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The use of a combination of ultraperformance liquid chromatography at ~11 000 psi on sub 2-[micro]m particles combined with reversed-phase gradient chromatography at a temperature of 90[degrees]C is described as applied to the analysis of endogenous and drug metabolites in human and animal urine. By using elevated temperatures, back pressures can be reduced while maintaining high flow rates and chromatographic efficiency, with peaks 1-3 s wide at the base. Application to urine samples provided a peak capacity of ~700 for a 10-min analysis and greater than ~1000 in 1 h. Despite the narrow nature of the peaks, good quality mass spectra were also obtained, allowing the identification of typical drug and endogenous metabolites. These ultra-high-resolution chromatograms should be ideal for the analysis of complex samples in, for example, metabolite identification, impurity identification, and metabonomic/metabolomic studies. Applications in natural product analysis and proteomics can also be envisaged.

ABSTRACT Background The identification of schizophrenia biomarkers is a crucial step towards improving current diagnosis, developing new presymptomatic treatments, identifying high-risk individuals and disease subgroups, and assessing the efficacy of [...]

Concurrent with the explosion in the number of publications reporting biomarker discovery by profiling technologies, such as proteomics and pattern recognition, has been the increase in evidence highlighting the susceptibility of these approaches to analytical and experimental bias. The work presented here addresses these timely issues by delivering a detailed characterization of the effect of common sources of bias in clinical studies on serum and plasma profiles generated by a key technology in metabonomics, NMR spectroscopy. Specifically, differences in composition when blood samples were collected onto and in the absence of ice, over a series of serum--clot contact times, the stability of NMR-prepared samples over time and the effect on the metabolic profile of freeze--thawing were examined. While differences between individuals were far greater than variation from any other experimental factor, each of the conditions examined did cause slight alterations to the NMR profile that could produce a systematic bias. Variation due to clotting time caused changes in energy metabolites, which were delayed by ice with no other spectral effects. Room-temperature stability and hence NMR spectral repeatability were high (< 1% intrasample variation). Higher molecular weight species such as lipoproteins were more susceptible to the variations present in the examined factors. These observations have implications for profiling study design, and hence, our results form a new and valuable resource for those attempting clinical metabolic profiling, for regulatory agencies involved in the licensing of clinical tests and in the generation of international reporting standards for metabonomics.

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.

Considerable confusion appears to exist in the metabonomics literature as to the real need for, and the role of, preprocessing the acquired spectroscopic data. A number of studies have presented various data manipulation approaches, some suggesting an optimum method. In metabonomics, data are usually presented as a table where each row relates to a given sample or analytical experiment and each column corresponds to a single measurement in that experiment, typically individual spectral peak intensities or metabolite concentrations. Here we suggest definitions for and discuss the operations usually termed normalization (a table row operation) and scaling (a table column operation) and demonstrate their need in [sup.1]H NMR spectroscopic data sets derived from urine. The problems associated with 'binned' data (i.e., values integrated over discrete spectral regions) are also discussed, and the particular biological context problems of analytical data on urine are highlighted. It is shown that care must be exercised in calculation of correlation coefficients for data sets where normalization to a constant sum is used. Analogous considerations will be needed for other biofluids, other analytical approaches (e.g., HPLCMS), and indeed for other 'omics' techniques (i.e., transcriptomics or proteomics) and for integrated studies with 'fused' data sets. It is concluded that data preprocessing is context dependent and there can be no single method for general use.

Large-scale population phenotyping for molecular epidemiological studies is subject to all the usual criteria of analytical chemistry. As part of a major phenotyping investigation we have used high-resolution [sup.1]H NMR spectroscopy to characterize 24-h urine specimens obtained from population samples in Aito Town, Japan (n = 259), Chicago, IL (n = 315), and Guangxi, China (n = 278). We have investigated analytical reproducibility, urine specimen storage procedures, interinstrument variability, and split specimen detection. Our data show that the multivariate analytical reproducibility of the NMR screening platform was > 98% and that most classification errors were due to urine specimen handling inhomogeneity. Differences in metabolite profiles were then assessed for Alto Town, Chicago, and Guangxi population samples; novel combinations of biomarkers were detected that separated the population samples. These cross-population differences in urinary metabolites could be related to genetic, dietary, and gut microbial factors.

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.

This work presents the first application of high-resolution magic angle spinning (HR-MAS) [sup.1]H NMR spectroscopy to human liver biopsy samples, allowing a determination of their metabolic profiles before removal from donors, during cold perfusion, and after implantation into recipients. The assignment of peaks observed in the [sup.1]H HRMAS NMR spectra was aided by the use of two-dimensional J-resolved, TOCSY and [sup.1]H-[sup.13]C HMQC spectra. The spectra were dominated by resonances from triglycerides, phospholipids, and glycogen and from a variety of small molecules including glycerophosphocholine (GPC), glucose, lactate, creatine, acetate, amino acids, and nucleoside-related compounds such as uridine and adenosine. In agreement with histological data obtained on the same biopsies, two of the six livers were found to contain high amounts of triglycerides by NMR spectroscopy, which also indicated that these tissues contained a higher degree of unsaturated lipids and a lower proportion of phospholipids and low molecular weight compounds. Additionally, proton [T.sub.2] relaxation times indicated two populations of lipids, a higher mobility triglyceride fraction and a lower mobility phospholipid fraction, the proportions of which changed according to the degree of fat content. GPC was found to decrease from the pretransplant to the posttransplant biopsy of all livers except for one with a histologically confirmed high lipid content, and this might represent a biomarker of liver function posttransplantation. NMR signals produced by the liver preservation solution were clearly detected in the cold perfusion stage biopsies of all livers but remained in the posttransplant spectra of only the two livers with a high lipid content and were prominent mainly in the graft that later developed primary graft dysfunction. This study has shown biochemical differences between livers used for transplants that can be related to the degree and type of lipid composition. This technology might therefore provide a novel screening approach for donor organ quality and a means to assess function in the recipient after transplantation.

In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in [sup.1]H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.

[sup.1]H NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.

The exact nature of the interaction between small molecules and chromatographic solid phases has been the subject of much research, but detailed understanding of the molecular dynamics in such systems remains elusive. High-resolution [sup.1]H magic-angle-spinning (MAS) NMR spectroscopy has been applied to the investigation of C18-bonded silica material as used in chromatographic separation techniques together with an adsorbed model analyte, p-xylene. Two distinct p-xylene and water environments were identified within the C18-bonded silica through the measurement of [sup.1]H NMR chemical shifts, [T.sub.1] and [T.sub.2] relaxation times and diffusion coefficients, including their temperature dependence. The results have been analyzed in terms of two environments, p-xylene within the C18 chains, in slow exchange on the NMR time scale with p-xylene in a more mobile state adsorbed as a layer in close proximity to the C18 particles, but which is distinct from free liquid p-xylene. The techniques used here could have more general applications, including the study of drug molecules bound into phospholipid membranes in micelles or vesicles.

Although a wide range of risk factors for coronary heart disease have been identified from population studies, these measures, singly or in combination, are insufficiently powerful to provide a reliable, noninvasive diagnosis of the presence of coronary heart disease. Here we show that pattern-recognition techniques applied to proton nuclear magnetic resonance ([sup.1]H-NMR) spectra of human serum can correctly diagnose not only the presence, but also the severity, of coronary heart disease. Application of supervised partial least squares-discriminant analysis to orthogonal signal-corrected data sets allows [greater than] 90% of subjects with stenosis of all three major coronary vessels to be distinguished from subjects with angiographically normal coronary arteries, with a specificity of [greater than] 90%. Our studies show for the first time a technique capable of providing an accurate, noninvasive and rapid diagnosis of coronary heart disease that can be used clinically, either in population screening or to allow effective targeting of treatments such as statins., Author(s): Joanne T. Brindle [1]; Henrik Antti [1]; Elaine Holmes [1]; George Tranter [1]; Jeremy K. Nicholson [1]; Hugh W.L. Bethell [2]; Sarah Clarke [2]; Peter M. Schofield [2]; Elaine [...]

Cryogenic probe technology can significantly compensate for the inherently low sensitivity of natural abundance [sup.13]C NMR spectroscopy. This now permits its routine use in NMR spectroscopy of biofluids, such as urine or plasma, with acquisition times that enable a high throughput of samples. Metabonomic studies often generate numerous samples in order to characterize fully the time-dependent biochemical response to stimuli, but until now, they have been largely conducted using [sup.1]H NMR spectroscopy because of its high sensitivity and hence efficient data acquisition. Here, we demonstrate that information-rich [sup.13]C NMR spectra of rat urine can be obtained using appropriately short acquisition times suitable for biochemical samples when using a cryogenic probe. Furthermore, these data were amenable to automated pattern recognition analysis, which produced a profile of the metabolic response to the model hepatotoxin hydrazine that was consistent with earlier studies. Thus, a new source of detailed and complementary information is available to metabonomics using cryogenic probe [sup.13]C NMR spectroscopy.