The DOI matches its Crossref record; 182 of the 182 references it cites were found in a registry or an index.
Integration of GC–MS and LC–MS for untargeted metabolomics profiling
Abstract
Recently, metabolomics analyses have become increasingly common in the general scientific community as it is applied in several researches relating to diseases diagnosis. Identification and quantification of small molecules belonging to metabolism in biological systems have an important role in diagnosis of diseases. The combination of chromatography with mass spectrometry is used for the accurate and reproducible analysis of hundreds to thousands of metabolites in biological fluids or tissue samples. The number of metabolites that can be identified in biological fluids or tissue varies according to the gas (GC) or liquid (LC) chromatographic techniques used. The cover of these chromatographic techniques also differs from each other based on the metabolite group (polar, lipids, organic acid etc.). Consequently, some of the metabolites can only be analyzed using either GC or LC. However, more than one metabolite or metabolite group may be found altered in a particular disease. Thus, in order to find these alterations, metabolomics analyses that cover a wide range of metabolite groups are usually applied. In this regard, GC-MS and LC-MS techniques are mostly used together to identify completely all the altered metabolites during disease diagnosis. Using these combined techniques also allows identification of metabolite(s) with significantly altered phenotype. This review sheds light on metabolomics studies involving the simultaneous use of GC-MS and LC-MS. The review also discusses the coverage, sample preparation, data acquisition and data preprocessing for untargeted metabolomics studies. Moreover, the advantages and disadvantages of these methods were also evaluated. Finally, precautions and suggestions on how to perform metabolomics studies in an accurate, precise, complete and unbiased way were also outlined.
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Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., & Siuzdak, G. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,262
Systems biology: a brief overview
Kitano, H. (2002) · Science · doi:10.1126/science.1069492 · Cited by 4,265
Metabolomics—the link between genotypes and phenotypes
Fiehn, O. (2002) · Plant Molecular Biology · doi:10.1023/a:1013713905833 · Cited by 3,445
MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis
Tsugawa, H., Cajka, T., Kind, T., Ma, Y., Higgins, B., Ikeda, K., Kanazawa, M., VanderGheynst, J., Fiehn, O., & Arita, M. (2015) · Nature Methods · doi:10.1038/nmeth.3393 · Cited by 3,397
The Human Serum Metabolome (HUSERMET) Consortium, Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., Brown, M., Knowles, J. D., Halsall, A., et al. (2011) · Nature Protocols · doi:10.1038/nprot.2011.335 · Cited by 3,294
Metabolomics — the link between genotypes and phenotypes
Fiehn, O. (2002) · Functional Genomics · doi:10.1007/978-94-010-0448-0_11 · Cited by 3,167
Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis
Chong, J., Wishart, D. S., & Xia, J. (2019) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.86 · Cited by 2,166
Beckonert, O., Keun, H. C., Ebbels, T. M. D., Bundy, J., Holmes, E., Lindon, J. C., & Nicholson, J. K. (2007) · Nature Protocols · doi:10.1038/nprot.2007.376 · Cited by 2,109
HMDB: a knowledgebase for the human metabolome
Wishart, D. S., Knox, C., Guo, A. C., Eisner, R., Young, N., Gautam, B., Hau, D. D., Psychogios, N., Dong, E., Bouatra, S., et al. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn810 · Cited by 1,873
Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., Liu, P., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0073076 · Cited by 1,449
Metadata
- Authors
- Zeki, Ö. C., Eylem, C. C., Reçber, T., Kır, S., & Nemutlu, E.
- Venue
- Journal of Pharmaceutical and Biomedical Analysis
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 190
- Pages
- 113509
- ISSN
- 0731-7085 (ISSN-L)
- OpenAlex
- W3046356390
- Cited by
- 336
- References
- 182
- FWCI
- 10.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zeki, Ö. C., Eylem, C. C., Reçber, T., Kır, S., & Nemutlu, E. (2020). Integration of GC–MS and LC–MS for untargeted metabolomics profiling. Journal of Pharmaceutical and Biomedical Analysis. https://doi.org/10.1016/j.jpba.2020.113509
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