The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
High-Throughput Data Analysis for Detecting and Identifying Differences between Samples in GC/MS-Based Metabolomic Analyses
Abstract
In metabolomics, the objective is to identify differences in metabolite profiles between samples. A widely used tool in metabolomics investigations is gas chromatography-mass spectrometry (GC/MS). More than 400 compounds can be detected in a single analysis, if overlapping GC/MS peaks are deconvoluted. However, the deconvolution process is time-consuming and difficult to automate, and additional processing is needed in order to compare samples. Therefore, there is a need to improve and automate the data processing strategy for data generated in GC/MS-based metabolomics; if not, the processing step will be a major bottleneck for high-throughput analyses. Here we describe a new semiautomated strategy using a hierarchical multivariate curve resolution approach that processes all samples simultaneously. The presented strategy generates (after appropriate treatment, e.g., multivariate analysis) tables of all the detected metabolites that differ in relative concentrations between samples. The processing of 70 samples took similar time to that of the GC/TOFMS analyses of the samples. The strategy has been validated using two different sets of samples: a complex mixture of standard compounds and Arabidopsis samples.
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Cites
Reverse engineering of biological complexity
Csete, M. E., & Doyle, J. C. (2002) · Science · doi:10.1126/science.1069981 · Cited by 1,215
Cited by
Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma
Chen, T., Xie, G., Wang, X., Fan, J., Qiu, Y., Zheng, X., Qi, X., Cao, Y., Su, M., Wang, X., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.004945 · Cited by 357
Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives
Koek, M. M., Jellema, R. H., van der Greef, J., Tas, A. C., & Hankemeier, T. (2011) · Metabolomics · doi:10.1007/s11306-010-0254-3 · Cited by 353
Cedernaes, J., Schönke, M., Westholm, J. O., Mi, J., Chibalin, A., Voisin, S., Osler, M., Vogel, H., Hörnaeus, K., Dickson, S. L., et al. (2018) · Science Advances · doi:10.1126/sciadv.aar8590 · Cited by 137
Li, M., Wang, X., Aa, J., Qin, W., Zha, W., Ge, Y., Liu, L., Zheng, T., Cao, B., Shi, J., et al. (2013) · American Journal of Physiology-Renal Physiology · doi:10.1152/ajprenal.00536.2012 · Cited by 103
Chantzichristos, D., Svensson, P.-A., Garner, T., Glad, C. A., Walker, B. R., Bergthorsdottir, R., Ragnarsson, O., Trimpou, P., Stimson, R. H., Borresen, S. W., et al. (2021) · eLife · doi:10.7554/elife.62236 · Cited by 36
Metadata
- Authors
- Jonsson, P., Johansson, A. I., Gullberg, J., Trygg, J., A, J., Grung, B., Marklund, S., Sjöström, M., Antti, H., & Moritz, T.
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2005-08-04
- Volume
- 77
- Issue
- 17
- Pages
- 5635-5642
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W2036283662
- Cited by
- 426
- References
- 1
- FWCI
- 8.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jonsson, P., Johansson, A. I., Gullberg, J., Trygg, J., A, J., Grung, B., Marklund, S., Sjöström, M., Antti, H., & Moritz, T. (2005). High-Throughput Data Analysis for Detecting and Identifying Differences between Samples in GC/MS-Based Metabolomic Analyses. Analytical Chemistry. https://doi.org/10.1021/ac050601e
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