SS and TG are supported by National Institutes of Health grant HL076238, and the Medical Research Service, Veterans Administration. Additional support for JWN and TLP was derived from United States Department of Agriculture Agricultural Research Service Project# 5306-51530-019-00D. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This research was supported by the Canadian Institutes for Health Research (CIHR), Alberta Advanced Education and Technology (AAET), Genome Alberta, Genome BC, the Alberta Ingenuity Fund (AIF) and the University of Alberta. Received: SeptemAccepted: JanuPublished: February 16, 2011Ĭopyright: © 2011 Psychogios et al. PLoS ONE 6(2):Įditor: Darren Flower, Aston University, United Kingdom Ĭitation: Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al.

Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood.

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