Biological variability dominates and influences analytical variance in HPLC-ECD studies of the human plasma metabolome

Aging and "Age-Related" Diseases - What Is the Relation?

The study explores the intricate relationship between aging and the development of noncommunicable diseases [NCDs], focusing on whether these diseases are inevitable consequences of aging or primarily driven by lifestyle factors. By examining epidemiological data, particularly from hunter-gatherer societies, the study highlights that many NCDs prevalent in modern populations are rare in these societies, suggesting a significant influence of lifestyle choices. It delves into the mechanisms through which poor diet, smoking, and other lifestyle factors contribute to systemic physiological imbalances, characterized by oxidative stress, insulin resistance and hyperinsulinemia, and dysregulation of the sympathetic nervous system, the renin-angiotensin-aldosterone system, and the immune system. The interplay between this pattern and individual factors such as genetic susceptibility, biological variability, epigenetic changes and the microbiome is proposed to play a crucial role in the development of a range of age-related NCDs. Modified biomolecules such as oxysterols and advanced glycation end products also contribute to their development. Specific diseases such as benign prostatic hyperplasia, Parkinson's disease, glaucoma and osteoarthritis are analyzed to illustrate these mechanisms. The study concludes that while aging contributes to the risk of NCDs, lifestyle factors play a crucial role, offering potential avenues for prevention and intervention through healthier living practices. One possible approach could be to try to restore the physiological balance, e.g. through dietary measures [e.g. Mediterranean diet, Okinawan diet or Paleolithic diet] in conjunction with [a combination of] pharmacological interventions and other lifestyle changes.

Energy Metabolites and Indicative Significance of α-Ketoglutarate and α-Ketoglutaramate in Assessing the Progression of Chronic Hepatoencephalopathy

In the example of a rat model with chronic hepatoencephalopathy (HE), changes in the organ morphology of rats affect the balance of metabolites of the tricarboxylic acid (TCA) cycle and metabolites of the glutamine-glutamate (Gln-Glu) cycle, namely α-ketoglutarate (αKG) and α-ketoglutaramate (αKGM), as well as the enzymes associated with them, ω-amidase (ωA) and glutamine transaminase (GTK). This model of rats was obtained as a result of 2-22 weeks of consumption by animals of hepatotoxin thioacetamide (TAA) added to drinking water at a concentration of 0.4 g/L. The control (n = 26) and TAA-induced (n = 55) groups of rats consisted of 11 cohorts each. The control cohorts consisted of 2-4 rats, and the TAA-induced cohorts consisted of 4-7 individuals. Every two weeks, samples of blood plasma, liver, kidney, and brain tissues were taken from the next cohort of rats (a total of 320 samples). By the end of the experiment, irreversible morphological changes were observed in the organs of rats: the weight of the animals was reduced up to ~45%, the weight of the kidneys up to 5%, the brain up to ~20%, and the weight of the liver increased up to ~20%. The analysis revealed: (i) a decrease in the activity of ωA and GTK in the tissues of the brain, kidneys, and liver of rats with chronic HE (by ~3, 40, and 65% and ~10, 60, and 70%, respectively); and (ii) the appearance of a significant imbalance in the content of metabolites of the Gln-Glu cycle, αKG, and αKGM. It is indicative that a ~1.5-12-fold increase in the level of αKG in the blood plasma and tissues of the organs of rats with chronic HE was accompanied by a synchronous, ~1.2-2.5-fold decrease in the level of αKGM. The data obtained indicate an essential involvement of the Gln-Glu cycle in the regulation of energy metabolism in rats under conditions of chronic HE. Attention is focused on the significance of the αKG/αKGM ratio, which can act as a potential marker for diagnosing the degree of HE development.

Metabolomics in Preclinical Drug Safety Assessment: Current Status and Future Trends

Metabolomics is emerging as a powerful systems biology approach for improving preclinical drug safety assessment. This review discusses current applications and future trends of metabolomics in toxicology and drug development. Metabolomics can elucidate adverse outcome pathways by detecting endogenous biochemical alterations underlying toxicity mechanisms. Furthermore, metabolomics enables better characterization of human environmental exposures and their influence on disease pathogenesis. Metabolomics approaches are being increasingly incorporated into toxicology studies and safety pharmacology evaluations to gain mechanistic insights and identify early biomarkers of toxicity. However, realizing the full potential of metabolomics in regulatory decision making requires a robust demonstration of reliability through quality assurance practices, reference materials, and interlaboratory studies. Overall, metabolomics shows great promise in strengthening the mechanistic understanding of toxicity, enhancing routine safety screening, and transforming exposure and risk assessment paradigms. Integration of metabolomics with computational, in vitro, and personalized medicine innovations will shape future applications in predictive toxicology.

Tricarboxylic Acid Metabolite Imbalance in Rats with Acute Thioacetamide-Induced Hepatic Encephalopathy Indicates Incomplete Recovery

Exposure to the toxin thioacetamide (TAA) causes acute hepatic encephalopathy (HE), changes in the functioning of systemic organs, and an imbalance in a number of energy metabolites. The deferred effects after acute HE development are poorly understood. The study considers the balance of the tricarboxylic acid (TCA) cycle metabolites in the blood plasma, liver, kidneys, and brain tissues of rats in the post-rehabilitation period. The samples of the control (n = 3) and TAA-induced groups of rats (n = 13) were collected six days after the administration of a single intraperitoneal TAA injection at doses of 200, 400, and 600 mg/kg. Despite the complete physiological recovery of rats by this date, a residual imbalance of metabolites in all the vital organs was noted. The results obtained showed a trend of stabilizing processes in the main organs of the animals and permit the use of these data both for prognostic purposes and the choice of potential therapeutic agents.

Chromatomass-Spectrometric Method for the Quantitative Determination of Amino- and Carboxylic Acids in Biological Samples

A highly sensitive method for the qualitative and quantitative determination of amino- and carboxylic acids, as well as a number of urea and methionine cycle metabolites in the studied solutions, is presented. Derivatives (esterification) were obtained for amino acids by their reaction in a solution of 3 N of hydrochloric acid in n-butanol for 15 min at 65 °C and for carboxylic acids by their reaction with phenol in ethyl acetate with 3 N of hydrochloric acid for 20 min at 65 °C. Experimental work on the determination of individual metabolites was carried out using the HPLC-MS/MS method and included the creation of a library of spectra of the analyzed compounds and their quantitative determination. Multiplex methods have been developed for the quantitative analysis of the desired metabolites in a wide range of concentrations of 3-4 orders of magnitude. The approach to the analysis of metabolites was developed based on the method of the dynamic monitoring of multiple reactions of the formation of fragments for a mass analyzer with a triple quadrupole (QQQ). The effective chromatographic separation of endogenous metabolites was carried out within 13 min. The calibration curves of the analyzed compounds were stable throughout the concentration range and had the potential to fit below empirical levels. The developed methods and obtained experimental data are of interest for a wide range of biomedical studies, as well as for monitoring the content of endogenous metabolites in biological samples under various pathological conditions. The sensitivity limit of the methods for amino acids was about 4.8 nM and about 0.5 μM for carboxylic acids. Up to 19 amino- and up to 12 carboxy acids and about 10 related metabolites can be tested in a single sample.

From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids

The stability of lipids and other metabolites in human body fluids ranges from very stable over several days to very unstable within minutes after sample collection. Since the high-resolution analytics of metabolomics and lipidomics approaches comprise all these compounds, the handling of body fluid samples, and thus the pre-analytical phase, is of utmost importance to obtain valid profiling data. This phase consists of two parts, sample collection in the hospital (“bedside”) and sample processing in the laboratory (“bench”). For sample quality, the apparently simple steps in the hospital are much more critical than the “bench” side handling, where (bio)analytical chemists focus on highly standardized processing for high-resolution analysis under well-controlled conditions. This review discusses the most critical pre-analytical steps for sample quality from patient preparation; collection of body fluids (blood, urine, cerebrospinal fluid) to sample handling, transport, and storage in freezers; and subsequent thawing using current literature, as well as own investigations and practical experiences in the hospital. Furthermore, it provides guidance for (bio)analytical chemists to detect and prevent potential pre-analytical pitfalls at the “bedside,” and how to assess the quality of already collected body fluid samples. A knowledge base is provided allowing one to decide whether or not the sample quality is acceptable for its intended use in distinct profiling approaches and to select the most suitable samples for high-resolution metabolomics and lipidomics investigations.

High throughput pSTAT signaling profiling by fluorescent cell barcoding and computational analysis

Fluorescent cell barcoding (FCB) is a multiplexing technique for high-throughput flow cytometry (FCM). Although powerful in minimizing staining variability, it remains a subjective FCM technique because of inter-operator variability and differences in data analysis. FCB was implemented by combining two-dye barcoding (DyLight 350 plus Pacific Orange) with five-color surface marker antibody and intracellular staining for phosphoprotein signaling analysis. We proposed a robust method to measure intra- and inter-assay variability of FCB in T/B cells and monocytes by combining range and ratio of variability to standard statistical analyses. Data analysis was carried out by conventional and semi-automated workflows and built with R software. Results obtained from both analyses were compared to assess feasibility and reproducibility of FCB data analysis by machine-learning methods. Our results showed efficient FCB using DyLight 350 and Pacific Orange at concentrations of 0, 15 or 30, and 250 μg/mL, and a high reproducibility of FCB in combination with surface marker and intracellular antibodies. Inter-operator variability was minimized by adding an internal control bridged across matrices used as rejection criterion if significant differences were present between runs. Computational workflows showed comparable results to conventional gating strategies. FCB can be used to study phosphoprotein signaling in T/B cells and monocytes with high reproducibility across operators, and the addition of bridge internal controls can further minimize inter-operator variability. This FCB protocol, which has high throughput analysis and low intra- and inter-assay variability, can be a powerful tool for clinical trial studies. Moreover, FCB data can be reliably analyzed using computational software.

Reproducibility of non-fasting plasma metabolomics measurements across processing delays

INTRODUCTION

Processing delays after blood collection is a common pre-analytical condition in large epidemiologic studies. It is critical to evaluate the suitability of blood samples with processing delays for metabolomics analysis as it is a potential source of variation that could attenuate associations between metabolites and disease outcomes.

OBJECTIVES

We aimed to evaluate the reproducibility of metabolites over extended processing delays up to 48 h. We also aimed to test the reproducibility of the metabolomics platform.

METHODS

Blood samples were collected from 18 healthy volunteers. Blood was stored in the refrigerator and processed for plasma at 0, 15, 30, and 48 h after collection. Plasma samples were metabolically profiled using an untargeted, ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. Reproducibility of 1012 metabolites over processing delays and reproducibility of the platform were determined by intraclass correlation coefficients (ICCs) with variance components estimated from mixed-effects models.

RESULTS

The majority of metabolites (approximately 70% of 1012) were highly reproducible (ICCs ≥ 0.75) over 15-, 30- or 48-h processing delays. Nucleotides, energy-related metabolites, peptides, and carbohydrates were most affected by processing delays. The platform was highly reproducible with a median technical ICC of 0.84 (interquartile range 0.68-0.93).

CONCLUSION

Most metabolites measured by the UPLC-MS/MS platform show acceptable reproducibility up to 48-h processing delays. Metabolites of certain pathways need to be interpreted cautiously in relation to outcomes in epidemiologic studies with prolonged processing delays.

Stability of energy metabolites-An often overlooked issue in metabolomics studies: A review

Recent advances in analytical chemistry have set the stage for metabolite profiling to help understand complex molecular processes in physiology. Despite ongoing efforts, there are concerns regarding metabolomics workflows, since it has been shown that internal (enzyme activity, blood contamination, and the dynamic nature of metabolite concentrations) as well as external factors (storage, handling, and analysis method) may affect the metabolome profile. Many metabolites are intrinsically instable, particularly some of those associated with central carbon metabolism. While enzymatic conversions have been studied in great detail, nonenzymatic, chemical conversions received comparatively little attention. This review aims to give an in-depth overview of nonenzymatic energy metabolite degradation/interconversion chemistry focusing on a selected range of metabolites. Special attention will be given to qualitative (degradation pathways) as well as quantitative aspects, that may affect the acquisition of accurate data in the context of metabolomics studies. Problems related to the use of isotopically labeled internal standards hindering the quantitative analysis of common metabolites will be presented with an experimental example. Finally, general conclusions and perspectives are given.

A systems-level "misunderstanding": the plasma metabolome in Huntington's disease

Objective

Huntington’s disease (HD) is a rare neurodegenerative disease caused by the expansion of an N-terminal repeat in the huntingtin protein. The protein is expressed in all cells in the body; hence, peripheral tissues, such as blood, may recapitulate processes in the brain. The plasma metabolome may provide a window into active processes that influence brain health and a unique opportunity to noninvasively identify processes that may contribute to neurodegeneration. Alterations in metabolic pathways in brain have been shown to profoundly impact HD. Therefore, identification and quantification of critical metabolomic perturbations could provide novel biomarkers for disease onset and disease progression.

Methods

We analyzed the plasma metabolomic profiles from 52 premanifest (PHD), 102 early symptomatic HD, and 140 healthy controls (NC) using liquid chromatography coupled with a highly sensitive electrochemical detection platform.

Results

Alterations in tryptophan, tyrosine, purine, and antioxidant pathways were identified, including many related to energetic and oxidative stress and derived from the gut microbiome. Multivariate statistical modeling demonstrated mutually distinct metabolomic profiles, suggesting that the processes that determine onset were likely distinct from those that determine progression. Gut microbiome-derived metabolites particularly differentiated the PHD metabolome, while the symptomatic HD metabolome was increasingly influenced by metabolites that may reflect mutant huntingtin toxicity and neurodegeneration.

Interpretation

Understanding the complex changes in the delicate balance of the metabolome and the gut microbiome in HD, and how they relate to disease onset, progression, and phenotypic variability in HD are critical questions for future research.

Identification of metabolites from liquid chromatography-coulometric array detection profiling: gas chromatography-mass spectrometry and refractionation provide essential information orthogonal to LC-MS/microNMR

Liquid chromatography-coulometric array detection (LC-EC) is a sensitive, quantitative, and robust metabolomics profiling tool that complements the commonly used mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based approaches. However, LC-EC provides little structural information. We recently demonstrated a workflow for the structural characterization of metabolites detected by LC-EC profiling combined with LC-electrospray ionization (ESI)-MS and microNMR. This methodology is now extended to include (i) gas chromatography (GC)-electron ionization (EI)-MS analysis to fill structural gaps left by LC-ESI-MS and NMR and (ii) secondary fractionation of LC-collected fractions containing multiple coeluting analytes. GC-EI-MS spectra have more informative fragment ions that are reproducible for database searches. Secondary fractionation provides enhanced metabolite characterization by reducing spectral overlap in NMR and ion suppression in LC-ESI-MS. The need for these additional methods in the analysis of the broad chemical classes and concentration ranges found in plasma is illustrated with discussion of four specific examples: (i) characterization of compounds for which one or more of the detectors is insensitive (e.g., positional isomers in LC-MS, the direct detection of carboxylic groups and sulfonic groups in (1)H NMR, or nonvolatile species in GC-MS), (ii) detection of labile compounds, (iii) resolution of closely eluting and/or coeluting compounds, and (iv) the capability to harness structural similarities common in many biologically related, LC-EC-detectable compounds.

Chemical and technical challenges in the analysis of central carbon metabolites by liquid-chromatography mass spectrometry

This review deals with chemical and technical challenges in the analysis of small-molecule metabolites involved in central carbon and energy metabolism via liquid-chromatography mass-spectrometry (LC-MS). The covered analytes belong to the prominent pathways in biochemical carbon oxidation such as glycolysis or the tricarboxylic acid cycle and, for the most part, share unfavorable properties such as a high polarity, chemical instability or metal-affinity. The topic is introduced by selected examples on successful applications of metabolomics in the clinic. In the core part of the paper, the structural features of important analyte classes such as nucleotides, coenzyme A thioesters or carboxylic acids are linked to "problematic hotspots" along the analytical chain (sample preparation and-storage, separation and detection). We discuss these hotspots from a chemical point of view, covering issues such as analyte degradation or interactions with metals and other matrix components. Based on this understanding we propose solutions wherever available. A major notion derived from these considerations is that comprehensive carbon metabolomics inevitably requires multiple, complementary analytical approaches covering different chemical classes of metabolites.

Reproducibility of metabolomic profiles among men and women in 2 large cohort studies

BACKGROUND

Rigorous studies are necessary to demonstrate suitability of metabolomics platforms to profile metabolites in archived plasma within epidemiologic studies of human disease, for which attenuation of effect estimates due to measurement error is a key concern.

METHODS

Using a liquid chromatography-tandem mass spectrometry platform, we quantified 257 metabolites from archived plasma to evaluate metabolite interassay reproducibility, reproducibility with delayed processing, and within-person reproducibility over time. Interassay reproducibility was assessed with CVs from 60 duplicate plasma samples donated by participants in the Nurses' Health Study and Health Professionals Follow-up Study, and 20 QC pool plasma replicates. Metabolite reproducibility over a 24- to 48-h processing delay (n = 48 samples) and within-person reproducibility over 1-2 years (n = 80 samples) were assessed using Spearman and intraclass correlation coefficients (ICCs).

RESULTS

CVs were <20% for 92% of metabolites and generally were similar by plasma anticoagulant type (heparin or EDTA) and fasting time. Approximately 75% of metabolites were reproducible over delays in processing of blood samples (Spearman correlation or ICC ≥ 0.75, comparing immediate and 24-h delayed processing). Carbohydrates and purine/pyrimidine derivatives were most adversely affected by the processing delay. Ninety percent of metabolites were reproducible over 1-2 years within individuals (Spearman correlation or ICC ≥ 0.4).

CONCLUSIONS

For potential use in epidemiologic studies, the majority of plasma metabolites had low CVs and were reproducible over a 24-h processing delay and within individuals over 1-2 years. Certain metabolites, such as carbohydrates and purine/pyrimidine derivatives, may be challenging to evaluate if samples have delayed processing.

Preanalytical aspects and sample quality assessment in metabolomics studies of human blood

BACKGROUND

Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies.

METHODS

We systematically investigated the effects of preanalytical variables (blood collection tubes, hemolysis, temperature and time before further processing, and number of freeze-thaw cycles) on metabolomics studies of clinical blood and plasma samples using a nontargeted LC-MS approach.

RESULTS

Serum and heparinate blood collection tubes led to chemical noise in the mass spectra. Distinct, significant changes of 64 features in the EDTA-plasma metabolome were detected when blood was exposed to room temperature for 2, 4, 8, and 24 h. The resulting pattern was characterized by increases in hypoxanthine and sphingosine 1-phosphate (800% and 380%, respectively, at 2 h). In contrast, the plasma metabolome was stable for up to 4 h when EDTA blood samples were immediately placed in iced water. Hemolysis also caused numerous changes in the metabolic profile. Unexpectedly, up to 4 freeze-thaw cycles only slightly changed the EDTA-plasma metabolome, but increased the individual variability.

CONCLUSIONS

Nontargeted metabolomics investigations led to the following recommendations for the preanalytical phase: test the blood collection tubes, avoid hemolysis, place whole blood immediately in ice water, use EDTA plasma, and preferably use nonrefrozen biobank samples. To exclude outliers due to preanalytical errors, inspect the biomarker signal intensities reflecting systematic as well as accidental and preanalytical inaccuracies before processing the bioinformatics data.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Structural characterization of plasma metabolites detected via LC-electrochemical coulometric array using LC-UV fractionation, MS, and NMR

Liquid chromatography (LC) separation combined with electrochemical coulometric array detection (EC) is a sensitive, reproducible, and robust technique that can detect hundreds of redox-active metabolites down to the level of femtograms on column, making it ideal for metabolomics profiling. EC detection cannot, however, structurally characterize unknown metabolites that comprise these profiles. Several aspects of LC-EC methods prevent a direct transfer to other structurally informative analytical methods, such as LC-MS and NMR. These include system limits of detection, buffer requirements, and detection mechanisms. To address these limitations, we developed a workflow based on the concentration of plasma, metabolite extraction, and offline LC-UV fractionation. Pooled human plasma was used to provide sufficient material necessary for multiple sample concentrations and platform analyses. Offline parallel LC-EC and LC-MS methods were established that correlated standard metabolites between the LC-EC profiling method and the mass spectrometer. Peak retention times (RT) from the LC-MS and LC-EC system were linearly related (r(2) = 0.99); thus, LC-MS RTs could be directly predicted from the LC-EC signals. Subsequent offline microcoil-NMR analysis of these collected fractions was used to confirm LC-MS characterizations by providing complementary, structural data. This work provides a validated workflow that is transferrable across multiple platforms and provides the unambiguous structural identifications necessary to move primary mathematically driven LC-EC biomarker discovery into biological and clinical utility.

The human serum metabolome

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.

Symposium 2: Modern approaches to nutritional research challenges: Targeted and non-targeted approaches for metabolite profiling in nutritional research

The present report discusses targeted and non-targeted approaches to monitor single nutrients and global metabolite profiles in nutritional research. Non-targeted approaches such as metabolomics allow for the global description of metabolites in a biological sample and combine an analytical platform with multivariate data analysis to visualise patterns between sample groups. In nutritional research metabolomics has generated much interest as it has the potential to identify changes to metabolic pathways induced by diet or single nutrients, to explore relationships between diet and disease and to discover biomarkers of diet and disease. Although still in its infancy, a number of studies applying this technology have been performed; for example, the first study in 2003 investigated isoflavone metabolism in females, while the most recent study has demonstrated changes to various metabolic pathways during a glucose tolerance test. As a relatively new technology metabolomics is faced with a number of limitations and challenges including the standardisation of study design and methodology and the need for careful consideration of data analysis, interpretation and identification. Targeted approaches are used to monitor single or multiple nutrient and/or metabolite status to obtain information on concentration, absorption, distribution, metabolism and elimination. Such applications are currently widespread in nutritional research and one example, using stable isotopes to monitor nutrient status, is discussed in more detail. These applications represent innovative approaches in nutritional research to investigate the role of both single nutrients and diet in health and disease.

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.

Metabolomics tools for identifying biomarkers for neuropsychiatric diseases

The repertoire of biochemicals (or small molecules) present in cells, tissue, and body fluids is known as the metabolome. Today, clinicians utilize only a very small part of the information contained in the metabolome, as revealed by the quantification of a limited set of analytes to gain information on human health. Examples include measuring glucose or cholesterol to monitor diabetes and cardiovascular health, respectively. With a focus on comprehensively studying the metabolome, the rapidly growing field of metabolomics captures the metabolic state of organisms at the global or "-omics" level. Given that the overall health status of an individual is captured by his or her metabolic state, which is a reflection of what has been encoded by the genome and modified by environmental factors, metabolomics has the potential to have a great impact upon medical practice by providing a wealth of relevant biochemical data. Metabolomics promises to improve current, single metabolites-based clinical assessments by identifying metabolic signatures (biomarkers) that embody global biochemical changes in disease, predict responses to treatment or medication side effects (pharmachometabolomics). State of the art metabolomic analytical platforms and informatics tools are being used to map potential biomarkers for a multitude of disorders including those of the central nervous system (CNS). Indeed, CNS disorders are linked to disturbances in metabolic pathways related to neurotransmitter systems (dopamine, serotonin, GABA and glutamate); fatty acids such as arachidonic acid-cascade; oxidative stress and mitochondrial function. Metabolomics tools are enabling us to map in greater detail perturbations in many biochemical pathways and links among these pathways this information is key for development of biomarkers that are disease-specific. In this review, we elaborate on some of the concepts and technologies used in metabolomics and its promise for biomarker discovery. We also highlight early findings from metabolomic studies in CNS disorders such as schizophrenia, Major Depressive Disorder (MDD), Bipolar Disorder (BD), Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD).

Metabolite identification using a nanoelectrospray LC-EC-array-MS integrated system

A novel approach to the parallel coupling of normal-bore high-performance liquid chromatography (LC) with electrochemical-array detection (EC-array) and nanoelectrospray mass spectrometry (MS), based on the use of a nanosplitting interface, is described where both detectors are utilized at their optimal detection mode for parallel configuration. The dual detection platform was shown to maintain full chromatographic integrity with retention times and peak widths at half-height between the EC-array and MS displaying high reproducibility with relative standard deviations of <2%. Detection compatibility between the two detectors at the part per billion level injected on-column was demonstrated using selected metabolites representative of the diversity typically encountered in physiological systems. Metabolites were detected with equal efficiency whether neat or in serum, demonstrating the system's ability to handle biological samples with limited sample cleanup and reduced concern for biological matrix effects. Direct quantification of known analytes from the EC-array signal using Faraday's law can eliminate the need for isotopically labeled internal standards. The system was successfully applied to the detection and characterization of metabolites of phenylbutyrate from serum samples of Huntington's disease patients in an example that illustrates the complementarity of the dual detection nanoelectrospray LC-EC-array-MS system.