From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Metabolipidomic Analysis in Patients with Obstructive Sleep Apnea Discloses a Circulating Metabotype of Non-Dipping Blood Pressure

A non-dipping blood pressure (BP) pattern, which is frequently present in patients with obstructive sleep apnea (OSA), confers high cardiovascular risk. The mechanisms connecting these two conditions remain unclear. In the present study we performed a comprehensive analysis of the blood metabolipidome that aims to provide new insights into the molecular link between OSA and the dysregulation of circadian BP rhythmicity. This was an observational prospective longitudinal study involving adults with suspected OSA who were subjected to full polysomnography (PSG). Patients with an apnea-hypopnea index ≥ 5 events/h were included. Fasting plasma samples were obtained the morning after PSG. Based on the dipping ratio (DR; ratio of night/day BP values) measured via 24 h ambulatory BP monitoring, two groups were established: dippers (DR ≤ 0.9) and non-dippers (DR > 0.9). Treatment recommendations for OSA followed the clinical guidelines. Untargeted metabolomic and lipidomic analyses were performed in plasma samples via liquid chromatography-tandem mass spectrometry. Non-dipper patients represented 53.7% of the cohort (88/164 patients). A set of 31 metabolic species and 13 lipidic species were differentially detected between OSA patients who present a physiologic nocturnal BP decrease and those with abnormal BP dipping. Among the 44 differentially abundant plasma compounds, 25 were putatively identified, notably glycerophospholipids, glycolipids, sterols, and fatty acid derivates. Multivariate analysis defined a specific metabotype of non-dipping BP, which showed a significant dose-response relationship with PSG parameters of OSA severity, and with BP dipping changes after 6 months of OSA treatment with continuous positive airway pressure (CPAP). Bioinformatic analyses revealed that the identified metabolipidomic profile was found to be implicated in multiple systemic biological pathways, with potential physiopathologic implications for the circadian control of BP among individuals with OSA.

Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets

Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Respiratory Depression as Antibacterial Mechanism of Linalool against Pseudomonas fragi Based on Metabolomics

Linalool showed a broad-spectrum antibacterial effect, but few studies have elucidated the antibacterial mechanism of linalool on Pseudomonas fragi (P. fragi) to date. The present study aimed to uncover the antimicrobial activity and potential mechanism of linalool against P. fragi by determining key enzyme activities and metabolites combined with a high-throughput method and metabolomic pathway analysis. As a result, linalool had excellent inhibitory activity against P. fragi with MIC of 1.5 mL/L. In addition, the presence of linalool significantly altered the intracellular metabolic profile and a total of 346 differential metabolites were identified, of which 201 were up-regulated and 145 were down-regulated. The highlight pathways included beta-alanine metabolism, pantothenic acid and CoA metabolism, alanine, aspartate and glutamate metabolism, nicotinate and nicotinamide metabolism. Overall, linalool could cause metabolic disorders in cells, and the main metabolic pathways involved energy metabolism, amino acid metabolism and nucleic acid metabolism. In particular, the results of intracellular ATP content and related enzymatic activities (ATPase, SDH, and GOT) also highlighted that energy limitation and amino acid disturbance occurred intracellularly. Together, these findings provided new insights into the mechanism by which linalool inhibited P. fragi and theoretical guidance for its development as a natural preservative.

Bioactive Compounds from Marine Sponges and Algae: Effects on Cancer Cell Metabolome and Chemical Structures

Metabolomics represent the set of small organic molecules generally called metabolites, which are located within cells, tissues or organisms. This new “omic” technology, together with other similar technologies (genomics, transcriptomics and proteomics) is becoming a widely used tool in cancer research, aiming at the understanding of global biology systems in their physiologic or altered conditions. Cancer is among the most alarming human diseases and it causes a considerable number of deaths each year. Cancer research is one of the most important fields in life sciences. In fact, several scientific advances have been made in recent years, aiming to illuminate the metabolism of cancer cells, which is different from that of healthy cells, as suggested by Otto Warburg in the 1950s. Studies on sponges and algae revealed that these organisms are the main sources of the marine bioactive compounds involved in drug discovery for cancer treatment and prevention. In this review, we analyzed these two promising groups of marine organisms to focus on new metabolomics approaches for the study of metabolic changes in cancer cell lines treated with chemical extracts from sponges and algae, and for the classification of the chemical structures of bioactive compounds that may potentially prove useful for specific biotechnological applications.

Applications of Metabolomics in Calcium Metabolism Disorders in Humans

The pathogenesis of the disorders of calcium metabolism is not fully understood. This review discusses the studies in which metabolomics was applied in this area. Indeed, metabolomics could play an essential role in discovering biomarkers and elucidating pathological mechanisms. Despite the limited bibliography, the present review highlights the potential of metabolomics in identifying the biomarkers of some of the most common endocrine disorders, such as primary hyperparathyroidism (PHPT), secondary hyperparathyroidism (SHPT), calcium deficiency, osteoporosis and vitamin D supplementation. Metabolites related to above-mentioned diseorders were grouped into specific classes and mapped into metabolic pathways. Furthermore, disturbed metabolic pathways can open up new directions for the in-depth exploration of the basic mechanisms of these diseases at the molecular level.

Metabolome analysis of the response and tolerance mechanisms of Saccharomyces cerevisiae to formic acid stress

Various inhibitors are produced during the hydrolysis of lignocellulosic biomass that can interfere with the growth of yeast cells and the production of bioethanol. Formic acid is a common weak acid inhibitor present in lignocellulosic hydrolysate that has toxic effects on yeast cells. However, the mechanism of the response of Saccharomyces cerevisiae to formic acid is not fully understood. In this study, liquid chromatography-mass spectrometry (LC-MS) was used to investigate the effects of formic acid treatment on cell metabolites of S. cerevisiae. Treatment with different concentrations of formic acid significantly inhibited the growth of yeast cells, reduced the yield of ethanol, prolonged the cell fermentation cycle, and increased the content of malondialdehyde. Principal component analysis and orthogonal partial least squares discriminant analysis showed that 55 metabolites were significantly altered in S. cerevisiae after formic acid treatment. The metabolic relevance of these compounds in the response of S. cerevisiae to formic acid stress was investigated. Formic acid can cause oxidative stress, inhibit protein synthesis, and damage DNA in S. cerevisiae, and these are possible reasons for the inhibition of S. cerevisiae cell growth. In addition, the levels of several aromatic amino acids identified in the cells of formic acid-treated yeast were increased; the biosynthesis of nucleotides was slowed, and energy consumption was reduced. These mechanisms may help to improve the tolerance of yeast cells to formic acid. The results described herein highlight our current understanding of the molecular mechanism of the response of S. cerevisiae to formic acid. The study will provide a theoretical basis for research on the tolerance mechanisms of S. cerevisiae.

Enhanced antibacterial efficacy and mechanism of octyl gallate/beta-cyclodextrins against Pseudomonas fluorescens and Vibrio parahaemolyticus and incorporated electrospun nanofibers for Chinese giant salamander fillets preservation

A series of alkyl gallates were evaluated for the antibacterial activity against two common Gram-negative foodborne bacteria (Pseudomonas fluorescens and Vibrio parahaemolyticus) associated with seafood. The length of the alkyl chain plays a pivotal role in eliciting their antibacterial activities and octyl gallate (OG) exerted an excellent inhibitory efficacy. To extend the aqueous solubility, stability, and bactericidal properties of octyl gallate (OG), an inclusion complex between OG and β-cyclodextrin (βCD), OG/βCD, was prepared and identified with various methods including X-ray diffraction (XRD), differential scanning calorimeter (DSC) and Fourier transform infrared spectroscopy (FTIR). Furthermore, the enhanced inhibitory effect and potential antibacterial mechanism of OG/βCD against two Gram-negative and Gram-positive foodborne bacteria were comprehensively investigated. The results show that OG/βCD could function against bacteria through effectively damaging the membrane, permeating into cells, and then disturbing the activity of the respiratory electron transport chain to cause the production of high-level intracellular hydroxyl radicals. Moreover, the reinforced OG/βCD-incorporated polylactic acid (PLA) nanofibers were fabricated using the electrospinning technique as food packaging to extend the Chinese giant salamander fillet's shelf life at 4 °C. This research highlights the antibacterial effectiveness of OG/βCD in aqueous media, which can be used as a safe multi-functionalized food additive combined with the benefits of electrospun nanofibers to extend the Chinese giant salamander fillets shelf life by 15 d at 4 °C.

Dynamic Evolution and Correlation between Metabolites and Microorganisms during Manufacturing Process and Storage of Fu Brick Tea

Fu brick tea (FBT) is one of the major brands of dark tea. Microbial fermentation is considered the key step in the development of the special characteristics of FBT. The systemic corelationship of the microbiome and metabolomics during manufacture of Fu brick tea is not fully understood. In this study, we comprehensively explored the microbiome and metabolite dynamic evolution during the FBT manufacturing processes, and revealed decisive factors for the quality and safety of FBT based on the grouped methods of metabolomics combined with biochemical measurements, microbiome sequencing combined with quantitative polymerase chain reaction (PCR), and multiplex analysis. Both the microbiome and quantitative PCR showed that fungi displayed concentrated distribution characteristics in the primary dark tea samples, while bacterial richness increased during the flowering processes and ripening period. All microorganism species, as well as dominant fungi and bacteria, were identified in the distinct processes periods. A total of 178 metabolites were identified, and 34 of them were characterized as critical metabolites responsible for metabolic changes caused by the corresponding processes. Metabolic analysis showed that most metabolites were decreased during the FBT manufacturing processes, with the exception of gallic acid. Multivariate analysis verified that the critical metabolites were correlated with specific dominant microbial species. All the top fungal species except unclassified_g_ Aspergillus showed positive correlations with six critical metabolites (L-The, epigallocatechin (EGC), Gln, tea polyphenol (TP), tea polysaccharides (TPs) and caffeine). Five of the top bacteria species (Cronobacter, Klebsiella, Pantoea, Pluralibacter, and unclassified_ f_Entero-bacteriaceae) showed positive correlations with epigallocatechins and tea polyphenols, while the other 11 top bacterial species correlated negatively with all the critical metabolites. The content of amino acids, tea polyphenols, tea polysaccharides, and flavonoids was reduced during microbial fermentation. In conclusion, our results reveal that microbial composition is the critical factor in changing the metabolic profile of FBT. This discovery provides a theoretical basis for improving the quality of FBT and enhancing its safety.

Advances in SIV/SHIV Non-Human Primate Models of NeuroAIDS

Non-human primates (NHPs) are the most relevant model of Acquired Immunodeficiency Syndrome (AIDS) and neuroAIDS, being of great importance in explaining the pathogenesis of HIV-induced nervous system damage. Simian Immunodeficiency Virus (SIV)/ Simian-Human Immunodeficiency Virus (SHIV)-infected monkeys have provided evidence of complex interactions between the virus and host that include host immune response, viral genetic diversity, and genetic susceptibility, which may explain virus-associated central nervous system (CNS) pathology and HIV-associated neurocognitive disorders (HAND). In this article, we review the recent progress contributions obtained using monkey models of HIV infection of the CNS, neuropathogenesis and SIV encephalitis (SIVE), with an emphasis on pharmacologic therapies and dependable markers that predict development of CNS AIDS.

Metabolomics and metabolites in ischemic stroke

Stroke is a major reason for disability and the second highest cause of death in the world. When a patient is admitted to a hospital, it is necessary to identify the type of stroke, and the likelihood for development of a recurrent stroke, vascular dementia, and depression. These factors could be determined using different biomarkers. Metabolomics is a very promising strategy for identification of biomarkers. The advantage of metabolomics, in contrast to other analytical techniques, resides in providing low molecular weight metabolite profiles, rather than individual molecule profiles. Technically, this approach is based on mass spectrometry and nuclear magnetic resonance. Furthermore, variations in metabolite concentrations during brain ischemia could alter the principal neuronal functions. Different markers associated with ischemic stroke in the brain have been identified including those contributing to risk, acute onset, and severity of this pathology. In the brain, experimental studies using the ischemia/reperfusion model (IRI) have shown an impaired energy and amino acid metabolism and confirmed their principal roles. Literature data provide a good basis for identifying markers of ischemic stroke and hemorrhagic stroke and understanding metabolic mechanisms of these diseases. This opens an avenue for the successful use of identified markers along with metabolomics technologies to develop fast and reliable diagnostic tools for ischemic and hemorrhagic stroke.

In vivo2H/13C flux analysis in metabolism research

Identifying the factors and mechanisms that regulate metabolism under normal and diseased states requires methods to quantify metabolic fluxes of live tissues within their physiological milieu. A number of recent developments have expanded the reach and depth of isotope-based in vivo flux analysis, which have in turn challenged existing dogmas in metabolism research. First, minimally invasive techniques of intravenous isotope infusion and sampling have advanced in vivo metabolic tracer studies in animal models and human subjects. Second, recent breakthroughs in analytical instrumentation have expanded the scope of isotope labeling measurements and reduced sample volume requirements. Third, innovative modeling approaches and publicly available software tools have facilitated rigorous analysis of sophisticated experimental designs involving multiple tracers and expansive metabolomics datasets. These developments have enabled comprehensive in vivo quantification of metabolic fluxes in specific tissues and have set the stage for integrated multi-tissue flux assays.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

Antimicrobial effect and mechanism of non-antibiotic alkyl gallates against Pseudomonas fluorescens on the surface of Russian sturgeon (Acipenser gueldenstaedti)

Since Pseudomonas fluorescens is the main microorganism causing severe spoilage in refrigerated aquatic products, the searching for non-antibiotic antibacterial agents effective against it continues to receive increasing interest. This study aimed to investigate the antibacterial effects and mechanisms of alkyl gallic esters against P. fluorescens isolated from the Russian sturgeon (Acipenser gueldenstaedti), as well as the effectiveness in combination with chitosan films on the preservation of sturgeon meats at 4 °C. Our data shows that the alkyl chain length plays a significant role in eliciting their antibacterial activities and octyl gallate (GAC8) exhibited an outstanding inhibitory efficacy. GAC8 can rapidly enter into the membrane lipid bilayer portion to disorder the membrane, and further inhibit the growth of the P. fluorescens through interfering both tricarboxylic acid cycle related to energy supply and amino acid metabolism associated with cell membranes, suppressing oxygen consumption and disturbing the respiration chain. Moreover, the alteration in membrane fatty acids indicated that GAC8 could disrupt the composition of cell membrane fatty acids, rendering the bacteria more sensitive to the antibacterial. The SEM results also substantiate the damage of the structure of the bacterial membrane caused by GAC8. Additionally, the edible chitosan-based films incorporated with GAC8 showed the enhanced antibacterial efficacy to remarkably extend the shelf life of Russian sturgeon. Overall, our findings not only provide new insight into the mode of action of GAC8 against P. fluorescens but also demonstrate composite films containing GAC8, as a kind of safe and antibacterial material, have a great promise for application in food preservations.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.

Analyses of Cholesterol Metabolites of Optic Nerve Using GC-MS Methods

Gas chromatography-mass spectrometry (GC-MS) is considered the gold standard for analyzing and quantifying the presence of biological compounds in tissue samples due to its high sensitivity, peak resolution, and reproducibility. In this chapter, we describe a step-by-step modified Bligh and Dyer protocol for lipid extraction from the optic nerve tissue and a procedure for GC-MS analyses of the lipid extract. These protocols are based on our experience and can be modified depending on samples and compounds of interest.

Assessing metabolic properties of dairy cows fed low quality straws by integrative arterial and venous metabolomics

OBJECTIVE

This study was conducted to reveal potential metabolic differences of dairy cows fed corn stover (CS) and rice straw (RS) instead of alfalfa hay (AH) as main forage source.

METHODS

Thirty multiparous mid-late lactation Holstein dairy cows were selected and randomly assigned to three diets, AH, CS, or RS (n = 10). After 13 weeks of the feeding trial, coccygeal arterial and superficial epigastric venous plasma samples were collected before morning feeding for gas chromatography time-of-flight/mass spectrometry analyses.

RESULTS

In the artery, 8 and 13 metabolites were detected as differential metabolites between AH and CS, and between AH and RS, respectively. The relative abundance of phenylpropanoate (log2fold change [FC]) = 1.30, 1.09), panthenol (log2FC = 2.36, 2.20), threitol (log2FC = 1.00, 1.07), and 3,7,12-trihydroxycoprostane (log2FC = 0.79, 0.78) were greater in both CS and RS than in AH, and tyrosine (log2FC = -0.32), phenylalanine (log2FC = -0.30), and pyruvic acid (log2FC = -0.30) were lower in RS than in AH. In the vein, 1 and 7 metabolites were detected as differential metabolites between AH and CS, and between AH and RS, respectively. By comparing AH and RS, we found that metabolic pathways of phenylalanine, tyrosine, and tryptophan biosynthesis and phenylalanine metabolism were enriched by integrative artery and vein analysis. Furthermore, AH and RS, arterial phenylpropanoate and 4-hydroxyproline were positively, and phenylalanine was negatively correlated with milk urea nitrogen. Finally, in AH and CS, arterial panthenol was negatively correlated with feed efficiency.

CONCLUSION

Arterial metabolic profiles changed more than those in the veins from animals on three forage diets, differing in amino acids. We found that phenylalanine, tyrosine, and tryptophan biosynthesis and phenylalanine metabolism were restricted when cows were fed low-quality cereal straw diets.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

Advances of Metabolomics in Fungal Pathogen-Plant Interactions

Plant disease caused by fungus is one of the major threats to global food security, and understanding fungus-plant interactions is important for plant disease control. Research devoted to revealing the mechanisms of fungal pathogen-plant interactions has been conducted using genomics, transcriptomics, proteomics, and metabolomics. Metabolomics research based on mass spectrometric techniques is an important part of systems biology. In the past decade, the emerging field of metabolomics in plant pathogenic fungi has received wide attention. It not only provides a qualitative and quantitative approach for determining the pathogenesis of pathogenic fungi but also helps to elucidate the defense mechanisms of their host plants. This review focuses on the methods and progress of metabolomics research in fungal pathogen-plant interactions. In addition, the prospects and challenges of metabolomics research in plant pathogenic fungi and their hosts are addressed.

Electric modeling and characterization of pulsed high-voltage nanoelectrospray ionization sources by a miniature ion trap mass spectrometer

A better understanding of nanoelectrospray ionization (nano-ESI) would be beneficial in further improving the performances of nano-ESI. In this work, the pulsed high-voltage (HV) nano-ESI has been electrically modeled and then systematically characterized by both voltage-current and mass spectrometry measurements. First, the equivalent resistance of a nano-ESI source changes with respect to both emitter tip diameter and the HV applied. Increased voltage could improve both spray current and ionization efficiency of the pulsed HV nano-ESI. Compared with conventional DC HV method, a pulsed HV has less heating effect on the capillary tip and thus allowing the application of a much higher voltage onto a nano-ESI source. As a result, a pulsed HV nano-ESI could further boost the ionization efficiency of nano-ESI by employing even higher voltages than conventional DC nano-ESI sources.

HILIC-Enabled 13C Metabolomics Strategies: Comparing Quantitative Precision and Spectral Accuracy of QTOF High- and QQQ Low-Resolution Mass Spectrometry

Dynamic ¹³C-tracer-based flux analyses of in vivo reaction networks still require a continuous development of advanced quantification methods applying state-of-the-art mass spectrometry platforms. Utilizing alkaline HILIC chromatography, we adapt strategies for a systematic quantification study in non- and ¹³C-labeled multicomponent endogenous Corynebacterium glutamicum extracts by LC-QTOF high resolution (HRMS) and LC-QQQ tandem mass spectrometry (MS/MS). Without prior derivatization, a representative cross-section of 17 central carbon and anabolic key intermediates were analyzed with high selectivity and sensitivity under optimized ESI-MS settings. In column detection limits for the absolute quantification range were between 6.8-304.7 (QQQ) and 28.7-881.5 fmol (QTOF) with comparable linearities (3-5 orders of magnitude) and enhanced precision using QQQ-MRM detection. Tailor-made preparations of uniformly (U)¹³C-labeled cultivation extracts for isotope dilution mass spectrometry enabled the accurate quantification in complex sample matrices and extended linearities without effect on method parameters. Furthermore, evaluation of metabolite-specific m+1-to-m+0 ratios (ISR_1:0) in non-labeled extracts exhibited sufficient methodical spectral accuracies with mean deviations of 3.89 ± 3.54% (QTOF) and 4.01 ± 3.01% (QQQ). Based on the excellent HILIC performance, conformity analysis of time-resolved isotopic enrichments in ¹³C-tracer experiments revealed sufficient spectral accuracy for QQQ-SIM detection. However, only QTOF-HRMS ensures determination of the full isotopologue space in complex matrices without mass interferences.

Biomarker for Ischemic Stroke Using Metabolome: A Clinician Perspective

Finding ischemic stroke biomarker is highly desirable because it can improve diagnosis even before a patient arrives to the hospital. Metabolome is one of new technologies that help to find biomarkers. Most metabolome-related ischemic stroke studies were done in Asia and had exploratory designs. Although failed to find specific biomarkers, they discovered several important metabolite-stroke associations which belong to three pathophysiological mechanisms: Excitotoxicity with activation of glutamate, resulting in the increase of glutamate derivatives proline and pyroglutamate; Oxidative stress with production of free radicals and perturbed concentrations of uric acid, matrix metalloproteinase-9, branch-chained amino acids, sphingolipids, homocysteine, asymmetric dimethylarginine, nitric oxide and folate cycle metabolites; and Stroke mediated inflammation, affecting phospholipid metabolism with perturbed levels of lysophosphatidylethanolamine and lysophosphatidylcholine. The discovered metabolite-stroke associations need further evaluation in prospective, high-quality studies with patients matched for age, risk factors, and medications.

Strategies for large-scale targeted metabolomics quantification by liquid chromatography-mass spectrometry

Advances in liquid chromatography-mass spectrometry (LC-MS) instruments and analytical strategies have brought about great progress in targeted metabolomics analysis. This methodology is now capable of performing precise targeted measurement of dozens or hundreds of metabolites in complex biological samples. Classic targeted quantification assay using the multiple reaction monitoring (MRM) mode has been the foundation of high-quality metabolite quantitation. However, utilization of this strategy in biological studies has been limited by its relatively low metabolite coverage and throughput capacity. A number of methods for large-scale targeted metabolomics assay which have been developed overcome these limitations. These strategies have enabled extended metabolite coverage which is defined as targeting of large numbers of metabolites, while maintaining reliable quantification performance. These recently developed techniques thus bridge the gap between traditional targeted metabolite quantification and untargeted metabolomics profiling, and have proven to be powerful tools for metabolomics study. Although the LC-MRM-MS strategy has been used widely in large-scale metabolomics quantification analysis due to its fast scan speed and ideal analytic stability, there are still drawbacks which are due to the low resolution of the triple quadrupole instruments used for MRM assays. New approaches have been developed to expand the options for large-scale targeted metabolomics study, using high-resolution instruments such as parallel reaction monitoring (PRM). MRM and PRM-based techniques are now attractive strategies for quantitative metabolomics analysis and high-throughput biomarker discovery. Here we provide an overview of the major developments in LC-MS-based strategies for large-scale targeted metabolomics quantification in biological samples. The advantages of LC-MRM/PRM-MS based analytical strategies which may be used in multiplexed and high throughput quantitation for a wide range of metabolites are highlighted. In particular, PRM and MRM strategies are compared, and we summarize the work flow commonly used for large-scale targeted metabolomics analysis including sample preparation, LC separation and data analysis, as well as recent applications in biological studies.

Preparation of Drosophila Larval Samples for Gas Chromatography-Mass Spectrometry (GC-MS)-based Metabolomics

Recent advances in the field of metabolomics have established the fruit fly Drosophila melanogaster as a powerful genetic model for studying animal metabolism. By combining the vast array of Drosophila genetic tools with the ability to survey large swaths of intermediary metabolism, a metabolomics approach can reveal complex interactions between diet, genotype, life-history events, and environmental cues. In addition, metabolomics studies can discover novel enzymatic mechanisms and uncover previously unknown connections between seemingly disparate metabolic pathways. In order to facilitate more widespread use of this technology among the Drosophila community, here we provide a detailed protocol that describes how to prepare Drosophila larval samples for gas chromatography-mass spectrometry (GC-MS)-based metabolomic analysis. Our protocol includes descriptions of larval sample collection, metabolite extraction, chemical derivatization, and GC-MS analysis. Successful completion of this protocol will allow users to measure the relative abundance of small polar metabolites, including amino acids, sugars, and organic acids involved in glycolysis and the TCA cycles.

Functional Genomics Approaches to Studying Symbioses between Legumes and Nitrogen-Fixing Rhizobia

Biological nitrogen fixation gives legumes a pronounced growth advantage in nitrogen-deprived soils and is of considerable ecological and economic interest. In exchange for reduced atmospheric nitrogen, typically given to the plant in the form of amides or ureides, the legume provides nitrogen-fixing rhizobia with nutrients and highly specialised root structures called nodules. To elucidate the molecular basis underlying physiological adaptations on a genome-wide scale, functional genomics approaches, such as transcriptomics, proteomics, and metabolomics, have been used. This review presents an overview of the different functional genomics approaches that have been performed on rhizobial symbiosis, with a focus on studies investigating the molecular mechanisms used by the bacterial partner to interact with the legume. While rhizobia belonging to the alpha-proteobacterial group (alpha-rhizobia) have been well studied, few studies to date have investigated this process in beta-proteobacteria (beta-rhizobia).

Promises and pitfalls of untargeted metabolomics

Metabolomics is one of the newer omics fields, and has enabled researchers to complement genomic and protein level analysis of disease with both semi-quantitative and quantitative metabolite levels, which are the chemical mediators that constitute a given phenotype. Over more than a decade, methodologies have advanced for both targeted (quantification of specific analytes) as well as untargeted metabolomics (biomarker discovery and global metabolite profiling). Untargeted metabolomics is especially useful when there is no a priori metabolic hypothesis. Liquid chromatography coupled to mass spectrometry (LC-MS) has been the preferred choice for untargeted metabolomics, given the versatility in metabolite coverage and sensitivity of these instruments. Resolving and profiling many hundreds to thousands of metabolites with varying chemical properties in a biological sample presents unique challenges, or pitfalls. In this review, we address the various obstacles and corrective measures available in four major aspects associated with an untargeted metabolomics experiment: (1) experimental design, (2) pre-analytical (sample collection and preparation), (3) analytical (chromatography and detection), and (4) post-analytical (data processing).

Arteriovenous blood metabolomics: An efficient method to determine the key metabolic pathway for milk synthesis in the intra-mammary gland

The present study aimed to identify metabolic signature changes of the arteriovenous metabolome and the new metabolites that involved in mammary biological process during milk synthesis. GC/MS-based metabolomics profiling of arteriovenous plasma from 30 lactating dairy cows fed three diets identified a total of 144 metabolites. Phenylalanine and tyrosine, involved in aminoacyl-tRNA biosynthesis and phenylalanine metabolism, were shown higher expression in the artery than in the vein based on both GC/MS and targeted analysis for cows fed both alfalfa hay diet and rice straw diet. Mammary uptake or clearance of citric acid, stearic acid, oleic acid, fructose, β-mannosylglycerate, 4-hydroxybutyrate, and D-talose were significantly correlated with milk performance or feed intake, indicating that these metabolites might be newly identified precursors or indicators of milk synthesis. This comprehensive assessment of metabolic changes in the arteriovenous metabolome will provide a fundamental understanding of the key metabolites involved in milk synthesis and shows implications of how metabolites from arteriovenous plasma across MG are involved in biological processes or physiological functions for milk synthesis. The newly identified metabolites from the present study provide potential new targeted insights into the study of physiological process for milk synthesis in the MG.

Metabolomics analysis reveals an effect of homocysteine on arachidonic acid and linoleic acid metabolism pathway

An increase in serum homocysteine level has been associated with an increased risk of vascular disease; however, the biochemical mechanisms that underlie these effects remain largely unknown. The present study aimed to use high-performance liquid chromatography-mass spectrometry (HPLC‑MS) to demonstrate the effects of serum homocysteine on human blood metabolites. A total of 75 fasting serum samples were investigated in the present study. Using a threshold of 15 µmol/l serum homocysteine level, samples were divided into high‑ and low‑homocysteine groups, and the serum extracts were analyzed with an HPLC‑MS‑based method. A total of 269 features exhibited significant differences and correlation with serum homocysteine levels in the electrospray ionization‑positive [ESI(+)] mode, and 69 features were identified in the ESI(‑) mode between the two groups. The principal component analysis plot revealed a separation between the high‑ and the low‑homocysteine groups. Metabolite set enrichment analysis identified arachidonic acid metabolism and linoleic acid metabolism as the two pathways with significantly enriched differences. These results revealed that arachidonic acid and linoleic acid metabolism may be associated with serum homocysteine levels and may be involved in homocysteine-induced vascular disease.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.

Exposure Marker Discovery of Phthalates Using Mass Spectrometry

Phthalates are chemicals widely used in industry and the consequences on human health caused by exposure to these agents are of significant interest currently. The urinary metabolites of phthalates can be measured and used as exposure markers for the assessment of the actual internal contamination of phthalates coming from different sources and absorbed by various ways. The purpose of this paper is to review the markers for exposure and risk assessment of phthalates such as di-methyl phthalate (DMP), di-ethyl phthalate (DEP), di-butyl phthalate (DBP), benzylbutyl phthalate (BBP), di-(2-ethylhexyl)phthalate (DEHP), di-(2-propylheptyl)phthalate (DPHP), di-iso-nonyl phthalate (DINP), di-n-octyl phthalate (DnOP) and di-iso-decyl phthalate (DIDP), and introduction of the analytical approach of three metabolomics data processing approaches that can be used for chemical exposure marker discovery in urine with high-resolution mass spectrometry (HRMS) data.

A Nontargeted UHPLC-HRMS Metabolomics Pipeline for Metabolite Identification: Application to Cardiac Remote Ischemic Preconditioning

In recent years, the number of investigations based on nontargeted metabolomics has increased, although often without a thorough assessment of analytical strategies applied to acquire data. Following published guidelines for metabolomics experiments, we report a validated nontargeted metabolomics strategy with pipeline for unequivocal identification of metabolites using the MSMLS molecule library. We achieved an in-house database containing accurate m/z values, retention times, isotopic patterns, full MS, and MS/MS spectra. A UHPLC-HRMS Q-Exactive method was developed, and experimental variations were determined within and between 3 experimental days. The extraction efficiency as well as the accuracy, precision, repeatability, and linearity of the method were assessed, the method demonstrating good performances. The methodology was further blindly applied to plasma from remote ischemic pre-conditioning (RIPC) rats. Samples, previously analyzed by targeted metabolomics using completely different protocol, analytical strategy, and platform, were submitted to our analytical pipeline. A combination of multivariate and univariate statistical analyses was employed. Selection of putative biomarkers from OPLS-DA model and S-plot was combined to jack-knife confidence intervals, metabolites' VIP values, and univariate statistics. Only variables with strong model contribution and highly statistical reliability were selected as discriminated metabolites. Three biomarkers identified by the previous targeted metabolomics study were found in the current work, in addition to three novel metabolites, emphasizing the efficiency of the current methodology and its ability to identify new biomarkers of clinical interest, in a single sequence. The biomarkers were identified to level 1 according to the metabolomics standard initiative and confirmed by both RPLC and HILIC-HRMS.

Metabolomic applications in hepatocellular carcinoma: toward the exploration of therapeutics and diagnosis through small molecules

Hepatocellular carcinoma (HCC), a complex public health issue that is the most common primary hepatic malignancy, remains the highest incidence in developing countries and is showing sustained growth across the developed world.

Discrimination of pancreatic cancer and pancreatitis by LC-MS metabolomics

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.

OBJECTIVES

The aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).

METHODS

A discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.

RESULTS

Glycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.

CONCLUSION

Using LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

SPE-IMS-MS: An automated platform for sub-sixty second surveillance of endogenous metabolites and xenobiotics in biofluids

Characterization of endogenous metabolites and xenobiotics is essential to deconvoluting the genetic and environmental causes of disease. However, surveillance of chemical exposure and disease-related changes in large cohorts requires an analytical platform that offers rapid measurement, high sensitivity, efficient separation, broad dynamic range, and application to an expansive chemical space. Here, we present a novel platform for small molecule analyses that addresses these requirements by combining solid-phase extraction with ion mobility spectrometry and mass spectrometry (SPE-IMS-MS). This platform is capable of performing both targeted and global measurements of endogenous metabolites and xenobiotics in human biofluids with high reproducibility (CV 6 3%), sensitivity (LODs in the pM range in biofluids) and throughput (10-s sample-to-sample duty cycle). We report application of this platform to the analysis of human urine from patients with and without type 1 diabetes, where we observed statistically significant variations in the concentration of disaccharides and previously unreported chemical isomers. This SPE-IMS-MS platform overcomes many of the current challenges of large-scale metabolomic and exposomic analyses and offers a viable option for population and patient cohort screening in an effort to gain insights into disease processes and human environmental chemical exposure.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

Applications of Fourier Transform Ion Cyclotron Resonance (FT-ICR) and Orbitrap Based High Resolution Mass Spectrometry in Metabolomics and Lipidomics

Metabolomics, along with other "omics" approaches, is rapidly becoming one of the major approaches aimed at understanding the organization and dynamics of metabolic networks. Mass spectrometry is often a technique of choice for metabolomics studies due to its high sensitivity, reproducibility and wide dynamic range. High resolution mass spectrometry (HRMS) is a widely practiced technique in analytical and bioanalytical sciences. It offers exceptionally high resolution and the highest degree of structural confirmation. Many metabolomics studies have been conducted using HRMS over the past decade. In this review, we will explore the latest developments in Fourier transform mass spectrometry (FTMS) and Orbitrap based metabolomics technology, its advantages and drawbacks for using in metabolomics and lipidomics studies, and development of novel approaches for processing HRMS data.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.