Journal of Chromatography B, 871 (2008) 191–201 Contents lists available at ScienceDirect Journal of Chromatography B journal homepage: www.elsevier.com/locate/chromb Standardizing GC–MS metabolomics  Harin Kanani a,1 , Panagiotis K. Chrysanthopoulos b,c , Maria I. Klapa a,b,∗ a Metabolic Engineering and Systems Biology Laboratory, Department of Chemical and Biomolecular Engineering, University of Maryland, MD 20742, USA b Metabolic Engineering and Systems Biology Laboratory, Institute of Chemical Engineering and High-Temperature Chemical Processes, Foundation for Research and Technology-Hellas, Patras GR-265 04, Greece c Division of Genetics and Cell and Developmental Biology, Department of Biology, University of Patras, Patras GR-265 00, Greece article info Article history: Received 18 February 2008 Accepted 30 April 2008 Available online 21 May 2008 Keywords: Quantitative systems biology High-throughput “omic” techniques Data correction and normalization Data validation Derivatization biases TMS-derivatives Methoximation Metabolite extraction abstract Metabolomics being the most recently introduced “omic” analytical platform is currently at its develop- ment phase. For the metabolomics to be broadly deployed to biological and clinical research and practice, issues regarding data validation and reproducibility need to be resolved. Gas chromatography–mass spec- trometry (GC–MS) will remain integral part of the metabolomics laboratory. In this paper, the sources of biases in GC–MS metabolomics are discussed and experimental evidence for their occurrence and impact on the final results is provided. When available, methods to correct or account for these biases are pre- sented towards the standardization of a systematic methodology for quantitative GC–MS metabolomics. © 2008 Elsevier B.V. All rights reserved. 1. Introduction The post-genomic era is characterized by two major shifts in the way problems in life sciences are now approached. The first refers to what is known as the “high-throughput” revolution triggered by the development of the “omic” technical platforms that allow for the simultaneous measurement of hundreds to thousands of molecular quantities. Thus, rather than examining a small number of genes and/or reactions at any one time, the focus shifts to the analysis of gene expression and protein activity in the context of networks and systems of interacting genes and gene products [1]. The second major shift in biological research concerns the impor- tance that has been attributed to quantitative biology. It is indeed essential to know the structure of a particular gene-, protein- or metabolic-network. However, this alone is insufficient to describe  This paper is part of a special volume entitled “Hyphenated Techniques for Global Metabolite Profiling”, guest edited by Georgios Theodoridis and Ian D. Wilson. ∗ Corresponding author at: Metabolic Engineering and Systems Biology Labora- tory, Institute of Chemical Engineering and High-Temperature Chemical Processes, Foundation for Research and Technology-Hellas, Patras GR-265 04, Greece. Tel.: +30 2610 965249; fax: +30 2610 965223. E-mail address: mklapa@iceht.forth.gr (M.I. Klapa). 1 Present address: Pioneer Hi-Bred International, Inc., IA 50131, USA. how the in vivo state of the cellular function(s) that is(are) described from this network changes depending on the physiological con- ditions and/or the biological system. Quantitative analysis of the molecular quantities that define the activity of this network, e.g. gene expression, protein concentration, protein activity, metabo- lite concentration or metabolic flux, is required. Based primarily on these two major shifts, the post-genomic was granted as the era of the quantitative systems biology revolution. To succeed in the challenge of quantitative systems biology, major issues concerning the quantification capabilities of the high-throughput molecular analysis techniques for each level of cellular function need to be resolved. They range from limitations in the available experimen- tal protocols to lack of data analysis techniques for upgrading the information content of the acquired measurements. Metabolomics is the most recently introduced [2,3], but cur- rently one of the fastest growing, high-throughput molecular analysis platforms. It refers to the simultaneous quantification of the (relative) concentration of the free small metabolite pools of a biological system [4]. It provides thus a comprehensive metabolic fingerprint, correspondent at the metabolic level of the high- throughput transcriptional and proteomic profiles [2]. Considering the role of metabolism in the context of the overall cellular function, it is easily understandable why quantifying a complete and accurate metabolomic profile is among the major goals of quantitative systems biology and metabolic pathway engineering. 1570-0232/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jchromb.2008.04.049