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The following study investigates the preparation of human blood plasma for metabolomic profiling analysis by ultrahigh performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC/TOFMS) in a novel two-step design study. Four different organic solvents (acetonitrile, acetone, methanol, and ethanol) were used to assess human blood plasma preparation via protein precipitation. The optimal conditions for sample preparation were investigated, with consideration to the number of extracted markers, data quality/reproducibility, and column lifetime prolongation. Isotopically labeled internal standards were used to monitor data quality/reproducibility. Gel electrophoresis was also used to measure the protein content in the supernatant of the 'first design step' allowing assessment of the amount of protein that would be injected/accumulate onto the column after many injections that would be apparent in a global metabolic profiling study. The second design step followed on from the results obtained in step one, with four of the best conditions selected and further investigated, looking at the effects of vortex time and temperature on precipitation/extraction. Two choices of solvent compositions were found to be 'optimal' for preparation of plasma for global metabolic profiling analysis; these were 'methanol/ethanol' (1:1, v/v) and 'methanol/acetonitrile/acetone' (1: 1:1, v/v/v) added to plasma (4:1 ratio, 400 [micro]L total volume).

A new analytical strategy for biomarker recovery from directly coupled ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC Tof MS) data on biofluids is presented and exemplified using a study on hydrazine-induced liver toxicity. A key step in the strategy involves a novel procedure for reducing the spectroscopic search space by differential analysis of cohorts of normal and pathological samples using an orthogonal projection to latent structures discriminant analysis (O-PLS-DA). This efficiently sorts principal discriminators of toxicity from the background of thousands of metabolic features commonly observed in data sets generated by UPLC--MS analysis of biological fluids and is thus a powerful tool for biomarker discovery.

Statistical heterospectroscopy (SHY) is a new statistical paradigm for the coanalysis of multispectroscopic data sets acquired on multiple samples. This method operates through the analysis of the intrinsic covariance between signal intensities in the same and related molecules measured by different techniques across cohorts of samples. The potential of SHY is illustrated using both 600-MHz [sup.1]H NMR and UPLC-TOFMS data obtained from control rat urine samples (n = 54) and from a corresponding hydrazine-treated group (n = 58). We show that direct cross-correlation of spectral parameters, viz. chemical shifts from NMR and m/z data from MS, is readily achievable for a variety of metabolites, which leads to improved efficiency of molecular biomarker identification. In addition to structure, higher level biological information can be obtained on metabolic pathway activity and connectivities by examination of different levels of the NMR to MS correlation and anticorrelation matrixes. The SHY approach is of general applicability to complex mixture analysis, if two or more independent spectroscopic data sets are available for any sample cohort. Biological applications of SHY as demonstrated here show promise as a new systems biology tool for biomarker recovery.