The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges
Open accessAbstract
The field of metabolomics has witnessed an exponential growth in the last decade driven by important applications spanning a wide range of areas in the basic and life sciences and beyond. Mass spectrometry in combination with chromatography and nuclear magnetic resonance are the two major analytical avenues for the analysis of metabolic species in complex biological mixtures. Owing to its inherent significantly higher sensitivity and fast data acquisition, MS plays an increasingly dominant role in the metabolomics field. Propelled by the need to develop simple methods to diagnose and manage the numerous and widespread human diseases, mass spectrometry has witnessed tremendous growth with advances in instrumentation, experimental methods, software, and databases. In response, the metabolomics field has moved far beyond qualitative methods and simple pattern recognition approaches to a range of global and targeted quantitative approaches that are now routinely used and provide reliable data, which instill greater confidence in the derived inferences. Powerful isotope labeling and tracing methods have become very popular. The newly emerging ambient ionization techniques such as desorption ionization and rapid evaporative ionization have allowed direct MS analysis in real time, as well as new MS imaging approaches. While the MS-based metabolomics has provided insights into metabolic pathways and fluxes, and metabolite biomarkers associated with numerous diseases, the increasing realization of the extremely high complexity of biological mixtures underscores numerous challenges including unknown metabolite identification, biomarker validation, and interlaboratory reproducibility that need to be dealt with for realization of the full potential of MS-based metabolomics. This chapter provides a glimpse at the current status of the mass spectrometry-based metabolomics field highlighting the opportunities and challenges.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mass Spectrometry Sampling Under Ambient Conditions with Desorption Electrospray Ionization
Takáts, Z., Wiseman, J. M., Gologan, B., & Cooks, R. G. (2004) · Science · doi:10.1126/science.1104404 · Cited by 3,324
Partial least squares for discrimination
Barker, M., & Rayens, W. (2003) · Journal of Chemometrics · doi:10.1002/cem.785 · Cited by 2,841
Versatile New Ion Source for the Analysis of Materials in Open Air under Ambient Conditions
Cody, R. B., Laramée, J. A., & Durst, H. D. (2005) · Analytical Chemistry · doi:10.1021/ac050162j · Cited by 2,483
Metabolite profiling for plant functional genomics
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000) · Nature Biotechnology · doi:10.1038/81137 · Cited by 2,127
Nicholson, J. K., & Lindon, J. C. (2008) · Nature · doi:10.1038/4551054a · Cited by 1,886
Intraoperative Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry
Balog, J., Sasi-Szabó, L., Kinross, J., Lewis, M. R., Muirhead, L. J., Veselkov, K., Mirnezami, R., Dezső, B., Damjanovich, L., Darzi, A., et al. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005623 · Cited by 608
Bajad, S. U., Lu, W., Kimball, E. H., Yuan, J., Peterson, C., & Rabinowitz, J. D. (2006) · Journal of Chromatography A · doi:10.1016/j.chroma.2006.05.019 · Cited by 592
Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B. S., van Ommen, B., Pujos-Guillot, E., Verheij, E., Wishart, D., & Wopereis, S. (2009) · Metabolomics · doi:10.1007/s11306-009-0168-0 · Cited by 565
13C-based metabolic flux analysis
Zamboni, N., Fendt, S.-M., Rühl, M., & Sauer, U. (2009) · Nature Protocols · doi:10.1038/nprot.2009.58 · Cited by 544
Chen, H., Venter, A., & Cooks, R. G. (2006) · Chemical Communications · doi:10.1039/b602614a · Cited by 514
Metadata
- Authors
- Gowda, G. A. N., & Djukovic, D.
- Venue
- Methods in Molecular Biology
- Publisher
- Springer New York
- Published
- 2014
- Volume
- 1198
- Pages
- 3-12
- ISSN
- 1064-3745 (ISSN-L)
- 1940-6029
- OpenAlex
- W2399662018
- Cited by
- 334
- References
- 43
- FWCI
- 12.68
- Type
- book chapter
- Language
- en
- Open access
- green
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges. https://app.cochat.ai/papers/10.1007/978-1-4939-1258-2_1
- The paper
- Gowda, G. A. N., & Djukovic, D. (2014). Overview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges. Methods in Molecular Biology. https://doi.org/10.1007/978-1-4939-1258-2_1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.