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 1H 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.

The use of Ultra Performance Liquid Chromatography (UPLC), with a rapid 1.5 minute reversed-phase gradient separation on a 1.7 microm reversed-phase packing material to provide rapid "high throughput" support for metabonomic screening is demonstrated. The peak capacity and the number of marker ions detected using these fast UPLC separations and oa-TOF MS was found to be similar to that generated by conventional HPLC-MS methods with a 10 minute separation. The UPLC-MS methodology was applied to the analysis of urine samples from rodents, including normal and Zucker obese rats and three strains of mice (of both sexes), and was found to provide rapid discrimination between age, strain, gender and diurnal variation.

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.

Plasma obtained from 20 week old normal Wistar-derived and Zucker (fa/fa) rats was analysed using a number of different analytical methodologies to obtain global metabolite profiles as part of metabonomic investigations of animal models of diabetes. Samples were analysed without sample pre-treatment using 1H NMR spectroscopy, after acetonitrile solvent protein precipitation by ultra-performance liquid chromatography-MS (UPLC-MS) and after acetonitrile protein precipitation and derivatisation for capillary gas chromatography-MS (GC-MS). Subsequent data analysis using principal components analysis revealed that all three analytical platforms readily detected differences between the plasma metabolite profiles of the two strains of rat. There was only limited overlap between the metabolites detected by the different methodologies and the combination of all three methods of metabolite profiling therefore provided a much more comprehensive profile than would have been provided by their use individually.