The DOI matches its Crossref record; 132 of the 133 references it cites were found in a registry or an index.
Analytical and statistical approaches to metabolomics research
Abstract
Metabolomics, the global profiling of metabolites in different living systems, has experienced a rekindling of interest partially due to the improved detection capabilities of the instrumental techniques currently being used in this area of biomedical research. The analytical methods of choice for the analysis of metabolites in search of disease biomarkers in biological specimens, and for the study of various low molecular weight metabolic pathways include NMR spectroscopy, GC/MS, CE/MS, and HPLC/MS. Global metabolite analysis and profiling of two different sets of data results in a plethora of data that is difficult to manage or interpret manually because of their subtle differences. Multivariate statistical methods and pattern-recognition programs were developed to handle the acquired data and to search for the discriminating features between data acquired from two sample sets, healthy and diseased. Metabolomics have been used in toxicology, plant physiology, and biomedical research. In this paper, we discuss various aspects of metabolomic research including sample collection, handling, storage, requirements for sample analysis, peak alignment, data interpretation using statistical approaches, metabolite identification, and finally recommendations for successful analysis.
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“Briefly, NMR is robust, nondestructive, and quantitative but has a relatively low sensitivity, which narrows its coverage of the food metabolome to predominant nutrients, sugars, and microbial metabolites present at millimolar to micromolar concentrations.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [113]
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Global metabolic profiling of animal and human tissues via UPLC-MS
Want, E. J., Masson, P., Michopoulos, F., Wilson, I. D., Theodoridis, G. A., Plumb, R. S., Shockcor, J. P., Loftus, N. J., Holmes, E. C., & Nicholson, J. K. (2012) · Nature Protocols · doi:10.1038/nprot.2012.135 · Cited by 1,647
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Metadata
- Authors
- Issaq, H. J., Van, Q. N., Waybright, T. J., Muschik, G. M., & Veenstra, T. D.
- Affiliations
- National Cancer Institute (US)
- Venue
- Journal of Separation Science
- Publisher
- Wiley
- Published
- 2009-06-30
- Volume
- 32
- Issue
- 13
- Pages
- 2183-2199
- ISSN
- 1615-9306 (ISSN-L)
- 1615-9314
- OpenAlex
- W2103773262
- Cited by
- 227
- References
- 133
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 8 · 2023: 7 · 2022: 6 · 2021: 11 · 2020: 4 · 2019: 17 · 2018: 6 · 2017: 7 · 2016: 11 · 2015: 15 · 2014: 27 · 2013: 22 · 2012: 18
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Separation Science (publishedVersion)
- PubMed (publishedVersion)
- DigitalCommons-Cedarville (Cedarville University) (submittedVersion)
- Funders
- National Cancer Institute, National Institutes of Health (HHSN261200800001E)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Keywords
- metabolomics, multivariate analysis, pattern recognition, disease biomarkers, metabolite profiling, sample collection and storage, peak alignment, metabolite identification, NMR spectroscopy, gas chromatography-mass spectrometry, HPLC-MS, capillary electrophoresis-mass spectrometry, toxicology, plant physiology
- MeSH terms
- Chromatography, High Pressure Liquid, Humans, Gas Chromatography-Mass Spectrometry, Software, Specimen Handling, Biomarkers, Electrophoresis, Capillary, Nuclear Magnetic Resonance, Biomolecular, Biomedical Research, Metabolomics
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