Metabolomics robustness

D. B. Kell, Metabolomics and machine learning Explanatory analysis of complex metabolome data using genetic programming to produce simple, robust rules. Mol. Biol. Rep. 29, 231 241 (2002). 

Zelena E, Dunn WB, Broadhurst D, Francis-Mclntyre S, Carroll KM, Begley P, O Hagan S, Knowles JD, Halsall A, Consortium H, Wilson ID, Kell DB (2009) Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem 81 1357-1364... 

In what many consider to be a landmark publication on metabolomics, Fiehn et al. (2000) state it is crucial to perform unbiased (metabolite) analyses in order to define precisely the biochemical function of plant metabolism. The authors argue that for metabolomics/metabolite profiling to become a robust and sensitive method suited to automation, a mature technology such as gas chromatography-mass spectrometry (GC-MS) is required as an analytical technique. The authors go on to describe a simple sample preparation and analysis regime that allowed for the detection and quantification of more than 300 compounds from a single-leaf sample extract. 

Data analysis and sensibly applied statistical tools are of crucial importance for metabolomics. Good experimental design is of course a fundamental first requirement. There have been a number of books and research papers written recently discussing statistics use and models for data analysis of metabolomics.100-104 Statistical and experimental robustness have been the focus of metabolomics and demonstrated in a study of NMR protocols and multivariate statistical batch processing, which were examined for consistency over six different centers. The data were shown to be sufficiently robust to generate comparable results across each center.105... 

Quantitative metabolomics, on the other hand, can be described as a targeted approach focused on the analysis of specific metabolite species. In this method, multivariate statistical analysis follows metabolite identification and quantitation. Because of the reliable peak identification and measurement of metabolite integrals, quantitative metabolomics promises greater insights into the dynamics and fluxes of metabolites, as well as robust statistical models for distinguishing classes with better classification accuracy. A major requirement for quantitative metabolomics is good-quality spectral analysis to provide reliable peak assignments and metabolite identification. 

Metabolomics is now an integral part of most major systems biology grants we can thus expect more coordinated large-scale efforts and projects of high-quality research where metabolite profiling data will be fused with that from other Omics platforms. Most importantly, standardization and establishment of robust and reliable cross-platform metabolomics databases are absolutely necessary. Such initiatives (57) are needed to promote the topic and finally assess its utility in enhancing the quality of human life. 

Zelena E, et al. Development of a robust and repeatable UPLC—MS method for the long-term metabolomic study of human serum. Anal Chemi 2009 81 1357-1364. 

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