Urine has long been a “favored” biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.

Reproducible quantification of metabolites in tissue samples is of high importance for characterization of animal models and identification of metabolic changes that occur in different tissue types in specific diseases. However, the extraction of metabolites from tissue is often the most labor-intensive and error-prone step in metabolomics studies. Here, we report the development of a standardized high-throughput method for rapid and reproducible extraction of metabolites from multiple tissue samples from different organs of several species. The method involves a bead-based homogenizer in combination with a simple extraction protocol and is compatible with state-ofthe-art metabolomics kit technology for quantitative and targeted flow injection tandem mass spectrometry. We analyzed different extraction solvents for both reproducibility as well as suppression effects for a range of different animal tissue types including liver, kidney, muscle, brain, and fat tissue from mouse and bovine. In this study, we show that for most metabolites a simple methanolic extraction is best suited for reliable results. An additional extraction step with phosphate buffer can be used to improve the extraction yields for a few more polar metabolites. We provide a verified tissue extraction setup to be used with different indications. Our results demonstrate that this high-throughput procedure provides a basis for metabolomic assays with a wide spectrum of metabolites. The developed method can be used for tissue extraction setup for different indications like studies of metabolic syndrome, obesity, diabetes or cardiovascular disorders and nutrient transformation in livestock.

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The molecular analysis of serum is an important field for the definition of potential diagnostic markers or disease-related protein alterations. Novel proteomic technologies such as the mass spectrometric-based surface-enhanced laser desorption/ionization (SELDI) ProteinChip s technique facilitate a rapid and reproducible analysis of such protein mixtures and affords the researcher a new dimension in the search for biomarkers of disease. Here, we have applied this technology to the study of a cohort of serum samples from wellcharacterized renal cell carcinoma patients for the identification of such proteins by comparison to healthy controls. We detected and characterized haptoglobin 1 a and serum amyloid a-1 (SAA-1) as disease related, in addition to an as-yet-unidentified marker of 10.84 kDa. Of particular note is the detection of multiple variants of SAA-1 in multiplex that have not been described in the sera of cancer patients. SAA-1 is detected as full-length protein, des-Arginine and des-Arginine/des-Serine variants at the N terminus by SELDI. In addition, we could also detect a low-abundant variant minus the first five N-terminal amino acids. Such variants may impact the function of the protein. We conclude the technique to be a reproducible, fast and simple mode for the discovery and analysis of marker proteins of disease in serum.