Hubbry Logo
MetabolomicsMetabolomicsMain
Open search
Metabolomics
Community hub
Metabolomics
logo
8 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
Metabolomics
Metabolomics
Metabolomics
View on Wikipedia
from Wikipedia

The central principle of biology showing the flow of information from DNA to the phenotype. Associated with each stage is the corresponding systems biology tool, from genomics to metabolomics.

Metabolomics is the scientific study of chemical processes involving 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.[1] The metabolome represents the complete set of metabolites in a biological cell, tissue, organ, or organism, which are the end products of cellular processes.[2] 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,[3] and thus, metabolomics provides a direct "functional readout of the physiological state" of an organism.[4] 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.[5] 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.

History

[edit]

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,[6] 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.[7] 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.[8][9] 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.[10]

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.[11] 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.[8][12] 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.[13][14]

In 1994 and 1996, liquid chromatography mass spectrometry metabolomics experiments[15][16] 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,[17][18] for characterizing human metabolites was developed in the Siuzdak laboratory at the Scripps Research Institute. 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,[19] 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,[20] it has since (2012)[21] 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.[22][23] Similar projects have been underway in several plant species, most notably Medicago truncatula[24] and Arabidopsis thaliana[25] for several years.

As late as mid-2010, metabolomics was still considered an "emerging field".[26] 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.[26]

In 2015, real-time metabolome profiling was demonstrated for the first time.[27]

Metabolome

[edit]
The human metabolome project
See also: Metabolome and Human Metabolome Database

The metabolome refers to the complete set of small-molecule (<1.5 kDa)[22] 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.[28][29] The word was coined in analogy with transcriptomics and proteomics; like the transcriptome and the proteome, the metabolome is dynamic, changing from second to second. Although the metabolome can be defined readily enough, it is not currently possible to analyse the entire range of metabolites by a single analytical method.

In January 2007, scientists at the University of Alberta and the University of Calgary completed the first draft of the human metabolome. The Human Metabolome Database (HMDB) is perhaps the most extensive public metabolomic spectral database to date[30] 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:

  1. Chemical data,
  2. Clinical data and
  3. 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.[31] The version 3.5 of the HMDB contains >16,000 endogenous metabolites, >1,500 drugs and >22,000 food constituents or food metabolites.[32] This information, available at the Human Metabolome Database and based on analysis of information available in the current scientific literature, is far from complete.[33] 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.[34][35]

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.[36] 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.[37] 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.[38]

Metabolites

[edit]
Part of a series on
Microbiomes
Plant microbiomes
Marine microbiomes
Human microbiomes
Other microbiomes
Microbiota
Holobionts
Viromes
Related
Projects

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.[22] 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.[39] In plant-based metabolomics, it is common to refer to "primary" and "secondary" metabolites.[3] 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.[40] By contrast, in human-based metabolomics, it is more common to describe metabolites as being either endogenous (produced by the host organism) or exogenous.[41][42] Metabolites of foreign substances such as drugs are termed xenometabolites.[43]

The metabolome derives from a large network of metabolic reactions, where outputs from one enzymatic chemical reaction are inputs to other chemical reactions. Such systems have been described as hypercycles.[citation needed]

Metabonomics

[edit]

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.[44] 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.[45][46][47]

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.[48]

Exometabolomics

[edit]
Main article: 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.[49]

Analytical technologies

[edit]
Key stages of a metabolomics study

The typical workflow of metabolomics studies is shown in the figure. First, samples are collected from tissue, plasma, urine, saliva, cells, etc. Next, metabolites are extracted often with the addition of internal standards and derivatization.[38] During sample analysis, metabolites are quantified (liquid chromatography or gas chromatography coupled with MS and/or NMR spectroscopy).[50] The raw output data can be used for metabolite feature extraction and further processed before statistical analysis (such as principal component analysis, PCA). Many bioinformatic tools and software are available to identify associations with disease states and outcomes, determine significant correlations, and characterize metabolic signatures with existing biological knowledge.[51]

Separation methods

[edit]

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 ionization detector (GC/FID) or a mass spectrometer (GC-MS). The method is especially useful for identification and quantification of small and volatile molecules.[52] However, a practical limitation of GC is the requirement of chemical derivatization for many biomolecules as only volatile chemicals can be analysed without derivatization. In cases where greater resolving power is required, two-dimensional chromatography (GCxGC) can be applied.

High performance liquid chromatography (HPLC) has emerged as the most common separation technique for metabolomic analysis. With the advent of electrospray ionization, HPLC was coupled to MS. In contrast with GC, HPLC has lower chromatographic resolution, but requires no derivatization for polar molecules, and separates molecules in the liquid phase. Additionally HPLC has the advantage that a much wider range of analytes can be measured with a higher sensitivity than GC methods.[53]

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.[54]

In direct-infusion mass spectrometry (DI-MS), sample is directly introduced into the spectrometer and separation steps are skipped. DI-MS can be employed to perform single cell metabolic analysis of human cells.[55]

Detection methods

[edit]

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

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

In the 2000s, surface-based mass analysis has seen a resurgence, with new MS technologies focused on increasing sensitivity, minimizing background, and reducing sample preparation. The ability to analyze metabolites directly from biofluids and tissues continues to challenge current MS technology, largely because of the limits imposed by the complexity of these samples, which contain thousands to tens of thousands of metabolites. Among the technologies being developed to address this challenge is Nanostructure-Initiator MS (NIMS),[56][57] 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.[citation needed] 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.[citation needed] Most recently, ion trap techniques such as orbitrap mass spectrometry are also applied to metabolomics research.[58]

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.[59][60]

Although NMR and MS are the most widely used modern-day techniques for detection, there are other methods in use. These include Fourier-transform ion cyclotron resonance,[61] ion-mobility spectrometry,[62] electrochemical detection (coupled to HPLC), Raman spectroscopy and radiolabel (when combined with thin-layer chromatography).[citation needed]

Table 1. Comparison of most common used metabolomics methods
Technology Sensitivity (LOD) Sample volume Compatible with gases Compatible with liquids Compatible with solids Start-up cost Can be used in metabolite imaging (MALDI or DESI) Advantages Disadvantages
GC-MS 0.5 μM 0.1-0.2 mL Yes Yes No <$300,000 No
  • Quantitative (with calibration)
  • Large body of software and databases for metabolite identification
  • Detects most organic and some inorganic molecules
  • Excellent separation reproductibility
  • Destructive (not recoverable)
  • Requires sample separation
  • Slow (20—40 min per sample)
LC-MS 0.5 nM 10—100 μL No Yes Yes >$300,000 Yes
  • Very flexible technology
  • Detects most organic and some inorganic molecules
  • Destructive (not recoverable)
  • Not very quantitative
  • Slow (15—40 min per sample)
  • Usually requires separation
NMR spectroscopy 5 μM 10—100 μL No Yes Yes >US$1 million Yes
  • Very flexible technology
  • Detects most organic and some inorganic molecules
  • Large instrument footprint
  • Cannot detect or identify salts and inorganic ions
  • Cannot detect non-protonated compounds
  • Requires large sample volumes (0.1—0.5 mL)

Statistical methods

[edit]

The data generated in metabolomics usually consist of measurements performed on subjects under various conditions. These measurements may be digitized spectra, or a list of metabolite features. In its simplest form, this generates a matrix with rows corresponding to subjects and columns corresponding with metabolite features (or vice versa).[8] Several statistical programs are currently available for analysis of both NMR and mass spectrometry data. A great number of free software are already available for the analysis of metabolomics data shown in the table. Some statistical tools listed in the table were designed for NMR data analyses were also useful for MS data.[63] For mass spectrometry data, software is available that identifies molecules that vary in subject groups on the basis of mass-over-charge value and sometimes retention time depending on the experimental design.[64]

Once metabolite data matrix is determined, unsupervised data reduction techniques (e.g. PCA) can be used to elucidate patterns and connections. In many studies, including those evaluating drug-toxicity and some disease models, the metabolites of interest are not known a priori. This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice. The most common of these methods includes principal component analysis (PCA) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation.[37] 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.[65] This clustering can elucidate patterns and assist in the determination of disease biomarkers – metabolites that correlate most with class membership.

Linear models are commonly used for metabolomics data, but are affected by multicollinearity. On the other hand, multivariate statistics are thriving methods for high-dimensional correlated metabolomics data, of which the most popular one is Projection to Latent Structures (PLS) regression and its classification version PLS-DA. Other data mining methods, such as random forest, support-vector machines, etc. are received increasing attention for untargeted metabolomics data analysis.[66] In the case of univariate methods, variables are analyzed one by one using classical statistics tools (such as Student's t-test, ANOVA or mixed models) and only these with sufficient small p-values are considered relevant.[36] However, correction strategies should be used to reduce false discoveries when multiple comparisons are conducted since there is no standard method for measuring the total amount of metabolites directly in untargeted metabolomics.[67] For multivariate analysis, models should always be validated to ensure that the results can be generalized.

Machine learning and data mining

[edit]

Machine learning is a powerful tool that can be used in metabolomics analysis. Recently, scientists have developed retention time prediction software. These tools allow researchers to apply artificial intelligence to the retention time prediction of small molecules in complex mixture, such as human plasma, plant extracts, foods, or microbial cultures. Retention time prediction increases the identification rate in liquid chromatography and can lead to an improved biological interpretation of metabolomics data.[68]

Key applications

[edit]

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.[48]

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.[69][70]

Metabologenomics is a novel approach to integrate metabolomics and genomics data by correlating microbial-exported metabolites with predicted biosynthetic genes.[71] This bioinformatics-based pairing method enables natural product discovery at a larger-scale by refining non-targeted metabolomic analyses to identify small molecules with related biosynthesis and to focus on those that may not have previously well known structures.

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

Nutrigenomics is a generalised term which links genomics, transcriptomics, proteomics and metabolomics to human nutrition. In general, in a given body fluid, a metabolome is influenced by endogenous factors such as age, sex, body composition and genetics as well as underlying pathologies. The large bowel microflora are also a very significant potential confounder of metabolic profiles and could be classified as either an endogenous or exogenous factor. The main exogenous factors are diet and drugs. Diet can then be broken down to nutrients and non-nutrients. Metabolomics is one means to determine a biological endpoint, or metabolic fingerprint, which reflects the balance of all these forces on an individual's metabolism.[72][73] Thanks to recent cost reductions, metabolomics has now become accessible for companion animals, such as pregnant dogs.[74][75]

Volatolomics is a development of metabolomics that studies volatile organic compounds (VOCs) emitted by a biological systems.

Plant metabolomics is designed to study the overall changes in metabolites of plant samples and then conduct deep data mining and chemometric analysis. Specialized metabolites are considered components of plant defense systems biosynthesized in response to biotic and abiotic stresses.[76] Metabolomics approaches have recently been used to assess the natural variance in metabolite content between individual plants, an approach with great potential for the improvement of the compositional quality of crops.[77]

See also

[edit]

References

[edit]

Further reading

[edit]
[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

Metabolomics

View on Grokipedia
from Grokipedia
Metabolomics is the systematic identification, quantification, and of all small-molecule metabolites in a biological sample, such as cells, tissues, or biofluids, providing a comprehensive profile of the metabolic state of an . This approach captures the end products of cellular processes, offering insights into physiological and pathological conditions that reflect the interplay of genes, environment, and lifestyle. Unlike or , which focus on potential or functional elements, metabolomics directly measures the functional output of biological systems. The roots of metabolomics trace back to early 20th-century biochemical studies, such as Archibald Garrod's 1902 observations on like , which linked genetic defects to metabolite accumulation. The field formalized in the 1990s amid the "omics" revolution, driven by advancements in analytical technologies that enabled high-throughput metabolite detection. By the early 2000s, metabolomics had emerged as a key component of , integrating with other disciplines to model complex biological networks. Key techniques in metabolomics include nuclear magnetic resonance (NMR) spectroscopy for non-destructive, quantitative analysis of metabolite structures, and mass spectrometry (MS) coupled with —such as gas chromatography-MS (GC-MS) for volatile compounds and liquid chromatography-MS (LC-MS) for polar metabolites—for high-sensitivity detection of thousands of molecules. These methods generate vast datasets requiring chemometric tools for peak identification, alignment, and statistical interpretation to distinguish biological signals from noise. Challenges include metabolite instability, standardization across platforms, and the need for comprehensive databases, addressed by initiatives like the Metabolomics Standards Initiative. Applications of metabolomics span biomarker discovery for diseases like cancer and cardiovascular disorders, where altered metabolite profiles—such as elevated 2-hydroxyglutarate in gliomas—aid and . In precision , it supports personalized therapeutics by assessing , , and , as seen in pharmacogenomic studies of responses. Beyond human health, metabolomics informs , plant breeding for enhanced , and microbial , such as gut microbiota's role in absorption. Overall, it bridges with phenotypic outcomes, advancing holistic understandings of health and .

Overview

Definition and Scope

Metabolomics is defined as the comprehensive, qualitative, and quantitative analysis of all small-molecule metabolites present in a , such as cells, tissues, organs, or organisms, under specific physiological or environmental conditions. This field emerged as a key component of , focusing on the —the complete set of metabolites within a system—to capture the dynamic biochemical state at a given time. The scope of metabolomics encompasses both endogenous metabolites, which are produced by the organism's own metabolic processes (such as , sugars, and ), and exogenous metabolites derived from external sources like diet, drugs, or microbial interactions. These metabolites are typically low-molecular-weight compounds under 1,500 Da, excluding larger macromolecules such as proteins, nucleic acids, or . This boundary ensures focus on small molecules that serve as substrates, intermediates, or products in metabolic reactions, providing a snapshot of biochemical activity without overlapping into other disciplines. The primary objectives of metabolomics include identifying and characterizing metabolic profiles to map biochemical pathways, elucidating how these pathways respond to genetic variations, environmental stressors, or states, and linking metabolite changes to phenotypes or physiological outcomes. By quantifying alterations in levels, researchers can infer functional impacts of upstream biological processes, such as or protein activity, offering insights into how organisms adapt to internal or external perturbations. In distinction from other omics fields like or , metabolomics serves as the downstream readout of gene-protein interactions, directly reflecting the integrated effects of genetic, transcriptional, and on cellular function and environmental responses. This positions metabolomics as a proximal measure of , bridging the gap between molecular mechanisms and observable traits in a way that upstream cannot achieve alone.

Importance and Interdisciplinary Role

Metabolomics serves as a vital bridge between , transcriptomics, and , integrating these datasets to connect with and reveal underlying functional . By analyzing the end products of cellular processes, it uncovers dynamic dysregulations that genomics and transcriptomics alone cannot fully explain, providing deeper insights into mechanisms such as those in cancer and . For example, joint transcriptomic-metabolomic analyses have highlighted enhanced enzyme-metabolite coupling in , illustrating how metabolomics elucidates molecular interactions at the systems level. This interdisciplinary integration supports comprehensive , advancing the understanding of complex biological responses. The field significantly contributes to through discovery and the evaluation of individual responses to therapeutic interventions or dietary changes. In , baseline metabolomic profiles predict drug efficacy, such as elevated acids indicating better LDL reduction with simvastatin treatment. Similarly, in , metabolomics identifies metabotypes that stratify dietary responses, enabling tailored recommendations; for instance, branched-chain serve as s for risk influenced by diet. These applications facilitate patient stratification and optimize treatment outcomes by capturing personalized metabolic variations. Metabolomics extends across disciplines, including , , and , where it informs exposure effects and environmental impacts. In , known as toxicometabolomics, it profiles metabolic perturbations from chemical exposures, aiding and mechanism elucidation. Nutritional applications reveal dietary intake biomarkers, such as those for or whole grains, linking consumption to outcomes like risk. In , metabolomics assesses effects on , for example, detecting altered metabolism and in exposed to persistent organic pollutants. Metabolic fingerprinting further enhances diagnostics by generating rapid metabolite pattern profiles for disease identification using techniques like NMR and . A key quantitative impact of metabolomics lies in its production of high-dimensional datasets, often encompassing thousands of metabolite features per sample, which enable holistic, systems-wide views of biological states. This scale captures the complexity of metabolic networks, supporting integrative analyses that reveal emergent properties in and .

Historical Development

Early Foundations

The foundations of metabolomics trace back to 19th-century biochemistry, when chemists began systematically identifying and characterizing small organic molecules central to biological processes. Pioneers such as advanced the understanding of metabolic transformations through studies on , respiration, and nutrient breakdown, establishing the groundwork for viewing as a network of chemical reactions. By the late 1800s, the structural elucidation of key metabolites like glucose, , and had been achieved, laying the conceptual basis for later quantitative analyses of biological fluids. In the early 20th century, Archibald Garrod's seminal work further solidified these roots by introducing the concept of "" in his 1908 Croonian Lectures and subsequent book. Garrod described disorders such as , , , and pentosuria as inherited conditions arising from blocks in specific metabolic pathways, leading to the accumulation of abnormal metabolites detectable in . This approach marked the first targeted profiling of metabolites for diagnostic purposes, emphasizing how genetic defects disrupt normal biochemical flows. From the through the , extensions of Garrod's ideas involved more routine examination of urinary metabolite patterns to identify inborn errors, facilitated by emerging separation techniques. Researchers employed to resolve and other compounds in urine samples, enabling population-based screening for disorders like and . These methods, though qualitative and labor-intensive, represented early systematic efforts to capture metabolic snapshots, often revealing diagnostic patterns of elevated or missing compounds in affected individuals. A pivotal advancement came in 1971 with the work of Evan C. Horning and colleagues, who applied gas chromatography-mass spectrometry (GC-MS) to generate comprehensive metabolic profiles from human , , and tissues. This technique allowed the simultaneous detection of steroids, organic acids, and other volatiles, demonstrating the feasibility of broad-spectrum analysis and coining the phrase "metabolic profiles" for such datasets. Although performed in clinical and biochemical contexts before the , these pre-high-throughput efforts in focused on targeted diagnostics rather than global overviews. The formal term ""—denoting the entirety of metabolites within a —was introduced later in 1998 by Stephen G. Oliver and colleagues, retrospectively framing these historical analyses as precursors to the field. The discovery of the (Krebs cycle) in 1937 by Hans A. Krebs and William A. Johnson exemplified early mapping, integrating 19th- and early 20th-century biochemical insights into a coherent cycle of carbon oxidation that underpins cellular energy production. This elucidation of interconnected reactions highlighted the dynamic nature of interconversions, influencing subsequent profiling strategies.

Key Milestones and Technological Advances

The field of metabolomics emerged in the 1990s as a distinct discipline, driven by early applications of (NMR) spectroscopy and (MS) to profile metabolites in and microbial systems. These initial studies focused on comprehensive metabolic fingerprinting to link genotypes to phenotypes, marking a shift from targeted analyses to global approaches. In 2000, a study by Oliver Fiehn and colleagues on functional genomics demonstrated the potential of gas chromatography-MS for identifying hundreds of metabolites in Arabidopsis mutants, establishing metabolomics as complementary to and . A seminal contribution came in 2002 when Oliver Fiehn defined metabolomics as "a post-genomic technology for the systematic study of the unique chemical fingerprints that specific cellular processes leave behind, specifically using the complete set of metabolites within a cell, tissue, or ." In the 2000s, organizational and database efforts accelerated the field's growth, integrating metabolomics with through international consortia. The Metabolomics Society was founded in 2004 to foster collaboration, standardize methods, and promote education among researchers worldwide. That same year, the Human Metabolome Project (HMP) was launched to systematically identify and quantify all detectable metabolites in human tissues, biofluids, and cells, initially cataloging approximately 2,200 compounds by 2007 via high-throughput NMR and MS techniques. This project not only expanded knowledge of the human metabolome but also facilitated cross-disciplinary integration, such as combining metabolomic data with genomic profiles to uncover disease biomarkers. The 2010s and 2020s witnessed transformative technological advances, particularly in untargeted metabolomics, which enables broad-spectrum detection without prior knowledge of metabolites, and spatial metabolomics for mapping metabolite distributions in tissues. Untargeted approaches evolved with software like XCMS (introduced in 2006 but refined through the 2010s) for processing LC-MS data, allowing detection of thousands of features per sample. Spatial metabolomics advanced significantly with (MALDI) imaging MS, which gained prominence around 2015 for high-resolution metabolite localization in biological tissues, such as visualizing lipid gradients in plant roots or drug metabolites in tumors. Concurrently, (AI) has revolutionized metabolite annotation by predicting structures from MS spectra using models, improving accuracy for unknown compounds in complex datasets. By 2025, databases like the (HMDB) have grown to over 220,000 metabolite entries, reflecting expanded coverage from ongoing HMP updates and global contributions. A pivotal milestone aiding metabolite localization was the 2012 Nobel Prize in Chemistry awarded to Eric Betzig, , and for developing super-resolved fluorescence microscopy, which overcomes the diffraction limit to achieve nanoscale resolution. This breakthrough has enhanced spatial metabolomics by enabling precise correlation of fluorescently labeled s with MS imaging data, revealing subcellular distributions previously unattainable.

Core Concepts

The Metabolome

The refers to the complete set of all small-molecule metabolites present within a , such as a cell, tissue, organ, or , at a specific point in time. This collection represents the downstream products of genomic, transcriptomic, and proteomic activities, providing a snapshot of physiological state influenced by genetic makeup, environmental factors, and physiological conditions. First coined in 1998 to describe the metabolites synthesized by an in parallel to the and , the encapsulates the functional output of cellular metabolism. The metabolome can be categorized into a core or constitutional component, consisting of stable, conserved metabolites essential for basic cellular functions and present across similar biological systems, and a dynamic or fluxional component, which includes condition-specific metabolites that vary with external stimuli or internal changes. The core metabolome typically involves a relatively small number of fundamental compounds, such as those in central metabolic pathways, while the dynamic portion reflects adaptive responses to factors like stress or diet. This distinction highlights the metabolome's dual nature: a stable foundation overlaid with transient variability. Estimates suggest the human metabolome encompasses approximately 100,000 distinct compounds, though databases like the (HMDB) catalog over 250,000 potential entries, including detected, quantified, and predicted metabolites, underscoring its vast chemical diversity. This diversity spans major classes such as , , sugars, organic acids, and , with many exhibiting structural complexity and functional specificity. Full remains challenging due to the instability of certain metabolites, their occurrence at low concentrations, and the influence of variability factors like diurnal cycles, age, and sex, which can alter profiles significantly across individuals and time points. These elements complicate comprehensive mapping but emphasize the 's role as a sensitive indicator of and environmental interactions.

Metabolites: Types and Functions

Metabolites serve as the end products of cellular regulatory processes, offering a dynamic reflection of an organism's real-time physiological state in response to genetic and environmental influences, unlike the relatively static . This positions them as key indicators in metabolomics, where the —the comprehensive set of all —captures the integrated outcomes of metabolic pathways. Metabolites are commonly classified into primary and secondary categories based on their essentiality to core biological functions. Primary metabolites are vital for growth, development, and reproduction, encompassing compounds such as , , carbohydrates, , and organic acids that support fundamental processes like protein synthesis and energy production. In contrast, secondary metabolites, such as alkaloids, terpenoids, and , are not required for basic cellular maintenance but confer specialized advantages, including defense against pathogens, attraction of pollinators, and adaptation to environmental stresses. An alternative classification organizes metabolites by their chemical composition, highlighting their diversity. Organic metabolites form the majority and include carbohydrates (e.g., glucose for energy), (e.g., phospholipids for membranes), and organic acids (e.g., in metabolic cycles), all of which are carbon-based and endogenously produced. Inorganic metabolites consist of small ions like Na⁺ (maintaining osmotic balance and nerve signaling), K⁺ (regulating membrane potentials), and Ca²⁺ (mediating and enzymatic activation). Exogenous metabolites, often termed xenobiotics, are externally derived compounds such as drugs, pollutants, and dietary components that undergo within the organism. The functions of metabolites are multifaceted, underpinning cellular and adaptation. They facilitate energy storage through molecules like and triglycerides, which release energy during demand. In signaling, metabolites act as hormones or second messengers, exemplified by hormones (e.g., ) that regulate stress responses and cyclic AMP that propagates intracellular signals. Structural roles are evident in components like in cell walls and phospholipids in biomembranes, providing mechanical support and compartmentalization. Additionally, metabolites exert regulatory effects via allosteric modulation of enzymes, such as ATP inhibiting to prevent overproduction, thereby fine-tuning metabolic flux. Metabonomics represents a approach to studying the global metabolic responses of to pathophysiological stimuli, environmental changes, or genetic modifications, emphasizing the dynamic, time-related multiparametric changes in the through techniques like multivariate statistical analysis of (NMR) spectroscopic data. Introduced by Nicholson and colleagues in 1999, this field highlights holistic metabolic perturbations and their integration with physiological processes, often overlapping with metabolomics but with a stronger focus on functional dynamics rather than comprehensive static profiling. Historically, the distinction between metabonomics and metabolomics arose from their origins: metabonomics was coined in the UK by the Nicholson group to describe broader, response-oriented metabolic studies, while metabolomics emerged in the US through Oliver Fiehn's 2001 work, centering on the systematic identification and quantification of all metabolites in a biological system to link genotypes to phenotypes. By the mid-2020s, however, the terms have become largely interchangeable in scientific literature, both encompassing the comprehensive analysis of small-molecule metabolites under various conditions, with methodological overlaps in technologies like mass spectrometry and NMR. Exometabolomics, a specialized subfield also termed metabolic footprinting, examines extracellular metabolites secreted or excreted by organisms into their surrounding environment, such as culture media, biofilms, or waste streams, providing insights into intercellular interactions and resource exchange. This approach is particularly valuable in microbial ecology for mapping community-level metabolism and in industrial applications like fermentation monitoring to detect contamination or optimize yields without disrupting the system. A distinctive feature of exometabolomics is its facilitation of non-invasive sampling, exemplified by breathomics, which profiles volatile organic compounds in exhaled breath to noninvasively evaluate metabolic states in clinical settings.00163-2)

Analytical Technologies

Sample Preparation and Separation Methods

Sample preparation in metabolomics begins with the collection and handling of diverse biological samples, including biofluids such as , plasma, , and , as well as tissues and cells. For biofluids, initial steps involve to remove , typically at 4°C and 15,000×g for 10 minutes, followed by storage at -80°C to preserve integrity. Tissues require homogenization, often using mechanical disruption like bead milling or freeze-thaw cycles, while cells are harvested via or to capture intracellular s. is essential to halt ongoing metabolic processes and prevent artifactual changes; common methods include rapid addition of cold (e.g., -40°C to -48°C at 60% concentration) for polar metabolites or snap-freezing for tissues and cells, minimizing leakage and degradation. These steps are critical as metabolite instability, driven by enzymatic activity, can lead to rapid turnover if not addressed promptly. Extraction techniques isolate metabolites from complex matrices, with solvent-based methods predominating for their efficiency in recovering diverse compound classes. For polar and semi-polar metabolites in biofluids and cells, or precipitation (e.g., 1:8 sample-to-solvent ratio) effectively removes proteins and yields high metabolite coverage, detecting up to 201 compounds in with coefficients of variation below 30%. extraction often employs the - mixture (2:1 ratio, known as the Folch method) or Bligh-Dyer variant (chloroform::water, 1:2:0.8), which partitions non-polar into the organic phase while aqueous phases capture polars. Multi-phase extractions, such as sequential solvent applications, provide orthogonal data by fractionating metabolites into polar, semi-polar, and non-polar groups, enhancing coverage in tissues where matrix effects from proteins and salts can suppress recovery. Derivatization is particularly vital for gas chromatography-compatible analyses, converting non-volatile or polar compounds into volatile derivatives; trimethylsilylation with agents like MSTFA replaces active hydrogens on hydroxyl, carboxyl, amino, or groups, enabling analysis of , sugars, and organic acids. Challenges in extraction include metabolite instability during processing and matrix interferences, which can reduce reproducibility, necessitating optimized protocols tailored to sample type. Separation methods fractionate extracted metabolites prior to downstream analysis, with chromatography techniques selected based on compound properties like volatility, polarity, and charge. (GC) excels for volatile and semi-volatile metabolites, operating on partitioning principles where analytes distribute between a mobile gas phase and a non-polar stationary liquid film (e.g., RTX-5MS column); temperature gradients from 50°C to 330°C at 20°C/min resolve compounds like fatty acids, sterols, and derivatized sugars with peak widths of 2-3 seconds. Liquid chromatography (LC) addresses polar and non-volatile metabolites; reversed-phase LC (RPLC) uses non-polar stationary phases (e.g., C18) to separate based on hydrophobicity, ideal for and steroids, while hydrophilic interaction LC (HILIC) employs polar phases to retain charged like and nucleosides via water-enriched layers. (CE) separates charged molecules through electrophoretic mobility in an within a fused-silica , influenced by charge-to-size ratio and electroosmotic flow; it is suited for ionic metabolites such as organic acids, , and phosphorylated sugars, often in positive mode (low ) for cations or negative mode for anions. Multidimensional approaches, combining RPLC and HILIC, can increase metabolite detection by up to 108% in plasma by orthogonally resolving diverse polarities. These methods mitigate challenges like co-elution in complex samples, though metabolite instability and matrix effects remain hurdles requiring careful optimization.

Detection and Quantification Techniques

Mass spectrometry (MS) is a cornerstone technique in metabolomics for the detection and quantification of metabolites, offering high sensitivity and the ability to analyze complex mixtures by measuring the (m/z) of ions. In mass spectrometry (ESI-MS), liquid samples are nebulized into charged droplets, enabling soft ionization suitable for polar and ionic metabolites commonly found in biological samples; this method is often coupled with liquid chromatography (LC) for enhanced separation prior to detection. mass spectrometry (MALDI-MS), on the other hand, uses a to desorb and ionize analytes from a solid matrix, making it particularly effective for spatial metabolomics in tissue imaging where direct profiling without extensive is needed. Metabolite identification in MS relies on accurate m/z measurements, often achieved with high-resolution instruments like or time-of-flight (TOF) analyzers, while tandem MS (MS/MS) provides structural confirmation through fragmentation patterns generated by colliding selected precursor ions with a gas, producing diagnostic daughter ions for database matching. Nuclear magnetic resonance (NMR) serves as a complementary, non-destructive detection method in metabolomics, excelling in structural elucidation without requiring or derivatization. It detects metabolites based on their chemical shifts, which arise from the interaction of atomic nuclei (typically or carbon) with an external , allowing identification of molecular skeletons and functional groups in aqueous samples. Proton NMR (¹H-NMR) is the most routine approach for metabolomic profiling due to its high natural abundance and sensitivity for common metabolites like and sugars, providing quantitative data proportional to signal integrals under standardized conditions. Other spectroscopic techniques, such as infrared (FTIR) and ultraviolet-visible (UV-Vis) spectroscopy, are applied for targeted detection of specific metabolite classes, though less comprehensively than MS or NMR. FTIR identifies vibrational modes of molecular bonds, enabling rapid, label-free profiling of carbohydrates, , and proteins in crude extracts via characteristic absorption bands in the spectrum. UV-Vis detects chromophores in aromatic or conjugated metabolites, such as and , through absorbance at specific wavelengths, often used in hyphenated systems for initial screening. Hybrid systems like LC-MS combine chromatographic separation with MS detection to achieve high resolution, resolving thousands of features in a single run and improving specificity for low-abundance metabolites in complex matrices. Quantification in metabolomics distinguishes between relative abundance (e.g., peak area ratios) and absolute measurements, with the latter providing concentrations in molar units essential for pathway modeling. Absolute quantification typically employs MS, where stable isotope-labeled standards (e.g., ¹³C or ²H analogs) are added to samples, compensating for matrix effects and variability to yield accurate levels via the ratio of heavy to light s. Detection limits for MS-based methods reach the femtomole (fmol) range, with limits of detection (LODs) as low as 50 fmol for many metabolites under optimized conditions, enabling analysis of trace-level compounds in biofluids. As of 2025, (IMS) is integrated into MS workflows, adding a gas-phase separation dimension based on ion shape and size to enhance resolution of isobaric metabolites and improve quantification in high-throughput metabolomics; recent advancements include cyclic IMS for spatial metabolomics applications.

Data Analysis Methods

Statistical Approaches

In metabolomics, statistical approaches begin with preprocessing to ensure and comparability across samples. Normalization adjusts for systematic variations such as differences in sample concentration or instrument response, with common methods including sum normalization, which scales intensities to a constant total current, and normalization using added reference compounds to correct for technical variability. Imputation addresses missing values arising from detection limits or technical noise, often employing techniques like k-nearest neighbors or zero-filling, though caution is advised as imputation can introduce bias if missingness is not random. Scaling methods, such as Pareto scaling (which divides variables by the square root of their standard deviation) or unit variance scaling (dividing by standard deviation), mitigate the dominance of high-abundance metabolites in downstream analyses. Univariate statistical methods evaluate individual metabolites independently to identify significant differences between groups, facilitating . T-tests are applied for comparing two groups, assuming normality of the data distribution, while analysis of variance (ANOVA) extends this to multiple groups, testing for overall differences in means. Given the high dimensionality of metabolomics datasets, where thousands of features are tested simultaneously, multiple testing corrections are essential; the Benjamini-Hochberg procedure controls the (FDR) by adjusting p-values to limit the proportion of false positives among significant results. These methods assume among features and normality (or large sample sizes for applicability), though metabolomics data often violate these, necessitating robust variants or transformations. Multivariate statistical approaches handle the correlative nature of metabolomics data by analyzing multiple variables simultaneously, enabling and pattern detection. (PCA) is an method that decomposes the centered data matrix X\mathbf{X} into principal components, expressed as: X=TPT+E\mathbf{X} = \mathbf{T} \mathbf{P}^T + \mathbf{E} where T\mathbf{T} contains the scores (projections of samples onto principal components), P\mathbf{P} the loadings (variable contributions to components), and E\mathbf{E} the residual matrix. PCA assumes linear relationships, of observations, and often for inference, though it does not strictly require normality for decomposition. Score plots visualize sample clustering in low-dimensional space, revealing outliers or group separations, while loading plots highlight influential metabolites driving variance. Partial least squares discriminant analysis (PLS-DA), a supervised extension, maximizes between X\mathbf{X} and a class indicator matrix Y\mathbf{Y}, enhancing class separation; its score plots show group discrimination, and loading plots identify discriminatory features, with validation via cross-validation to avoid . These techniques provide interpretable visualizations but require careful preprocessing to meet assumptions of and .

Machine Learning and Data Mining

Machine learning (ML) and techniques have become essential for handling the high-dimensional, noisy datasets generated in metabolomics, enabling the discovery of patterns, biomarkers, and predictive models that statistical methods alone cannot efficiently uncover. Supervised ML approaches, in particular, excel at and regression tasks by training on to predict outcomes such as states or treatment responses. Unsupervised methods facilitate exploratory analysis by identifying inherent structures without prior labels, while tools like network analysis reveal complex interactions among metabolites. These methods often build on preprocessing steps from statistical approaches, such as normalization, to mitigate variability in data. Recent integrations with large databases further enhance accuracy, though challenges like persist in high-dimensional spaces. Supervised ML algorithms, including random forests (RF) and support vector machines (SVM), are widely applied for biomarker prediction in metabolomics. RF, an ensemble method that constructs multiple decision trees and aggregates their predictions, is particularly robust to noisy data and handles common in metabolite profiles. SVM, effective for high-dimensional classification, separates data using hyperplanes and has demonstrated superior performance in diagnosing metabolic-associated (MASH) by integrating metabolomic features, achieving areas under the curve (AUC) exceeding 0.90 in validation sets. Feature importance in RF is often assessed via the Gini index, which measures impurity reduction at each split, allowing prioritization of influential metabolites like in models. A 10-metabolite RF-based model, for instance, predicted gastric cancer with 90.5% sensitivity in external cohorts, underscoring RF's interpretability and generalizability. Unsupervised ML techniques, such as , group metabolomic profiles to reveal subgroups or subtypes without predefined outcomes. builds a tree-like structure of similarities, often using Ward's linkage, and has demonstrated substantial compositional differences in microbial communities via metabolomics, enabling objective similarity assessments across samples; for example, it has proven effective for stratifying patients based on pre-treatment blood metabolomes, where it identified high- and low-risk groups linked to survival outcomes. Sparse variants of further refine this by selecting key , improving clustering quality in untargeted assays. partitions data into k clusters by minimizing intra-cluster variance. Association rule mining, exemplified by the , uncovers co-occurring patterns by identifying frequent itemsets with support and confidence thresholds; in metabolomics-inspired studies, it has revealed disorder co-occurrence networks in datasets, adaptable to metabolite synergies in biological pathways. Data mining in metabolomics emphasizes network-based analyses to model metabolite interactions as graphs. Tools like Cytoscape, through plugins such as MetScape, visualize and interpret metabolomic data within human metabolic networks, integrating and compound levels to highlight pathway enrichments. These graphs reveal connectivity patterns, such as correlations between metabolites in datasets, aiding in the inference of functional modules. methods, including autoencoders, address spectral denoising by learning compressed representations to reconstruct clean signals from noisy inputs. Denoising autoencoders have reduced systematic errors in large-scale untargeted metabolomics data, such as in GC-MS workflows, lowering sample variability by up to 50% and enhancing peak detection reliability; similar approaches apply to LC-MS data. As of 2025, ML integration with databases like the Global Natural Products Social Molecular Networking (GNPS) platform has advanced spectral through ensemble models that predict fragmentation patterns. The Ensemble Spectral Prediction (ESP) model, for example, combines neural networks to match MS/MS spectra against libraries, improving annotation accuracy for diverse metabolites in untargeted workflows. However, high-dimensional metabolomics data—often featuring thousands of features per sample—exacerbates the curse of dimensionality, leading to data sparsity and increased risk in ML models. Techniques like (e.g., as preprocessing) and regularization mitigate this, but are increasingly used to control false discovery rates in annotation pipelines. For supervised ML in dietary discovery, such as urinary indicators of intake under habitual diets, —an ensemble method based on gradient-boosted decision trees—has achieved high accuracy (e.g., 88% with a 7-metabolite panel).

Applications

Biomedical and Clinical Uses

Metabolomics plays a pivotal role in biomedical and clinical applications by enabling the identification of disease-specific metabolic signatures, which facilitate early , stratification, and personalized treatment strategies in human health. Through high-throughput analysis of metabolites in biofluids such as , , and , researchers can detect perturbations in metabolic pathways associated with various pathologies, leveraging techniques like (NMR) spectroscopy and for precise quantification. This approach has advanced precision medicine by integrating metabolomic data with and to uncover actionable insights into disease mechanisms. In biomarker discovery, metabolomics has been instrumental in identifying metabolites indicative of cancer and cardiovascular diseases. For instance, NMR-based serum profiling reveals elevated lactate levels in patients with metastatic cancers, reflecting the Warburg effect where tumor cells favor aerobic , allowing for non-invasive detection and monitoring of disease progression. Similarly, elevated branched-chain (BCAAs) in plasma serve as biomarkers for cardiometabolic risks, including and , as their accumulation correlates with impaired and predicts adverse cardiovascular outcomes independent of traditional risk factors. Pharmacometabolomics extends these principles to predict individual drug responses and toxicities, particularly for hepatotoxic agents like acetaminophen. Pre-dose urinary metabolomic profiles can forecast susceptibility to acetaminophen-induced liver injury by identifying baseline variations in endogenous metabolites, such as those linked to glutathione synthesis pathways; upon overdose, rapid glutathione depletion allows the toxic metabolite N-acetyl-p-benzoquinone imide (NAPQI) to bind cellular proteins, and pharmacometabolomic models have shown potential for early prediction of hepatotoxicity in clinical cohorts. In clinical trials for precision medicine, metabolomics supports phenotyping to tailor interventions, as seen in research where altered lipid profiles, including tau-associated phospholipids and , correlate with neurodegeneration and cognitive decline, enabling stratification of patients for targeted therapies like anti-tau agents. FDA-cleared tandem mass spectrometry-based systems for of , such as and acylcarnitine profiling, are standard diagnostics, detecting over 30 disorders with high sensitivity to initiate early interventions like . Longitudinal metabolomic studies further elucidate microbiome-metabolite interactions in health and disease, revealing how gut-derived influence and progression in conditions like .

Environmental, Agricultural, and Industrial Applications

Metabolomics has emerged as a vital tool for monitoring environmental , particularly in aquatic ecosystems, by profiling metabolic changes in organisms exposed to contaminants such as endocrine disruptors. (MS)-based approaches, including untargeted metabolomics, enable the detection of subtle disruptions in metabolic pathways, such as alterations in and in and following exposure to pollutants like and . For instance, studies on marine mussels have revealed downregulation of energy-related metabolites under multi-contaminant stress, providing biomarkers for early detection without prior knowledge of specific toxins. In , metabolomics supports crop improvement by identifying metabolic responses to abiotic stresses, such as , which often involve the accumulation of protective secondary metabolites like . In and , stress triggers upregulation of phenylpropanoid pathways, leading to increased levels of such as and glycosides, which enhance capacity and stress tolerance. Additionally, metabolic quantitative trait loci (mQTLs) mapping integrates metabolomics with to pinpoint genetic variants associated with desirable traits; for example, in , over 2,800 mQTLs have been identified for 900 metabolites, facilitating marker-assisted breeding for resilient varieties. Industrial applications of metabolomics, particularly exometabolomics, optimize bioprocesses by analyzing extracellular metabolites in media to improve production. In fermentation for , exometabolomics via liquid chromatography-MS identifies inhibitory compounds like and acetic acid, allowing strain engineering to improve yields through enhanced pathways. In food industries, volatile metabolomics profiling using gas chromatography-MS assesses quality attributes; for wine, it distinguishes varietal aromas by quantifying and esters, correlating volatile profiles with sensory scores to refine production and authentication. Advancements in the include field-deployable portable spectrometers, which enable real-time metabolomics analysis in , such as on-site profiling of plant volatiles for pest detection, reducing reliance on lab-based methods. These technologies contribute to by minimizing use; metabolomics-guided breeding has developed varieties with inherent resistance, potentially cutting chemical inputs while maintaining yields, as demonstrated in studies. Exometabolomics briefly informs industrial media optimization, linking extracellular profiles to process efficiency without delving into intracellular details.

Challenges and Future Directions

Current Limitations

One major technical limitation in metabolomics is the incomplete coverage of the metabolome, with typical studies detecting only about 1-5% (or less) of the estimated total metabolites due to the vast chemical diversity and the constraints of current analytical platforms. This partial detection arises from challenges in extracting, separating, and identifying the full spectrum of polar, non-polar, and volatile compounds across biological samples. Additionally, remains a significant hurdle, as variations in protocols across laboratories hinder data comparability; the Metabolomics Standards Initiative (MSI) has proposed guidelines for reporting experimental design, , and to address this, yet adherence is inconsistent. Biologically, metabolite instability poses a key challenge, as many compounds degrade rapidly due to enzymatic activity or chemical reactivity during sample collection and processing, leading to altered profiles that do not reflect states. Compartmentation further complicates analysis, with metabolites exhibiting tissue-specific concentration gradients and subcellular localization that are often lost in bulk tissue extractions, masking heterogeneous distributions within organs or cells. The gut introduces additional confounders, as microbial metabolites can overlap with host-derived ones in biofluids like plasma or , influencing interpretations of systemic metabolic changes without integrated multi-omics approaches. Ethical concerns in clinical metabolomics center on data privacy, particularly for patient-derived samples where metabolite profiles may inadvertently reveal sensitive health information, necessitating robust consent processes and secure data-sharing frameworks compliant with regulations like GDPR. issues exacerbate these challenges, with a documented crisis in high-throughput studies stemming from practices like p-hacking—selective reporting of statistically significant results—which undermines biomarker validation across cohorts. Statistical tools from data analysis methods can mitigate some variability, but they cannot fully resolve underlying experimental inconsistencies. A quantitative barrier is the metabolome's vast , spanning up to 12 orders of magnitude in concentration, which exceeds the typical 4-5 orders achievable by instruments, often requiring hybrid targeted-untargeted strategies to capture both low- and high-abundance metabolites. One prominent emerging trend in metabolomics is the integration of metabolomic data with other layers, such as and , to uncover complex biological relationships. Tools like MetaboAnalyst 6.0 facilitate this multi-omics analysis by providing unified platforms for processing targeted and untargeted data, enabling joint statistical modeling and pathway enrichment across datasets. For instance, integrating single nucleotide polymorphisms (SNPs) from genomic data with metabolite profiles allows researchers to link genetic variants to metabolic perturbations, as demonstrated in studies using integrative to identify SNP-metabolite associations in disease contexts. This approach has been applied to reveal how genetic variants activate latent metabolic pathways in microbial systems, enhancing understanding of phenotypic diversity. Technological advancements are pushing metabolomics toward higher resolution at the cellular level, particularly with single-cell metabolomics enabled by techniques like nano-electrospray ionization mass spectrometry (nano-ESI MS). Developed in the 2020s, nano-ESI MS allows direct analysis of metabolites from individual cells without extensive sample preparation, achieving detection limits in the attomole range and revealing heterogeneity in cellular metabolism. Complementary to this, artificial intelligence (AI) is transforming de novo structure prediction for unknown metabolites, with software like SIRIUS 4 integrating fragmentation tree analysis and machine learning-based scoring to annotate molecular formulas and structures from tandem mass spectra with over 70% accuracy in benchmark datasets. These tools reduce reliance on reference databases, enabling identification of novel compounds in diverse biological samples. By 2025, metabolomics is witnessing the rise of portable devices, which enable on-site analysis of s in field settings, such as or clinical diagnostics, with compact systems achieving sufficient sensitivity for volatile organic compounds and small molecules. Spatial metabolomics, combining with metabolomics workflows, is another key trend, allowing tissue mapping of metabolite distributions at micrometer resolution to study in diseases like cancer. Global consortia, including the NIH Metabolomics Consortium and initiatives like the Global Natural Products Social Molecular Networking (GNPS), are driving database expansion, with resources like the (HMDB) surpassing 220,000 entries and spectral libraries growing to support broader annotation of untargeted data. Looking ahead, fluxomics represents a future direction in metabolomics, focusing on dynamic pathway rates through isotope tracing methods like 13C-labeling. In 13C metabolic flux analysis (13C-MFA), steady-state labeling patterns from 13C tracers are used to quantify intracellular fluxes, with flux rates (v) estimated from measurements of pool sizes (M), isotopic enrichment patterns, and extracellular rates by fitting comprehensive mathematical models of labeling dynamics, under steady-state or non-stationary assumptions. This approach, advanced by tools for non-stationary labeling experiments, provides quantitative insights into activity beyond static snapshots.

References

  1. https://pmc.ncbi.nlm.nih.gov/articles/PMC2140247/
  2. https://pmc.ncbi.nlm.nih.gov/articles/PMC3682684/
  3. https://pmc.ncbi.nlm.nih.gov/articles/PMC3334318/
  4. https://pmc.ncbi.nlm.nih.gov/articles/PMC4850886/
  5. https://www.nature.com/articles/npp2008174
  6. https://www.sciencedirect.com/topics/medicine-and-dentistry/metabolome
  7. https://www.ebi.ac.uk/training/online/courses/metabolomics-introduction/what-is-metabolomics/small-molecules/
  8. https://pubmed.ncbi.nlm.nih.gov/33598670/
  9. https://www.sciencedirect.com/science/article/pii/S0010482524014781
  10. https://pmc.ncbi.nlm.nih.gov/articles/PMC7281435/
  11. https://doi.org/10.1038/srep29662
  12. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1215-1
  13. https://pmc.ncbi.nlm.nih.gov/articles/PMC4055080/
  14. https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2019.00041/full
  15. https://www.mdpi.com/1420-3049/22/7/1173
  16. https://pmc.ncbi.nlm.nih.gov/articles/PMC3808515/
  17. https://www.mdpi.com/2305-6304/10/7/380
  18. https://pubmed.ncbi.nlm.nih.gov/17924839/
  19. https://pmc.ncbi.nlm.nih.gov/articles/PMC9863827/
  20. https://pmc.ncbi.nlm.nih.gov/articles/PMC10131248/
  21. https://hekint.org/2023/06/13/archibald-edward-garrod-inborn-errors-of-metabolism/
  22. https://onlinelibrary.wiley.com/doi/10.1002/jimd.12767
  23. https://academic.oup.com/chromsci/article-abstract/9/3/129/283670
  24. https://www.britannica.com/biography/Hans-Krebs
  25. https://pubmed.ncbi.nlm.nih.gov/11062433/
  26. https://pubmed.ncbi.nlm.nih.gov/11868884/
  27. https://metabolomicssociety.org/
  28. https://pmc.ncbi.nlm.nih.gov/articles/PMC1899095/
  29. https://pubmed.ncbi.nlm.nih.gov/17202168/
  30. https://xcmsonline.scripps.edu/
  31. https://pmc.ncbi.nlm.nih.gov/articles/PMC12160488/
  32. https://www.hmdb.ca/
  33. https://www.nature.com/articles/eye201637
  34. https://www.nature.com/articles/s41467-023-37164-x
  35. https://www.sciencedirect.com/science/article/abs/pii/S0958166910001898
  36. https://www.sciencedirect.com/topics/neuroscience/metabolome
  37. https://pmc.ncbi.nlm.nih.gov/articles/PMC2989741/
  38. https://pubmed.ncbi.nlm.nih.gov/17702530/
  39. https://pmc.ncbi.nlm.nih.gov/articles/PMC5558977/
  40. https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/metabolite
  41. https://pmc.ncbi.nlm.nih.gov/articles/PMC10051737/
  42. https://www.ncbi.nlm.nih.gov/books/NBK9879/
  43. https://pubmed.ncbi.nlm.nih.gov/8968582/
  44. https://pmc.ncbi.nlm.nih.gov/articles/PMC9308113/
  45. https://www.sciencedirect.com/topics/medicine-and-dentistry/metabolomics
  46. https://www.sciencedirect.com/science/article/pii/S0958166915000592
  47. https://journals.asm.org/doi/10.1128/AEM.00469-11
  48. https://pmc.ncbi.nlm.nih.gov/articles/PMC8305469/
  49. https://pmc.ncbi.nlm.nih.gov/articles/PMC9019480/
  50. https://pmc.ncbi.nlm.nih.gov/articles/PMC10819689/
  51. https://pmc.ncbi.nlm.nih.gov/articles/PMC11618781/
  52. https://pmc.ncbi.nlm.nih.gov/articles/PMC4829120/
  53. https://pubmed.ncbi.nlm.nih.gov/22576654/
  54. https://www.sciencedirect.com/science/article/pii/S0165993622003661
  55. https://pmc.ncbi.nlm.nih.gov/articles/PMC12105183/
  56. https://www.sciencedirect.com/science/article/pii/S209592732300587X
  57. https://www.nature.com/articles/s44303-024-00025-3
  58. https://pubs.acs.org/doi/10.1021/acs.analchem.4c06477
  59. https://pmc.ncbi.nlm.nih.gov/articles/PMC8492264/
  60. https://pmc.ncbi.nlm.nih.gov/articles/PMC12177712/
  61. https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0370
  62. https://pubs.acs.org/doi/10.1021/acs.analchem.7b04758
  63. https://.ncbi.nlm.nih.gov/18694716/
  64. https://pubs.acs.org/doi/10.1021/jasms.5c00090
  65. https://www.sciencedirect.com/science/article/pii/S2352304223002246
  66. https://pmc.ncbi.nlm.nih.gov/articles/PMC8948616/
  67. https://pmc.ncbi.nlm.nih.gov/articles/PMC3901240/
  68. https://pmc.ncbi.nlm.nih.gov/articles/PMC9032224/
  69. https://pmc.ncbi.nlm.nih.gov/articles/PMC4465187/
  70. https://pmc.ncbi.nlm.nih.gov/articles/PMC10891053/
  71. https://pmc.ncbi.nlm.nih.gov/articles/PMC10296258/
  72. https://pmc.ncbi.nlm.nih.gov/articles/PMC10456436/
  73. https://academic.oup.com/bioinformatics/article/40/8/btae490/7734912
  74. https://pmc.ncbi.nlm.nih.gov/articles/PMC12581342/
  75. https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2017.00719/full
  76. https://pmc.ncbi.nlm.nih.gov/articles/PMC9259766/
  77. https://www.mdpi.com/2072-6694/12/1/241
  78. https://pmc.ncbi.nlm.nih.gov/articles/PMC9940269/
  79. https://www.researchgate.net/publication/41562634_Use_of_Pharmaco-Metabonomics_for_Early_Prediction_of_Acetaminophen-Induced_Hepatotoxicity_in_Humans
  80. https://pmc.ncbi.nlm.nih.gov/articles/PMC5489132/
  81. https://www.fda.gov/medical-devices/guidance-documents-medical-devices-and-radiation-emitting-products/newborn-screening-test-systems-amino-acids-free-carnitine-and-acylcarnitines-using-tandem-mass
  82. https://www.sciencedirect.com/science/article/abs/pii/S0048969720300619
  83. https://www.sciencedirect.com/science/article/abs/pii/S0048969723078026
  84. https://pmc.ncbi.nlm.nih.gov/articles/PMC8880617/
  85. https://pmc.ncbi.nlm.nih.gov/articles/PMC8431676/
  86. https://pubmed.ncbi.nlm.nih.gov/40625922/
  87. https://www.pnas.org/doi/10.1073/pnas.1319681110
  88. https://pmc.ncbi.nlm.nih.gov/articles/PMC3901257/
  89. https://pubs.acs.org/doi/abs/10.1021/acs.jafc.0c04095
  90. https://pmc.ncbi.nlm.nih.gov/articles/PMC10278091/
  91. https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1274405/full
  92. https://pmc.ncbi.nlm.nih.gov/articles/PMC9958811/
  93. https://pmc.ncbi.nlm.nih.gov/articles/PMC8728138/
  94. https://link.springer.com/article/10.1007/s11306-007-0070-6
  95. https://chemistry-europe.onlinelibrary.wiley.com/doi/full/10.1002/ansa.202400007
  96. https://pmc.ncbi.nlm.nih.gov/articles/PMC6824478/
  97. https://www.sciencedirect.com/science/article/pii/S0929664618306193
  98. https://www.nature.com/articles/s41467-021-26110-4
  99. https://doi.org/10.1016/j.trac.2024.117918
  100. https://www.sciencedirect.com/science/article/pii/S0165993624005478
  101. https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/metabolomics
  102. https://academic.oup.com/nar/article/52/W1/W398/7642060
  103. https://pmc.ncbi.nlm.nih.gov/articles/PMC8987531/
  104. https://www.nature.com/articles/s41467-025-63306-4
  105. https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.01152/full
  106. https://pubmed.ncbi.nlm.nih.gov/30886413/
  107. https://pubmed.ncbi.nlm.nih.gov/31953819/
  108. https://pubs.acs.org/doi/abs/10.1021/acs.analchem.4c01555
  109. https://www.sciencedirect.com/science/article/pii/S016599362400195X
  110. https://www.niehs.nih.gov/research/atniehs/facilities/nih-metabolomics-consortium
  111. https://pmc.ncbi.nlm.nih.gov/articles/PMC4552607/
  112. https://www.frontiersin.org/journals/molecular-neuroscience/articles/10.3389/fnmol.2022.883466/full
Add your contribution
Related Hubs
User Avatar
No comments yet.