): 875–885. 5111: 5082: 5080:on 2008-01-20. 5045: 5015: 5013: 5010: 5007: 5006: 4971: 4938:Phytochemistry 4928: 4877: 4826: 4797:(2): 121–125. 4777: 4756:(3): 497–503. 4736: 4687: 4646: 4611: 4562: 4538: 4502: 4467: 4418: 4369: 4320: 4293:(2): 483–490. 4276: 4232: 4213:(5): 648–654. 4197: 4146: 4099: 4048: 4029:(5): 488–494. 4013: 3978: 3954: 3935:(5): 289–306. 3919: 3890:(1): 277–304. 3870: 3829: 3808:(2): 809–822. 3785: 3756:(5): 714–717. 3736: 3715:10.1038/nrd728 3709:(2): 153–161. 3693: 3657: 3608: 3572: 3523: 3474: 3439: 3420:(5): 793–811. 3404: 3344: 3317:(8): 668–676. 3298: 3244: 3217:(7): 551–561. 3201: 3182:(4): 168–173. 3166: 3109: 3060: 3011: 3002: 2959: 2940:(9): 373–378. 2924: 2899: 2865: 2839: 2810:(2): 387–402. 2790: 2741: 2687: 2638: 2619:(3): 779–787. 2603: 2578: 2529: 2502:(6): 747–751. 2486: 2443: 2384: 2365:(2): 443–458. 2349: 2306: 2255: 2204: 2161: 2138: 2092: 2069: 2034: 1983: 1940: 1894: 1865:(3): 520–525. 1845: 1817: 1816: 1814: 1811: 1810: 1809: 1803: 1798: 1793: 1788: 1783: 1778: 1773: 1768: 1763: 1758: 1752: 1751: 1737: 1723: 1720:Biology portal 1707: 1704: 1602: 1599: 1590: 1587: 1521: 1518: 1515: 1514: 1513: 1512: 1509: 1506: 1503: 1498: 1497: 1496: 1493: 1488: 1485: 1482: 1479: 1476: 1473: 1470: 1467: 1463: 1462: 1461: 1460: 1457: 1454: 1451: 1446: 1445: 1444: 1441: 1436: 1433: 1432:>$ 300,000 1430: 1427: 1424: 1421: 1418: 1415: 1411: 1410: 1409: 1408: 1405: 1402: 1397: 1396: 1395: 1392: 1389: 1386: 1381: 1378: 1377:<$ 300,000 1375: 1372: 1369: 1366: 1363: 1360: 1356: 1355: 1354:Disadvantages 1352: 1349: 1346: 1345:Start-up cost 1343: 1340: 1337: 1334: 1333:Sample volume 1331: 1324: 1211: 1208: 1156: 1153: 1119: 1116: 1096:Main article: 1093: 1090: 1086:gut microflora 1055: 1052: 977: 976: 974: 973: 966: 959: 951: 948: 947: 946: 945: 929: 928: 924: 923: 918: 912: 911: 908: 907: 904: 903: 899: 898: 893: 888: 883: 878: 873: 872: 871: 861: 856: 851: 846: 844:Quorum sensing 841: 836: 830: 828: 827: 822: 816: 815: 812: 811: 808: 807: 803: 802: 797: 792: 786: 781: 775: 774: 769: 768: 765: 764: 760: 759: 758: 757: 746: 744: 743: 742: 741: 736: 731: 726: 721: 713: 711: 710: 705: 699: 698: 693: 692: 689: 688: 683: 682: 677: 672: 667: 662: 656: 654: 653: 652: 651: 646: 641: 636: 631: 620: 619: 618: 617: 616: 611: 606: 595: 594: 589: 588: 585: 584: 580: 579: 571: 566: 560: 554: 553: 550: 549: 546: 545: 541: 540: 535: 530: 524: 519: 517:Gut–brain axis 513: 511: 510: 505: 499: 498: 493: 492: 489: 488: 484: 483: 477: 475: 474: 469: 464: 458: 457: 452: 451: 448: 447: 443: 442: 441: 440: 435: 430: 425: 414: 412: 411: 406: 400: 399: 394: 393: 390: 389: 381: 380: 374: 373: 366: 363: 337: 336: 333: 330: 329:Chemical data, 276: 273: 184:Richard Lerner 147:Concurrently, 117: 114: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 5791: 5780: 5777: 5775: 5772: 5770: 5767: 5766: 5764: 5749: 5748: 5739: 5737: 5736: 5727: 5726: 5723: 5716: 5713: 5710: 5707: 5704: 5701: 5698: 5695: 5694: 5692: 5690:Organizations 5688: 5682: 5679: 5677: 5674: 5672: 5669: 5667: 5664: 5662: 5659: 5657: 5654: 5652: 5649: 5647: 5644: 5643: 5641: 5637: 5631: 5628: 5626: 5623: 5621: 5618: 5614: 5611: 5610: 5609: 5606: 5605: 5603: 5601: 5597: 5591: 5588: 5586: 5583: 5581: 5578: 5576: 5573: 5571: 5568: 5566: 5563: 5561: 5560:Nutrigenomics 5558: 5556: 5553: 5551: 5548: 5546: 5543: 5541: 5538: 5536: 5533: 5529: 5526: 5525: 5524: 5521: 5517: 5514: 5513: 5512: 5509: 5507: 5506:Chemogenomics 5504: 5502: 5499: 5497: 5494: 5493: 5491: 5489: 5485: 5479: 5476: 5474: 5472: 5468: 5466: 5463: 5461: 5458: 5456: 5453: 5449: 5446: 5445: 5444: 5441: 5437: 5434: 5433: 5432: 5429: 5427: 5424: 5422: 5419: 5417: 5414: 5412: 5409: 5408: 5406: 5402: 5398: 5391: 5386: 5384: 5379: 5377: 5372: 5371: 5368: 5362: 5359: 5357: 5354: 5352: 5349: 5347: 5344: 5342: 5339: 5337: 5334: 5332: 5329: 5327: 5324: 5322: 5319: 5317: 5313: 5308: 5304: 5303: 5298: 5293: 5292: 5288: 5281: 5277: 5273: 5271:1-588-29561-3 5267: 5262: 5261: 5254: 5250: 5248:4-431-25121-9 5244: 5240: 5235: 5231: 5227: 5222: 5217: 5213: 5209: 5205: 5201: 5197: 5192: 5188: 5184: 5179: 5174: 5170: 5166: 5162: 5158: 5154: 5149: 5145: 5141: 5137: 5133: 5129: 5125: 5121: 5117: 5112: 5108: 5104: 5100: 5096: 5092: 5088: 5083: 5079: 5075: 5071: 5067: 5063: 5059: 5055: 5051: 5046: 5042: 5038: 5034: 5030: 5026: 5022: 5017: 5016: 5011: 5002: 4998: 4994: 4990: 4986: 4982: 4975: 4972: 4967: 4963: 4959: 4955: 4951: 4947: 4943: 4939: 4932: 4929: 4924: 4920: 4915: 4910: 4905: 4900: 4896: 4892: 4888: 4881: 4878: 4873: 4869: 4864: 4859: 4854: 4849: 4845: 4841: 4837: 4830: 4827: 4822: 4818: 4813: 4808: 4804: 4800: 4796: 4792: 4788: 4781: 4778: 4773: 4769: 4764: 4759: 4755: 4751: 4747: 4740: 4737: 4732: 4728: 4723: 4718: 4714: 4710: 4707:(2): 99–108. 4706: 4702: 4698: 4691: 4688: 4683: 4679: 4674: 4669: 4665: 4661: 4657: 4650: 4647: 4642: 4638: 4634: 4630: 4626: 4622: 4615: 4612: 4607: 4603: 4598: 4593: 4589: 4585: 4581: 4577: 4573: 4566: 4563: 4552: 4548: 4542: 4539: 4534: 4530: 4526: 4522: 4518: 4514: 4506: 4503: 4498: 4494: 4490: 4486: 4482: 4478: 4471: 4468: 4463: 4459: 4454: 4449: 4445: 4441: 4437: 4433: 4429: 4422: 4419: 4414: 4410: 4405: 4400: 4396: 4392: 4389:(1): 96–108. 4388: 4384: 4380: 4373: 4370: 4365: 4361: 4356: 4351: 4347: 4343: 4339: 4335: 4331: 4324: 4321: 4316: 4312: 4308: 4304: 4300: 4296: 4292: 4288: 4280: 4277: 4272: 4268: 4264: 4260: 4256: 4252: 4248: 4244: 4236: 4233: 4228: 4224: 4220: 4216: 4212: 4208: 4201: 4198: 4193: 4189: 4184: 4179: 4174: 4169: 4165: 4161: 4157: 4150: 4147: 4142: 4138: 4134: 4130: 4126: 4122: 4118: 4114: 4110: 4103: 4100: 4095: 4091: 4087: 4083: 4079: 4075: 4071: 4067: 4063: 4059: 4052: 4049: 4044: 4040: 4036: 4032: 4028: 4024: 4017: 4014: 4009: 4005: 4001: 3997: 3993: 3989: 3982: 3979: 3968: 3964: 3958: 3955: 3950: 3946: 3942: 3938: 3934: 3930: 3923: 3920: 3915: 3911: 3906: 3901: 3897: 3893: 3889: 3885: 3881: 3874: 3871: 3866: 3862: 3857: 3852: 3848: 3844: 3840: 3833: 3830: 3825: 3821: 3816: 3811: 3807: 3803: 3799: 3792: 3790: 3786: 3781: 3777: 3773: 3769: 3764: 3759: 3755: 3751: 3747: 3740: 3737: 3732: 3728: 3724: 3720: 3716: 3712: 3708: 3704: 3697: 3694: 3689: 3685: 3681: 3677: 3673: 3669: 3661: 3658: 3653: 3649: 3644: 3639: 3635: 3631: 3627: 3623: 3619: 3612: 3609: 3604: 3600: 3596: 3592: 3588: 3584: 3576: 3573: 3568: 3564: 3559: 3554: 3550: 3546: 3542: 3538: 3534: 3527: 3524: 3519: 3515: 3510: 3505: 3501: 3497: 3493: 3489: 3485: 3478: 3475: 3470: 3466: 3462: 3458: 3454: 3450: 3443: 3440: 3435: 3431: 3427: 3423: 3419: 3415: 3408: 3405: 3400: 3396: 3391: 3386: 3382: 3378: 3374: 3370: 3366: 3362: 3358: 3351: 3349: 3345: 3340: 3336: 3332: 3328: 3324: 3320: 3316: 3312: 3305: 3303: 3299: 3294: 3290: 3285: 3280: 3275: 3270: 3266: 3262: 3258: 3251: 3249: 3245: 3240: 3236: 3232: 3228: 3224: 3220: 3216: 3212: 3205: 3202: 3197: 3193: 3189: 3185: 3181: 3177: 3170: 3167: 3162: 3158: 3154: 3150: 3145: 3140: 3136: 3132: 3128: 3124: 3120: 3113: 3110: 3105: 3101: 3096: 3091: 3087: 3083: 3079: 3075: 3071: 3064: 3061: 3056: 3052: 3047: 3042: 3038: 3034: 3030: 3026: 3022: 3015: 3012: 3006: 3003: 2998: 2994: 2990: 2986: 2982: 2978: 2974: 2970: 2963: 2960: 2955: 2951: 2947: 2943: 2939: 2935: 2928: 2925: 2913: 2909: 2903: 2900: 2887: 2883: 2879: 2872: 2870: 2866: 2854:on 2012-11-07 2853: 2849: 2843: 2840: 2835: 2831: 2826: 2821: 2817: 2813: 2809: 2805: 2801: 2794: 2791: 2786: 2782: 2777: 2772: 2768: 2764: 2760: 2756: 2752: 2745: 2742: 2737: 2733: 2728: 2723: 2719: 2715: 2711: 2707: 2703: 2696: 2694: 2692: 2688: 2683: 2679: 2674: 2669: 2665: 2661: 2657: 2653: 2649: 2642: 2639: 2634: 2630: 2626: 2622: 2618: 2614: 2607: 2604: 2592: 2588: 2582: 2579: 2574: 2570: 2565: 2560: 2556: 2552: 2548: 2544: 2540: 2533: 2530: 2525: 2521: 2517: 2513: 2509: 2505: 2501: 2497: 2490: 2487: 2482: 2478: 2474: 2470: 2466: 2462: 2458: 2454: 2447: 2444: 2439: 2435: 2430: 2425: 2420: 2415: 2411: 2407: 2403: 2399: 2395: 2388: 2385: 2380: 2376: 2372: 2368: 2364: 2360: 2353: 2350: 2345: 2341: 2337: 2333: 2329: 2325: 2321: 2317: 2310: 2307: 2302: 2298: 2294: 2290: 2286: 2282: 2278: 2274: 2270: 2266: 2259: 2256: 2251: 2247: 2243: 2239: 2235: 2231: 2227: 2223: 2219: 2215: 2208: 2205: 2200: 2196: 2192: 2188: 2184: 2180: 2176: 2172: 2165: 2162: 2157: 2153: 2149: 2142: 2139: 2134: 2130: 2125: 2120: 2116: 2112: 2108: 2101: 2099: 2097: 2093: 2088: 2084: 2083:The Scientist 2080: 2073: 2070: 2065: 2061: 2057: 2053: 2049: 2045: 2038: 2035: 2030: 2026: 2021: 2016: 2011: 2006: 2002: 1998: 1994: 1987: 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