metabolomics metabolomics metabolomics is the scientific study of chemical processes involving metabolites, the small molecule substrates, intermediates, and products of cell metabolism. specifically, metabolomics is the "systematic study of the unique chemical fingerprints that specific cellular processes leave behind", the study of their small-molecule metabolite profiles. the metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes. messenger rna (mrna), gene expression data, and proteomic analyses reveal the set of gene products being produced in the cell, data that represents one aspect of cellular function. conversely, metabolic profiling can give an instantaneous snapshot of the physiology of that cell, and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism. there are indeed quantifiable correlations between the metabolome and the other cellular ensembles (genome, transcriptome, proteome, and lipidome), which can be used to predict metabolite abundances in biological samples from, for example mrna abundances. one of the ultimate challenges of systems biology is to integrate metabolomics with all other -omics information to provide a better understanding of cellular biology. the central dogma of biology showing the flow of information from dna to the phenotype. associated with each stage is the corresponding systems biology tool, from genomics to metabolomics. history the concept that individuals might have a "metabolic profile" that could be reflected in the makeup of their biological fluids was introduced by roger williams in the late 1940s, 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 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 1994 and 1996 liquid chromatography mass spectrometry metabolomics experiments were performed by gary siuzdak while working with richard lerner (then president of the scripps research institute) and benjamin cravatt, to analyze the cerebral spinal fluid from sleep deprived animals. one molecule of particular interest, oleamide, was observed and later shown to have sleep inducing properties. this work is one of the earliest such experiments combining liquid chromatography and mass spectrometry in metabolomics. in 2005, the first metabolomics tandem mass spectrometry database, metlin, for characterizing human metabolites was developed in the siuzdak laboratory at the scripps research institute. metlin has since grown and as of july 1, 2019, metlin contains over 450,000 metabolites 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 roy goodacre. in 2005, the siuzdak lab was engaged in identifying metabolites associated with sepsis and in an effort to address the issue of statistically identifying the most relevant dysregulated metabolites across hundreds of lc/ms datasets, the first algorithm was developed to allow for the nonlinear alignment of mass spectrometry metabolomics data. called xcms, it has since (2012) been developed as an online tool and as of 2019 (with metlin) has over 30,000 registered users. on 23 january 2007, the human metabolome project, led by david s. wishart, completed the first draft of the human metabolome, consisting of a database of approximately 2,500 metabolites, 1,200 drugs and 3,500 food components. 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 human metabolome project the metabolome 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 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 fragmentation data from tandem mass spectrometry experiments was developed by the siuzdak lab at the scripps research institute in 2005. metlin contains over 450,000 metabolites and other chemical entities, each compound having experimental tandem mass spectrometry data. in 2006, the siuzdak lab also developed the first algorithm to allow for the nonlinear alignment of mass spectrometry metabolomics data. called xcms, where the "x" constitutes any chromatographic technology, it has been developed as an online tool. 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 and is a freely available electronic database (www.hmdb.ca) containing detailed information about small molecule metabolites found in the human body. it is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. the database is designed to contain or link three kinds of data: chemical data, clinical data and molecular biology/biochemistry data. the database contains 220,945 metabolite entries including both water-soluble and lipid soluble metabolites. additionally, 8,610 protein sequences (enzymes and transporters) are linked to these metabolite entries. each metabocard entry contains 130 data fields with 2/3 of the information being devoted to chemical/clinical data and the other 1/3 devoted to enzymatic or biochemical data. the version 3.5 of the hmdb 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 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. 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 collection facilitates high temporal resolution, and because they are always at dynamic equilibrium with the body, they can describe the host as a whole. genome can tell what could happen, transcriptome can tell what appears to be happening, proteome can tell what makes it happen and metabolome can tell what has happened and what is happening. metabolites part of a series onmicrobiomes plant microbiomes endosphere phyllosphere rhizosphere laimosphere root microbiome soil microbiome spermosphere marine microbiomes seagrass coral cetacean marine microbial symbiosis human microbiomes human milk fecal transplant gut–brain axis placental salivary uterine necrobiome other microbiomes mycobiome phycosphere built environment drosophila gut microbiota plant endophyte epiphyte rhizobacteria human gut lung oral skin vaginal marine microbial community initial acquisition accessible carbohydrates flora (microbiology) holobionts plant holobiont marine holobiont coral crab seagrass sponge rhodolith hologenomics hologenome evolution viromes bat human mangrove viriome virosphere related biomass partitioning dysbiosis gnotobiosis phytobiome quorum sensing biological dark matter microbial population biology microbial cooperation metagenomics viral metatranscriptomics metabolomics pan-genome superorganism symbiogenesis projects human microbiome project earth microbiome project category metabolites are the substrates, intermediates and products of metabolism. within the context of metabolomics, a metabolite is usually defined as any molecule less than 1.5 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 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 murdoch university and has been used in toxicology, disease diagnosis and a number of other fields. historically, the metabonomics approach was one of the first methods to apply the scope of systems biology to studies of metabolism. 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 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. analytical technologies key stages of a metabolomics study the typical workflow of metabolomics studies is shown in the figure. first, samples are collected from tissue, plasma, urine, saliva, cells, etc. next, metabolites extracted often with the addition of internal standards and derivatization. during sample analysis, metabolites are quantified (liquid chromatography or gas chromatography coupled with ms and/or nmr spectroscopy). the raw output data can be used for metabolite feature extraction 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. separation methods initially, analytes in a metabolomic sample comprise a highly complex mixture. this complex mixture can be