This review article compares and contrasts various types of ion mobility-mass spectrometers available today and describes their advantages for application to a wide range of analytes. Ion mobility spectrometry (IMS), when coupled with mass spectrometry, offers value-added data not possible from mass spectra alone. Separation of isomers, isobars, and conformers; reduction of chemical noise; and measurement of ion size are possible with the addition of ion mobility cells to mass spectrometers. In addition, structurally similar ions and ions of the same charge state can be separated into families of ions which appear along a unique mass-mobility correlation line. This review describes the four methods of ion mobility separation currently used with mass spectrometry. They are (1) drift-time ion mobility spectrometry (DTIMS), (2) aspiration ion mobility spectrometry (AIMS), (3) differential-mobility spectrometry (DMS) which is also called field-asymmetric waveform ion mobility spectrometry (FAIMS) and (4) traveling-wave ion mobility spectrometry (TWIMS). DTIMS provides the highest IMS resolving power and is the only IMS method which can directly measure collision cross-sections. AIMS is a low resolution mobility separation method but can monitor ions in a continuous manner. DMS and FAIMS offer continuous-ion monitoring capability as well as orthogonal ion mobility separation in which high-separation selectivity can be achieved. TWIMS is a novel method of IMS with a low resolving power but has good sensitivity and is well intergrated into a commercial mass spectrometer. One hundred and sixty references on ion mobility-mass spectrometry (IMMS) are provided. Copyright © 2008 John Wiley & Sons, Ltd.

Metabolic phenotypes are the products of interactions among a variety of factors—dietary, other lifestyle/environmental, gut microbial and genetic1, 2, 3. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide4). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study5, involving 17 population samples aged 40–59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10-16), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure6. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities2, 7, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.

1HNMR metabolomics to diagnose mulatifactorial disease diabetic kidney disease (DKD)

Escherichia coli is among the simplest and best-understood free-living organisms. It has served as a valuable model for numerous biological processes, including cellular metabolism. Just as E. coli stood at the front of the genomic revolution, it is playing a leading role in the development of cellular metabolomics: the study of the complete metabolic contents of cells, including their dynamic concentration changes and fluxes. This review briefly describes the essentials of cellular metabolomics and its fundamental differentiation from biomarker metabolomics and lipidomics. Key technologies for metabolite quantitation from E. coli are described, with a focus on those involving mass spectrometry. In particular emphasis is given to the cell handling and sample preparation steps required for collecting data of high biological reliability, such as fast metabolome quenching. Future challenges, both in terms of data collection and application of the data to obtain a comprehensive understanding of metabolic dynamics, are discussed.

A key unmet need in metabolomics is the ability to efficiently quantify a large number of known cellular metabolites. Here we present a liquid chromatography (LC)–electrospray ionization tandem mass spectrometry (ESI-MS/MS) method for reliable measurement of 141 metabolites, including components of central carbon, amino acid, and nucleotide metabolism. The selectedLCapproach, hydrophilic interaction chromatography with an amino column, effectively separates highly water soluble metabolites that fail to retain using standard reversed-phase chromatography. MS/MS detection is achieved by scanning through numerous selected reaction monitoring events on a triple quadrupole instrument. When applied to extracts of Escherichia coli grown in [12C]- versus [13C]glucose, the method reveals appropriate 12C- and 13C-peaks for 79 different metabolites. © 2006 Elsevier B.V. All rights reserved.

The past few years in the medical and biological sciences have been characterized by the advent of systems biology. However, despite the well-known connectivity between the molecules described by transcriptomic, proteomic and metabolomic approaches, few studies have tried to correlate parameters across the various levels of systemic description. When comparing the discriminatory power of metabolic and RNA profiling to distinguish between different potato tuber systems, using the techniques described here suggests that metabolic profiling has a higher resolution than expression profiling. When applying pairwise transcript–metabolite correlation analyses, 571 of the 26,616 possible pairs showed significant correlation, most of which was novel and included several strong correlations to nutritionally important metabolites. We believe this approach to be of high potential value in the identification of candidate genes for modifying the metabolite content of biological systems.