Promises and pitfalls of untargeted metabolomics
Open accessAbstract
Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi‐quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC‐MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre‐analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post‐analytical (data processing).
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Untargeted metabolomics methods have the advantage of detecting many thousands of metabolite features; however, most compounds detected via mass spectrometry and NMR are orphan compounds whose structure has not been identified.
“Untargeted metabolomics methods have the advantage of being able to detect many thousands of metabolite features; however, most compounds detected via mass spectrometry and NMR are orphan compounds, that is, their structure has not been identified122–124.”
From Measuring biological age using omics data (2022) · cites this paper as [123]
Targeted methods have better sensitivity but are limited by predefining a set of metabolites to look for, greatly reducing the number of features detected and precluding the discovery of new metabolites.
“Targeted methods have better sensitivity but are limited by predefining a set of metabolites to look for in the experiment, greatly reducing the number of features detected and precluding the discovery of new metabolites122–124.”
From Measuring biological age using omics data (2022) · cites this paper as [123]
Sensitivity is a challenge in untargeted methods, and many metabolites are detected in some but not other samples, which limits the utility of many analytes for modelling.
“Sensitivity is another challenge in untargeted methods, and many metabolites are detected in some but not other samples122–124, which limits the utility of many analytes for modelling.”
From Measuring biological age using omics data (2022) · cites this paper as [123]
In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood.
“In both targeted and untargeted methods, even for confidently identified compounds, the biological processes that generated them are often poorly understood122–125.”
From Measuring biological age using omics data (2022) · cites this paper as [123]
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- Authors
- Gertsman, I., & Barshop, B. A.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Barshop, Bruce A.
- Venue
- Journal of Inherited Metabolic Disease
- Publisher
- Wiley
- Published
- 2018-03-13
- Volume
- 41
- Issue
- 3
- Pages
- 355-366
- ISSN
- 0141-8955 (ISSN-L)
- 1573-2665
- PMC
- PMC5960440
- OpenAlex
- W2793222979
- Cited by
- 271
- References
- 96
- Citations by year
- 2026: 38 · 2025: 39 · 2024: 43 · 2023: 47 · 2022: 27 · 2021: 40 · 2020: 23 · 2019: 9 · 2018: 4
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Inherited Metabolic Disease (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R03HD069983)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- untargeted metabolomics, LC-MS, experimental design, sample collection and preparation, data processing, biomarker discovery, inborn errors of metabolism
- MeSH terms
- Calibration, Chromatography, Liquid, Humans, Research Design, Artifacts, Biomedical Research, Tandem Mass Spectrometry, Metabolomics, Metabolome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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