Multiorigination of Chromatographic Peaks in Derivatized GC/MS Metabolomics: A Confounder That Influences Metabolic Pathway Interpretation

Identification of Metabolites from Catharanthus roseus Leaves and Stem Extract, and In Vitro and In Silico Antibacterial Activity against Food Pathogens

The plant produced powerful secondary metabolites and showed strong antibacterial activities against food-spoiling bacterial pathogens. The present study aimed to evaluate antibacterial activities and to identify metabolites from the leaves and stems of Catharanthus roseus using NMR spectroscopy. The major metabolites likely to be observed in aqueous extraction were 2,3-butanediol, quinic acids, vindoline, chlorogenic acids, vindolinine, secologanin, and quercetin in the leaf and stem of the Catharanthus roseus. The aqueous extracts from the leaves and stems of this plant have been observed to be most effective against food spoilage bacterial strains, followed by methanol and hexane. However, leaf extract was observed to be most significant in terms of the content and potency of metabolites. The minimum inhibitory concentration (20 µg/mL) and bactericidal concentrations (35 g/mL) of leaf extract were observed to be significant as compared to the ampicillin. Molecular docking showed that chlorogenic acid and vindolinine strongly interacted with the bacterial penicillin-binding protein. The docking energies of chlorogenic acid and vindolinine also indicated that these could be used as food preservatives. Therefore, the observed metabolite could be utilized as a potent antibacterial compound for food preservation or to treat their illness, and further research is needed to perform.

Monomethyl branched-chain fatty acids: Health effects and biological mechanisms

Branched-chain fatty acids (BCFA) are a group of lipids that are widely present in various organisms; they take part in numerous biochemical processes and affect multiple signaling pathways. However, BCFA are not well explored in terms of their effects on human health. Recently, they have been gaining interest, especially in relation to various human diseases. This review describes the occurrence of BCFA, their dietary sources, their potential health effects, and the current state of knowledge concerning their mechanism(s) of action. Many studies have been conducted so far in cellular and animal models, which reveal potent anti-cancer, lipid lowering, anti-inflammatory and neuroprotective actions. Research in humans is scarce. Therefore, further studies on animals and humans should be performed to confirm and expand these findings, and improve our understanding of the potential relevance of BCFA to human health and disease.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation

Despite inherent complementarity, nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are routinely separately employed to characterize metabolomics samples. More troubling is the erroneous view that metabolomics is better served by exclusively utilizing MS. Instead, we demonstrate the importance of combining NMR and MS for metabolomics by using small chemical compound treatments of Chlamydomonas reinhardtii as an illustrative example. A total of 102 metabolites were detected (82 by gas chromatography-MS, 20 by NMR, and 22 by both techniques). Out of these, 47 metabolites of interest were identified: 14 metabolites were uniquely identified by NMR, and 16 metabolites were uniquely identified by GC-MS. A total of 17 metabolites were identified by both NMR and GC-MS. In general, metabolites identified by both techniques exhibited similar changes upon compound treatment. In effect, NMR identified key metabolites that were missed by MS and enhanced the overall coverage of the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways that informed on pathway activity in central carbon metabolism, leading to fatty-acid and complex-lipid synthesis. Our study emphasizes a prime advantage of combining multiple analytical techniques: the improved detection and annotation of metabolites.

A chemical derivatization based UHPLC-LTQ-Orbitrap mass spectrometry method for accurate quantification of short-chain fatty acids in bronchoalveolar lavage fluid of asthma mice

Recent studies have demonstrated the important role of short-chain fatty acids (SCFAs) in the maintenance of homeostasis of respiratory immunity. However, there is still no report focus on the determination of SCFAs level in bronchoalveolar lavage fluid (BALF), the most common sample used for screening biomarkers of the pulmonary diseases. Herein, an ultra-high-performance liquid chromatography with LTQ-Orbitrap mass spectrometer (UHPLC-LTQ-Orbitrap) oriented 3-nitrophenylhydrazine (3-NPH)-based derivatization method was developed for the quantification of SCFAs in BALF. To achieve accurate quantitation, d4-acetate was used as internal standard to compensate for the matrix effects. Method validation showed a good linearity (R² > 0.9992) with wide concentration range, and the intra-day and inter-day precision for determination of eight SCFAs in BALF samples was ≤ 14.79%. The quantitation accuracy, assessed by relative recoveries, ranged from 90% to 110% for target SCFAs at three concentration levels. Matrix effects ranged from 85% to 115%, and the lower limits of quantification of these targeted SCFAs were varied from 3 to 24 nmol/L. The SCFAs-targeted method was then applied to determine the changed levels in BALF samples from OVA-induced asthma mice and normal mice. In addition, the universality of our developed method was also demonstrated by determining the SCFAs concentrations in feces, serum and lung tissue samples from asthma and normal mice. These results indicate that 3-NPH derivatization based UHPLC-LTQ-Orbitrap provides accurate view of global SCFAs alternation in different samples, giving a support to deduce the origin of SCFAs in lung. The present study is of great importance for understanding the role of SCFAs in modulation of host metabolism and immunity.

Lipidomics to investigate the pharmacologic mechanisms of ginkgo folium in the hyperuricemic rat model

Hyperuricemia caused by purine metabolic abnormalities is reported to have close correlation with lipid metabolic disorders. Ginkgo folium, a frequently-used lipid-lowering medicine, has significant anti-hyperuricemia effects. However, it is poorly known about the interaction between lowering uric acid and regulation of lipid metabolic disorders. In this study, hyperuricemic rat model was induced by orally administration with fructose. Ultra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS) combined with pattern recognition approaches were used to determine different lipid metabolites in serum of control group, model group, and different doses of ginkgo folium groups. Principal component analysis (PCA) was applied to analyze the MS data to assess the establishment of model, partial least squares-discriminate analysis (PLS-DA) and independent samples T-test were performed to indicate the differences between different groups of rats and to find biomarkers. Metabolomics pathway analysis (MetPA) was introduced to reveal the pharmacologic mechanisms of ginkgo folium. 19 potential biomarkers associated with hyperuricemia were found. After intervened by ginkgo folium, these biomarkers were returning to normal level. Among these biomarkers, 13 lipid biomarkers were significantly reversed. Ginkgo filum can lower uric acid via adjusting back the level of PCs and LPCs, which suggested that its treatment mechanisms may be related to reducing the activity of PLA2. In sum, the lipidomics analysis in the system level have enhanced our understanding to pathogenesis of hyperuricemia and the results suggested that ginkgo folium could alleviate the abnormal metabolic status of hyperuricemia. These results demonstrated a new mechanism for lowering uric acid, which was helpful to the early treatment for hyperuricemia.

Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson's Disease

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by fibrillar cytoplasmic aggregates of α-synuclein (i.e., Lewy bodies) and the associated loss of dopaminergic cells in the substantia nigra. Mutations in genes such as α-synuclein (SNCA) account for only 10% of PD occurrences. Exposure to environmental toxicants including pesticides and metals (e.g., paraquat (PQ) and manganese (Mn)) is also recognized as an important PD risk factor. Thus, aging, genetic alterations, and environmental factors all contribute to the etiology of PD. In fact, both genetic and environmental factors are thought to interact in the promotion of idiopathic PD, but the mechanisms involved are still unclear. In this study, we summarize our findings to date regarding the toxic synergistic effect between α-synuclein and paraquat treatment. We identified an essential role for central carbon (glucose) metabolism in dopaminergic cell death induced by paraquat treatment that is enhanced by the overexpression of α-synuclein. PQ “hijacks” the pentose phosphate pathway (PPP) to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. PQ also stimulated an increase in glucose uptake, the translocation of glucose transporters to the plasma membrane, and AMP-activated protein kinase (AMPK) activation. The overexpression of α-synuclein further stimulated an increase in glucose uptake and AMPK activity, but impaired glucose metabolism, likely directing additional carbon to the PPP to supply paraquat redox cycling.

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (⩾1μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼10³-10⁴) and dynamic range (∼10³-10⁴), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas chromatography (GC) is commonly employed in conjunction with MS, but this may lead to other sources of error. As a result, NMR and mass spectrometry are highly complementary, and combining the two techniques is likely to improve the overall quality of a study and enhance the coverage of the metabolome. While the majority of metabolomic studies use a single analytical source, there is a growing appreciation of the inherent value of combining NMR and MS for metabolomics. An overview of the current state of utilizing both NMR and MS for metabolomics will be presented.

Influences of Normalization Method on Biomarker Discovery in Gas Chromatography-Mass Spectrometry-Based Untargeted Metabolomics: What Should Be Considered?

Data reduction techniques in gas chromatography-mass spectrometry-based untargeted metabolomics has made the following workflow of data analysis more lucid. However, the normalization process still perplexes researchers, and its effects are always ignored. In order to reveal the influences of normalization method, five representative normalization methods (mass spectrometry total useful signal, median, probabilistic quotient normalization, remove unwanted variation-random, and systematic ratio normalization) were compared in three real data sets with different types. First, data reduction techniques were used to refine the original data. Then, quality control samples and relative log abundance plots were utilized to evaluate the unwanted variations and the efficiencies of normalization process. Furthermore, the potential biomarkers which were screened out by the Mann-Whitney U test, receiver operating characteristic curve analysis, random forest, and feature selection algorithm Boruta in different normalized data sets were compared. The results indicated the determination of the normalization method was difficult because the commonly accepted rules were easy to fulfill but different normalization methods had unforeseen influences on both the kind and number of potential biomarkers. Lastly, an integrated strategy for normalization method selection was recommended.

Changes in fatty acid composition in the giant clam Tridacna maxima in response to thermal stress

Temperature can modify membrane fluidity and thus affects cellular functions and physiological activities. This study examines lipid remodelling in the marine symbiotic organism, Tridacna maxima, during a time series of induced thermal stress, with an emphasis on the morphology of their symbiont Symbiodinium First, we show that the French Polynesian giant clams harbour an important proportion of saturated fatty acids (SFA), which reflects their tropical location. Second, in contrast to most marine organisms, the total lipid content in giant clams remained constant under stress, though some changes in their composition were shown. Third, the stress-induced changes in fatty acid (FA) diversity were accompanied by an upregulation of genes involved in lipids and ROS pathways. Finally, our microscopic analysis revealed that for the giant clam's symbiont, Symbiodinium, thermal stress led to two sequential cell death processes. Our data suggests that the degradation of Symbiodinium cells could provide an additional source of energy to T maxima in response to heat stress.

Combining DI-ESI-MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death.

Metabolomics Reveals Inflammatory-Linked Pulmonary Metabolic Alterations in a Murine Model of House Dust Mite-Induced Allergic Asthma

Although the house dust mite (HDM) is a major environmental aeroallergen that promotes the pathogenesis and severity of allergic asthma, it remains elusive if HDM exposures can induce global metabolism aberrations during allergic airway inflammation. Using an integrated gas and liquid chromatography mass spectrometry-based metabolomics and multiplex cytokine profile analysis, metabolic alterations and cytokine changes were investigated in the bronchoalveolar lavage fluid (BALF), serum, and lung tissues in experimental HDM-induced allergic asthma. Allergic pulmonary HDM exposures lead to pronounced eosinophilia, neutrophilia, and increases in inflammatory cytokines. Metabolomics analysis of the BALF, serum, and lung tissues revealed distinctive compartmental metabolic signatures, which included depleted carbohydrates, increased energy metabolites, and consistent losses of sterols and phosphatidylcholines. Pearson correlation analysis uncovered strong associations between specific metabolic alterations and inflammatory cells and cytokines, linking altered pulmonary metabolism to allergic airway inflammation. The clinically prescribed glucocorticoid prednisolone could modulate airway inflammation but was ineffective against the reversal of many HDM-induced metabolic alterations. Collectively, metabolomics reveal comprehensive pulmonary metabolic signatures in HDM-induced allergic asthma, with specific alterations in carbohydrates, lipids, sterols, and energy metabolic pathways. Altered pulmonary metabolism may be a major underlying molecular feature involved during HDM-induced allergic airway inflammation, linked to inflammatory cells and cytokines changes.

Exploratory investigation of plasma metabolomics in human lung adenocarcinoma

Globally lung cancer is common among males and recently also noted with increasing incidences in females, especially adenocarcinoma. Further, most lung cancers are not easily detected until the late stage. Metabolic profiling of plasma low molecular weight metabolites may help unveil the complex pathophysiological changes during early lung adenocarcinoma development. Here we used a combination of gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods to investigate the metabolic signatures in the plasma of 31 stage I human lung adenocarcinoma patients and 28 healthy controls. The metabolic profiles were assayed using orthogonal projections to latent structures discriminant analysis (OPLS-DA), and were further analyzed to identify the associated marker metabolites. The OPLS-DA models derived from both GC-MS and LC-MS showed significant discriminations in metabolic profiles between cases and healthy controls. It was found that around 37 metabolites contributed to the differences. The alterations of these metabolites implied disturbances in amino acids, lipids, fatty acids and glutaminolysis metabolism in human lung adenocarcinoma, even after removal of influencing factors such as age, gender and smoking habits. Of particular interest, the sex hormone metabolic pathway involving the sulfate conjugate of testosterone, androsterone and pregnenolone was found to be disturbed considerably. All these metabolic perturbations occur at an early stage of lung adenocarcinoma and thus could act as biomarkers for its early diagnosis. These exploratory findings suggest that integration of two sensitive and complementary metabolomic approaches enables a comprehensive metabolite profiling for human lung adenocarcinoma, although a more extensive study is needed to confirm the findings.

Metabolic profiling of plasma from benign and malignant pulmonary nodules patients using mass spectrometry-based metabolomics

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs.

Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database

Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2-4 h to complete per feature, depending on the compound.

Metabolomics reveals altered metabolic pathways in experimental asthma

Metabolomics refers to the comprehensive analysis of metabolites in biological systems, and has been employed to study patients with asthma based on their urinary metabolite profile. We hypothesize that airway allergic asthma would affect metabolism in the lungs, and could be detected in bronchoalveolar lavage (BAL) fluid (BALF) using a combined liquid chromatography- and gas chromatography-mass spectrometry (MS) platform. The objective of this study was to investigate changes of lung metabolism in allergic asthma by metabolomic analysis of BALF. BALB/c mice were sensitized and challenged with ovalbumin to develop experimental asthma. Dexamethasone was administered to study the effects of corticosteroids on lung metabolism. Metabolites in BALF were measured using liquid chromatography-MS and gas chromatography-MS, and multivariate statistical analysis was performed by orthogonal projections to latent structures discriminant analysis. Metabolomic analysis of BALF from ovalbumin-challenged mice revealed novel changes in metabolic pathways in the lungs as compared with control animals. These metabolite changes suggest alterations of energy metabolism in asthmatic lungs, with increases of lactate, malate, and creatinine and reductions in carbohydrates, such as mannose, galactose, and arabinose. Lipid and sterol metabolism were affected with significant decreases in phosphatidylcholines, diglycerides, triglycerides, cholesterol, cortol, and cholic acid. Dexamethasone treatment effectively reversed many key metabolite changes, but was ineffective in repressing lactate, malate, and creatinine, and induced additional metabolite changes. Metabolomic analysis of BALF offers a promising approach to investigating allergic asthma. Our overall findings revealed considerable pathway changes in lung metabolism in asthmatic lungs, including energy, amino acids, and lipid metabolism.

Potential role of metabolomic approaches for Chinese medicine syndromes and herbal medicine

Systems biology has significantly increased in recent years. Its method and design resemble those of traditional Chinese medicine (TCM), which is a unique medical system that assisted the ancient Chinese in dealing with disease. The technology platforms of systems biology, especially metabolomics, could provide more rapid, direct, concise and effective methods for disease research. In particular, metabolomics could provide useful tools for exploring the essence of Chinese medicine syndromes (CMS) disease, facilitating personalized TCM. Moreover, metabolomics has the potential to enable mapping of early biochemical changes in disease and hence provide an opportunity to develop predictive biomarkers that can trigger earlier interventions. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from disease and enable early diagnosis of the disease or the identification of those at risk, and it will help to understand CMS and modernize TCM. In this review, we focus on the key role of robust metabolomic approaches as an aid for traditional herbal medicine; particular attention will be paid to the past successes in applications of metabonomics to contribute to low-molecular-weight metabolites (biomarkers) discovery in TCM research.

Potential role of metabolomics apporoaches in the area of traditional Chinese medicine: as pillars of the bridge between Chinese and Western medicine

Traditional Chinese medicine (TCM) is a holistic approach to health that attempts to bring the body, mind and spirit into harmony. Entering 21st century, TCM is getting more and more popular in the whole world for improving health condition of human beings and preventing or healing diseases, especially shows great advantages in early intervention, combination therapies and personalized medicine, etc. However, like almost all other ethnopharmacology, TCM also faces severe challenges and suffers from insufficient modern research owing to lack of scientific and technologic approaches, restricts the development of TCM in the world. Fortunately, a novel analytical technique, metabolomics (or metabonomics), adopts a 'top-down' strategy to reflect the function of organisms from terminal symptoms of metabolic network and understand metabolic changes of a complete system caused by interventions in holistic context. Its property consists with the holistic thinking of TCM, may beneficially provide an opportunity to scientifically express the meaning of evidence-based Chinese medicine, such as Chinese medicine syndromes (CMS), preventive treatment, action of Chinese medicine, Chinese medical formulae (CMF) and acupuncture efficacy. This review summarizes potential applications of robust metabolomics apporoaches in the area of traditional oriental medicine, and highlights the key role of metabolomics to resolve special TCM issues.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.