There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the reinterrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at

Environmental metabolomics is the application of metabolomics to characterise the interactions of organisms with their environment. This approach has many advantages for studying organism-environment interactions and for assessing organism function and health at the molecular level. As such, metabolomics is finding an increasing number of applications in the environmental sciences, ranging from understanding organismal responses to abiotic pressures, to investigating the responses of organisms to other biota. These interactions can be studied from individuals to populations, which can be related to the traditional fields of ecophysiology and ecology, and from instantaneous effects to those over evolutionary time scales, the latter enabling studies of genetic adaptation. This review provides a comprehensive and current overview of environmental metabolomics research. We begin with an overview of metabolomic studies into the effects of abiotic pressures on organisms. In the field of ecophysiology, studies on the metabolic responses to temperature, water, food availability, light and circadian rhythms, atmospheric gases and season are reviewed. A section on ecotoxicogenomics discusses research in aquatic and terrestrial ecotoxicology, assessing organismal responses to anthropogenic pollutants in both the laboratory and field. We then discuss environmental metabolomic studies of diseases and biotic-biotic interactions, in particular herbivory. Finally, we critically evaluate the contribution that metabolomics has made to the environmental sciences, and highlight and discuss recommendations to advance our understanding of the environment, ecology and evolution using a metabolomics approach.

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It is important to assess the chronic effects of chemical, physical, and biological stressors on organisms in the environment. Appropriate methods must enable rapid, inexpensive, and multibiomarker analyses of organism health. Here we investigate withering syndrome in red abalone (Haliotis rufescens), an important wild and farmed shellfish species along the Pacific coast, using a metabolomic approach that combines the metabolic profiling capabilities of nuclear magnetic resonance spectroscopy (NMR) with pattern recognition methods. Foot muscle, digestive gland, and hemolymph samples were collected from healthy, stunted, and diseased abalone, and the extracts were analyzed by NMR. Following spectral preprocessing, principal components analyses of the metabolite profiles were conducted. Our results confirm that NMR-based metabolomics can successfully distinguish the biochemical profiles of the three groups of animals, in every type of tissue or biofluid studied. Furthermore, this discovery-based approach successfully identified novel metabolic biomarker profiles associated with withering syndrome. The application of these methods for investigating other environmental stressors is discussed, as are the advantages of NMR-based metabolomics for biomonitoring, particularly in conjunction with gene and protein expression profiling.

Metabolomic analysis of tissue samples can be applied across multiple fields including medicine, toxicology, and environmental sciences. A thorough evaluation of several metabolite extraction procedures from tissues is therefore warranted. This has been achieved at two research laboratories using muscle and liver tissues from fish. Multiple replicates of homogenous tissues were extracted using the following solvent systems of varying polarities: perchloric acid, acetonitrile/water, methanol/water, and methanol/chloroform/water. Extraction of metabolites from ground wet tissue, ground dry tissue, and homogenized wet tissue was also compared. The hydrophilic metabolites were analyzed using 1-dimensional (1D) 1 H nuclear magnetic resonance (NMR) spectroscopy and projections of 2-dimensional J-resolved (p-JRES) NMR, and the spectra evaluated using principal components analysis. Yield, reproducibility, ease, and speed were the criteria for assessing the quality of an extraction protocol for metabolomics. Both laboratories observed that the yields of low molecular weight metabolites were similar among the solvent extractions; however, acetonitrile-based extractions provided poorer fractionation and extracted lipids and macromolecules into the polar solvent. Extraction using perchloric acid produced the greatest variation between replicates due to peak shifts in the spectra, while acetonitrile-based extraction produced highest reproducibility. Spectra from extraction of ground wet tissues generated more macromolecules and lower reproducibility compared with other tissue disruption methods. The p-JRES NMR approach reduced peak congestion and yielded flatter baselines, and subsequently separated the metabolic fingerprints of different samples more clearly than by 1D NMR. Overall, single organic solvent extractions are quick and easy and produce reasonable results. However, considering both yield and reproducibility of the hydrophilic metabolites as well as recovery of the hydrophobic metabolites, we conclude that the methanol/chloroform/water extraction is the preferred method.

The application of reporting standards in metabolomics allow data from different laboratories to be shared, integrated and interpreted. Although minimum reporting standards related to metabolite identification were published in 2007, it is clear that significant efforts are required to ensure their continuous update and appropriate use by the metabolomics community. These include their use in metabolomics data submission (e.g., MetaboLights) and as a requirement for publication in peer-reviewed journals (e.g., Metabolomics). The Data Standards and Metabolite Identification Task Groups of the international Metabolomics Society are actively working to develop and promote these standards and educate the community on their use.

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS) offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse spacecharge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection of multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 × 10 5 charges. SIMstitching and wide-scan range (WSR; Thermo Electron) were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3-and 4.3-fold, and increased the peak count by 5.3-and 1.8-fold, versus WSR (AGC targets of 1 × 10 5 and 5 × 10 5 , respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high-throughput metabolomics.Metabolomics involves the measurement of multiple small molecules within a biological sample to generate a unique metabolic profile. These profiles can be compared directly between different biological phenotypes, and as metabolites represent the end products of complex cellular control systems, such analyses can give insight into upstream gene and protein activity. 1 This approach has been applied to many fields, notably toxicology, drug design, disease diagnosis, and quality control of food substances. [2][3][4][5][6] Reproducibility of the measurement system is critical, with nuclear magnetic resonance (NMR)-based methods proving very robust. 6,7 However, due to its superior sensitivity over NMR, mass spectrometry (MS) represents an attractive detection method for metabolomics. 8,9 Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is a particularly powerful tool for complex mixture analysis due to its ultrahigh mass resolution and mass accuracy 10,11 and has been applied successfully to a small number of metabolomics studies. [12][13][14][15][16] In principle, this analysis method enables the empirical formulas of many low molecular weight metabolites to be unambiguously identified based upon