Challenges and developments in tandem mass spectrometry based clinical metabolomics

Clinical metabolomics: Useful insights, perspectives and challenges

Metabolomics, a cutting-edge omics technique, is a rapidly advancing field in biomedical research, concentrating on the elucidation of pathogenetic mechanisms and the discovery of novel metabolite signatures predictive of disease risk, aiding in earlier disease detection, prognosis and prediction of treatment response. The capacity of this omics approach to simultaneously quantify thousands of metabolites, i.e. small molecules less than 1500 Da in samples, positions it as a promising tool for research and clinical applications in personalized medicine. Clinical metabolomics studies have proven valuable in understanding cardiometabolic disorders, potentially uncovering diagnostic biomarkers predictive of disease risk. Liquid chromatography-mass spectrometry is the predominant analytical method used in metabolomics, particularly untargeted. Metabolomics combined with extensive genomic data, proteomics, clinical chemistry data, imaging, health records, and other pertinent health-related data may yield significant advances beneficial for both public health initiatives, clinical applications and precision medicine, particularly in rare disorders and multimorbidity. This special issue has gathered original research articles in topics related to clinical metabolomics as well as research articles, reviews, perspectives and highlights in the broader field of translational and clinical metabolic research. Additional research is necessary to identify which metabolites consistently enhance clinical risk prediction across various populations and are causally linked to disease progression.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

An atlas of genome-wide gene expression and metabolite associations and possible mediation effects towards body mass index

Investigating the cross talk of different omics layers is crucial to understand molecular pathomechanisms of metabolic diseases like obesity. Here, we present a large-scale association meta-analysis of genome-wide whole blood and peripheral blood mononuclear cell (PBMC) gene expressions profiled with Illumina HT12v4 microarrays and metabolite measurements from dried blood spots (DBS) characterized by targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) in three large German cohort studies with up to 7706 samples. We found 37,295 associations comprising 72 amino acids (AA) and acylcarnitine (AC) metabolites (including ratios) and 8579 transcripts. We applied this catalogue of associations to investigate the impact of associating transcript-metabolite pairs on body mass index (BMI) as an example metabolic trait. This is achieved by conducting a comprehensive mediation analysis considering metabolites as mediators of gene expression effects and vice versa. We discovered large mediation networks comprising 27,023 potential mediation effects within 20,507 transcript-metabolite pairs. Resulting networks of highly connected (hub) transcripts and metabolites were leveraged to gain mechanistic insights into metabolic signaling pathways. In conclusion, here, we present the largest available multi-omics integration of genome-wide transcriptome data and metabolite data of amino acid and fatty acid metabolism and further leverage these findings to characterize potential mediation effects towards BMI proposing candidate mechanisms of obesity and related metabolic diseases. KEY MESSAGES: Thousands of associations of 72 amino acid and acylcarnitine metabolites and 8579 genes expand the knowledge of metabolome-transcriptome associations. A mediation analysis of effects on body mass index revealed large mediation networks of thousands of obesity-related gene-metabolite pairs. Highly connected, potentially mediating hub genes and metabolites enabled insight into obesity and related metabolic disease pathomechanisms.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

A Streamlined High-Throughput Plasma Proteomics Platform for Clinical Proteomics with Improved Proteome Coverage, Reproducibility, and Robustness

Mass spectrometry-based clinical proteomics requires high throughput, reproducibility, robustness, and comprehensive coverage to serve the needs of clinical diagnosis, prognosis, and personalized medicine. Oftentimes these requirements are contradictory to each other. We report the development of a streamlined High-Throughput Plasma Proteomics (sHTPP) platform for untargeted profiling of the blood plasma proteome, which includes 96-well plates and simplified procedures for sample preparation, disposable trap column for peptide loading, robust liquid chromatographic system for separation, data-independent acquisition in tandem mass spectrometry, and DIA-NN, FragPipe, and in-house peptide spectral library-based data analysis. Using the optimized platform at a throughput of 60 samples per day, over 600 protein groups including 57 FDA-approved biomarkers can be consistently identified from whole human plasma, and more than 85% of the detected proteins have 100% completeness in quantitative values across 300 samples. The balance achieved between proteome coverage, throughput, and reproducibility of this sHTPP platform makes it promising in clinical settings, where a large number of samples are to be measured quickly and reliably to support various needs of clinical medicine.

Application of Metabolomics in Fungal Research

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

Advances and Trends in Omics Technology Development

The human history has witnessed the rapid development of technologies such as high-throughput sequencing and mass spectrometry that led to the concept of “omics” and methodological advancement in systematically interrogating a cellular system. Yet, the ever-growing types of molecules and regulatory mechanisms being discovered have been persistently transforming our understandings on the cellular machinery. This renders cell omics seemingly, like the universe, expand with no limit and our goal toward the complete harness of the cellular system merely impossible. Therefore, it is imperative to review what has been done and is being done to predict what can be done toward the translation of omics information to disease control with minimal cell perturbation. With a focus on the “four big omics,” i.e., genomics, transcriptomics, proteomics, metabolomics, we delineate hierarchies of these omics together with their epiomics and interactomics, and review technologies developed for interrogation. We predict, among others, redoxomics as an emerging omics layer that views cell decision toward the physiological or pathological state as a fine-tuned redox balance.

Whole Blood Metabolite Profiles Reflect Changes in Energy Metabolism in Heart Failure

A variety of atherosclerosis and cardiovascular disease (ASCVD) phenotypes are tightly linked to changes in the cardiac energy metabolism that can lead to a loss of metabolic flexibility and to unfavorable clinical outcomes. We conducted an association analysis of 31 ASCVD phenotypes and 97 whole blood amino acids, acylcarnitines and derived ratios in the LIFE-Adult (n = 9646) and LIFE-Heart (n = 5860) studies, respectively. In addition to hundreds of significant associations, a total of 62 associations of six phenotypes were found in both studies. Positive associations of various amino acids and a range of acylcarnitines with decreasing cardiovascular health indicate disruptions in mitochondrial, as well as peroxisomal fatty acid oxidation. We complemented our metabolite association analyses with whole blood and peripheral blood mononuclear cell (PBMC) gene-expression analyses of fatty acid oxidation and ketone-body metabolism related genes. This revealed several differential expressions for the heart failure biomarker N-terminal prohormone of brain natriuretic peptide (NT-proBNP) in peripheral blood mononuclear cell (PBMC) gene expression. Finally, we constructed and compared three prediction models of significant stenosis in the LIFE-Heart study using (1) traditional risk factors only, (2) the metabolite panel only and (3) a combined model. Area under the receiver operating characteristic curve (AUC) comparison of these three models shows an improved prediction accuracy for the combined metabolite and classical risk factor model (AUC = 0.78, 95%-CI: 0.76–0.80). In conclusion, we improved our understanding of metabolic implications of ASCVD phenotypes by observing associations with metabolite concentrations and gene expression of the mitochondrial and peroxisomal fatty acid oxidation. Additionally, we demonstrated the predictive potential of the metabolite profile to improve classification of patients with significant stenosis.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Systematic Feature Filtering in Exploratory Metabolomics: Application toward Biomarker Discovery

Exploratory mass spectrometry-based metabolomics generates a plethora of features in a single analysis. However, >85% of detected features are typically false positives due to inefficient elimination of chimeric signals and chemical noise not relevant for biological and clinical data interpretation. The data processing is considered a bottleneck to unravel the translational potential in metabolomics. Here, we describe a systematic workflow to refine exploratory metabolomics data and reduce reported false positives. We applied the feature filtering workflow in a case/control study exploring common variable immunodeficiency (CVID). In the first stage, features were detected from raw liquid chromatography-mass spectrometry data by XCMS Online processing, blank subtraction, and reproducibility assessment. Detected features were annotated in metabolomics databases to produce a list of tentative identifications. We scrutinized tentative identifications' physicochemical properties, comparing predicted and experimental reversed-phase liquid chromatography (LC) retention time. A prediction model used a linear regression of 42 retention indices with the cLogP ranging from -6 to 11. The LC retention time probes the physicochemical properties and effectively reduces the number of tentatively identified metabolites, which are further submitted to statistical analysis. We applied the retention time-based analytical feature filtering workflow to datasets from the Metabolomics Workbench (www.metabolomicsworkbench.org), demonstrating the broad applicability. A subset of tentatively identified metabolites significantly different in CVID patients was validated by MS/MS acquisition to confirm potential CVID biomarkers' structures and virtually eliminate false positives. Our exploratory metabolomics data processing workflow effectively removes false positives caused by the chemical background and chimeric signals inherent to the analytical technique. It reduced the number of tentatively identified metabolites by 88%, from initially detected 6940 features in XCMS to 839 tentative identifications and streamlined consequent statistical analysis and data interpretation.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Plasma Targeted Metabolomics Analysis for Amino Acids and Acylcarnitines in Patients with Prediabetes, Type 2 Diabetes Mellitus, and Diabetic Vascular Complications

BACKGROUND

We hypothesized that specific amino acids or acylcarnitines would have benefits for the differential diagnosis of diabetes. Thus, a targeted metabolomics for amino acids and acylcarnitines in patients with diabetes and its complications was carried out.

METHODS

A cohort of 54 normal individuals and 156 patients with type 2 diabetes mellitus and/or diabetic complications enrolled from the First Affiliated Hospital of Jinzhou Medical University was studied. The subjects were divided into five main groups: normal individuals, impaired fasting glucose, overt diabetes, diabetic microvascular complications, and diabetic peripheral vascular disease. The technique of tandem mass spectrometry was applied to obtain the plasma metabolite profiles. Metabolomics multivariate statistics were applied for the metabolic data analysis and the differential metabolites determination.

RESULTS

A total of 10 cross-comparisons within diabetes and its complications were designed to explore the differential metabolites. The results demonstrated that eight comparisons existed and yielded significant metabolic differences. A total number of 24 differential metabolites were determined from six selected comparisons, including up-regulated amino acids, down-regulated medium-chain and long-chain acylcarnitines. Altered differential metabolites provided six panels of biomarkers, which were helpful in distinguishing diabetic patients.

CONCLUSION

Our results demonstrated that the biomarker panels consisted of specific amino acids and acylcarnitines which could reflect the metabolic variations among the different stages of diabetes and might be useful for the differential diagnosis of prediabetes, overt diabetes and diabetic complications.

Signatures and Clinical Significance of Amino Acid Flux in Sarcopenia: A Systematic Review and Meta-Analysis

BACKGROUND

Dysregulation of amino acids is closely linked to the initiation and progression of sarcopenia. We summarized recent advancements in the studies of amino acid profiles in sarcopenia and systematically presented the clinical significance of amino acid flux in sarcopenia.

METHODS

We systematically searched in MEDLINE, EMBASE, and Cochrane library from inception to June 1, 2021 to capture all studies examining metabolomics of sarcopenia. We used the following keywords: sarcopenia, metabonomics, metabolomics, amino acid profile, and mass spectrometry. Original articles comparing amino acid patterns between persons with and without sarcopenia were included. Two independent investigators independently completed title and abstract screening, data extraction, and quality evaluation. We used a random effects model to examine the association between amino acids levels and sarcopenia. Sensitivity analyses restricted the analyses to studies in which muscle mass was measured by bioelectrical impedance analysis. Study quality was evaluated according to the Agency for Healthcare Research and Quality (AHRQ) checklist.

RESULTS

The systematic research yielded six eligible articles, comprising 1,120 participants. Five studies used muscle mass in combination with physical performance and/or muscle strength as the criteria to diagnose sarcopenia, while one study used muscle mass as a diagnostic criterion alone. We found that the concentrations of branched-chain amino acids leucine (standardized mean difference [SMD] -1.249; 95% confidence interval [CI]: -2.275, -0.223, P = 0.02, I² = 97.7%), isoleucine (SMD -1.077; 95% CI: -2.106, -0.049, P = 0.04, I² = 97.8%), and aromatic amino acid tryptophan (SMD -0.923; 95% CI: -1.580, -0.265, P = 0.01, I² = 89.9%) were significantly reduced in individuals with sarcopenia. Study results were robust in sensitivity analysis.

CONCLUSIONS

The homeostasis of amino acids is critical to maintaining muscle health. The profiles of amino acids might be useful biomarkers for the characterization of sarcopenia. Future studies are warranted to study the clinical significance of amino acids in the diagnosis and treatment of sarcopenia.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Relationship between fermented dairy consumption, circulating short-chain acylcarnitines and angiographic severity of coronary artery disease

BACKGROUND AND AIMS

Current epidemiologic data suggest beneficial cardiovascular effects of fermented dairy products (FDP). However, the relationship between FDP consumption and angiographic coronary status has not been previously studied. Furthermore, the role of novel metabolomic biomarkers of cardiovascular risk in this context is unclear. We hypothesize that short-chain acylcarnitines (SCA) reflect the link between FDP intake and angiographic extent of stable coronary artery disease (CAD).

METHODS AND RESULTS

We recruited 1185 patients admitted for suspected CAD [median age 62 years (interquartile range: 54-69); 714 men (60.3%)]. Prior to coronary angiography, each patient completed a validated Food Frequency Questionnaire. In addition, venous blood was collected from each patient for whole blood metabolomic analysis, using targeted mass-spectrometry. CAD was defined by the presence of ≥1 coronary stenosis ≥50%. Patients with CAD (n = 441) reported lower median FDP intake [86.8 g/day (IQR: 53.4-127.6)] than patients without CAD [n = 744; 103.9 g/day (IQR: 62.9-152.7); p < 0.001]. Upon adjustment for relevant confounders, increased circulating SCA, particularly level of acetylcarnitine (C2) associated with both higher CAD probability [SCA:β(SE) = 0.584 (0.235), p = 0.013; C2:β(SE) = 0.575 (0.242), p = 0.017] and decreased FDP consumption [SCA:β/100 g FDP-increment/day (SE) = -0.785 (0.242), p = 0.001; C2:β(SE) = -0.560 (0.230), p = 0.015]. By mediation analysis, neither SCA nor C2 showed relevant mediator effect linking FDP consumption to the risk of CAD.

CONCLUSION

Increased consumption of fermented milk was associated with lower prevalence of CAD and correlated inversely with circulating SCA, in particular with acetylcarnitine. No substantial mediator effect of SCA linking fermented milk intake with risk of CAD was found.

CLINICAL TRIAL REGISTRY

NCT00497887.

Analysis of Tryptophan Metabolites in Serum Using Wide-Isolation Strategies for UHPLC-HRMS/MS

Current targeted metabolomic workflows are limited by design and thus sacrifice crucial information from a profiling standpoint that could lead to a more fundamental understanding of the metabolic processes of interest. One drawback to performing targeted analysis on ion trapping instruments is the potential for increased variability in analysis when analytes and standards are isolated and trapped individually for fragmentation. In addition, this sequential isolation process increases the duty cycle of the mass spectrometer and reduces the number of points collected across a chromatographic peak. To address this, the use of a wide-isolation window (12 Da) to encompass the target analyte and the isotope standard within a single fragmentation window ensures that fragmentation is consistent when quantitation relies on the ratio of the target to the internal standard. Additionally, the preservation of a faster scan rate ensures that optimal representation of chromatographic peaks is preserved for the purposes of both quantitative and qualitative analyses that require peak integration for statistical analysis. The use of this flexible method is promising in the investigation of pathways that require multiple targets and are highly integrated within the system. Here, we demonstrate the application of this method in a fast ultra-high performance liquid chromatography (UHPLC) analysis to integrate wide-isolation quantitative strategies for high-resolution mass spectrometry (HRMS) combined with profiling qualitative metabolomics for the analysis of tryptophan degradation metabolites in mouse serum. Analysis of tryptophan-deficient states as compared to control in both germ-free or E. coli gut microbiota states was used to quantitate pathway-specific metabolites as well as obtain full profiling information. The quantitative and qualitative results revealed the preservation of the primary pathways of degradation in the kynurenine pathway to potentially produce primary products such as nicotinamide during stress-induced dietary states.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts

Objective

Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.

Methods

63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.

Results

Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.

Conclusions

We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

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Amino-acids and acylcarnitines analyzed in three studies with >16,000 individuals.

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Develop a generic and adaptable bioinformatics workflow.

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Analysis of the impact of 29 clinical and life-style factors on blood metabolites.

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Analysis of network between factors and metabolites.

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Comparison of results between studies.

Non-targeted metabolomics reveals diagnostic biomarker in the tongue coating of patients with chronic gastritis

Analysis of the properties of the tongue has been used in traditional Chinese medicine for disease diagnosis. Notably, tongue analysis, which is non-invasive and convenient compared with gastroscopy and pathological examination, can be used to assess chronic gastritis (CG). In order to find potential diagnostic biomarkers and study the metabolic mechanisms of the endogenous small molecules in the tongue coating related to CG, a non-targeted metabolomic analysis method was developed using ultra high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF-MS). It was performed using two different columns in positive and negative ion scanning modes separately. The stability of the samples was evaluated and the age and gender factors of the subjects were excluded to ensure the reliability of the data in this study. Finally, under the four analysis models, 130, 229, 113 and 92 differential compounds were found using multivariate statistical methods respectively. 37 potential biomarkers were putatively identified after removing the duplicate compounds and five potential diagnostic biomarkers were putatively identified by receiver operating characteristic (ROC) curve analysis, including inosine, oleamide, adenosine, N-acetylglucosamine (GlcNAc) and xanthine. The main metabolic pathways associated with CG were purine metabolism, amino acid metabolism, sphingolipid metabolism and energy metabolism, which suggested that oxygen free radicals and energy metabolism were altered in patients with CG. These results provided a potential new basis for the quantitative diagnosis and pathogenesis of CG.

Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification

Rapid, solution-phase hydrogen/deuterium exchange (HDX) coupled with mass spectrometry (MS) is demonstrated as a means for distinguishing small-molecule metabolites. HDX is achieved using capillary vibrating sharp-edge spray ionization (cVSSI) to allow sufficient time for reagent mixing and exchange in micrometer-sized droplets. Different compounds are observed to incorporate deuterium with varying efficiencies resulting in unique isotopic patterns as revealed in the MS spectra. Using linear regression techniques, parameters representing contribution to exchange by different hydrogen types can be computed. In this proof-of-concept study, the exchange parameters are shown to be useful in the retrodiction of the amount of deuterium incorporated within different compounds. On average, the exchange parameters retrodict the exchange level with ~ 2.2-fold greater accuracy than treating all exchangeable hydrogens equally. The parameters can be used to produce hypothetical isotopic distributions that agree (± 16% RMSD) with experimental measurements. These initial studies are discussed in light of their potential value for identifying challenging metabolite species.

Repeated administration of the NSAID meloxicam alters the plasma and urine lipidome

Non-steroidal anti-inflammatories (NSAIDs), such as meloxicam, are the mainstay for treating painful and inflammatory conditions in animals and humans; however, the repeated administration of NSAIDs can cause adverse effects, limiting the long-term administration of these drugs to some patients. The primary aim of this study was to determine the effects of repeated meloxicam administration on the feline plasma and urine lipidome. Cats (n = 12) were treated subcutaneously with either saline solution or 0.3 mg/kg body weight of meloxicam daily for up to 31 days. Plasma and urine lipidome were determined by LC-MS before the first treatment and at 4, 9 and 13 and 17 days after the first administration of meloxicam. The repeated administration of meloxicam altered the feline plasma and urine lipidome as demonstrated by multivariate statistical analysis. The intensities of 94 out of 195 plasma lipids were altered by the repeated administration of meloxicam to cats (p < 0.05). Furthermore, we identified 12 lipids in plasma and 10 lipids in urine that could serve as biomarker candidates for discriminating animals receiving NSAIDs from healthy controls. Expanding our understanding about the effects of NSAIDs in the body could lead to the discovery of mechanism(s) associated with intolerance to NSAIDs.

Metabolomics Profiling on Different Stages of Colorectal Cancer: A Systematic Review

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Early diagnosis and accurate staging of the disease is vital to improve the prognosis. Metabolomics has been used to identify changes in metabolite profiles in the different stages of cancer in order to introduce new non-invasive molecular tools for staging. In this systematic review, we aim to identify the common metabolite changes in human biological samples and the dominant metabolic pathways associated with CRC progression. A broad systematic search was carried out from selected databases. Four reviewers screened and reviewed the titles, abstracts, and full-text articles according to the inclusion and exclusion criteria. Quality assessment was conducted on the eight articles which met the criteria. Data showed that the metabolites involved with redox status, energy metabolism and intermediates of amino acids, choline and nucleotides metabolism were the most affected during CRC progression. However, there were differences in the levels of individual metabolites detected between the studies, and this might be due to the study population, sample preparation, analytical platforms used and statistical tools. In conclusion, this systematic review highlights the changes in metabolites from early to late stages of CRC. Moreover, biomarkers for prognosis are important to reduce CRC-related mortality.

Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics

Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats

Objectives This aim of this study was to characterize the composition and content of the feline urine metabolome. Methods Eight healthy domestic cats were acclimated at least 10 days before starting the study. Urine samples (~2 ml) were collected by ultrasound-guided cystocentesis. Samples were centrifuged at 1000 × g for 8 mins, and the supernatant was analyzed by gas chromatography/time-of-flight mass spectrometery. The urine metabolome was characterized using an untargeted metabolomics approach. Results Three hundred and eighteen metabolites were detected in the urine of the eight cats. These molecules are key components of at least 100 metabolic pathways. Feline urine appears to be dominated by carbohydrates, carbohydrate conjugates, organic acid and derivatives, and amino acids and analogs. The five most abundant molecules were phenaceturic acid, hippuric acid, pseudouridine phosphate and 3-(4-hydroxyphenyl) propionic acid. Conclusions and relevance This study is the first to characterize the feline urine metabolome. The results of this study revealed the presence of multiple low-molecular-weight substances that were not known to be present in feline urine. As expected, the origin of the metabolites detected in urine was diverse, including endogenous compounds and molecules biosynthesized by microbes. Also, the diet seemed to have had a relevant role on the urine metabolome. Further exploration of the urine metabolic phenotype will open a window for discovering unknown, or poorly understood, metabolic pathways. In turn, this will advance our understanding of feline biology and lead to new insights in feline physiology, nutrition and medicine.

Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow

Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis.

Sparse Proteomics Analysis - a compressed sensing-based approach for feature selection and classification of high-dimensional proteomics mass spectrometry data

BACKGROUND

High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible.

RESULTS

We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

The LIFE Child study: a population-based perinatal and pediatric cohort in Germany

The LIFE Child study is a large population-based longitudinal childhood cohort study conducted in the city of Leipzig, Germany. As a part of LIFE, a research project conducted at the Leipzig Research Center for Civilization Diseases, it aims to monitor healthy child development from birth to adulthood and to understand the development of lifestyle diseases such as obesity. The study consists of three interrelated cohorts; the birth cohort, the health cohort, and the obesity cohort. Depending on age and cohort, the comprehensive study program comprises different medical, psychological, and sociodemographic assessments as well as the collection of biological samples. Optimal data acquisition, process management, and data analysis are guaranteed by a professional team of physicians, certified study assistants, quality managers, scientists and statisticians. Due to the high popularity of the study, more than 3000 children have already participated until the end of 2015, and two-thirds of them participate continuously. The large quantity of acquired data allows LIFE Child to gain profound knowledge on the development of children growing up in the twenty-first century. This article reports the number of available and analyzable data and demonstrates the high relevance and potential of the study.

Systematic Assessment of Seven Solvent and Solid-Phase Extraction Methods for Metabolomics Analysis of Human Plasma by LC-MS

The comparison of extraction methods for global metabolomics is usually executed in biofluids only and focuses on metabolite coverage and method repeatability. This limits our detailed understanding of extraction parameters such as recovery and matrix effects and prevents side-by-side comparison of different sample preparation strategies. To address this gap in knowledge, seven solvent-based and solid-phase extraction methods were systematically evaluated using standard analytes spiked into both buffer and human plasma. We compared recovery, coverage, repeatability, matrix effects, selectivity and orthogonality of all methods tested for non-lipid metabolome in combination with reversed-phased and mixed-mode liquid chromatography mass spectrometry analysis (LC-MS). Our results confirmed wide selectivity and excellent precision of solvent precipitations, but revealed their high susceptibility to matrix effects. The use of all seven methods showed high overlap and redundancy which resulted in metabolite coverage increases of 34-80% depending on LC-MS method employed as compared to the best single extraction protocol (methanol/ethanol precipitation) despite 7x increase in MS analysis time and sample consumption. The most orthogonal methods to methanol-based precipitation were ion-exchange solid-phase extraction and liquid-liquid extraction using methyl-tertbutyl ether. Our results help facilitate rational design and selection of sample preparation methods and internal standards for global metabolomics.

Current LC-MS methods and procedures applied to the identification of new steroid metabolites

The study of the metabolism of steroids has a long history; from the first characterizations of the major metabolites of steroidal hormones in the pre-chromatographic era, to the latest discoveries of new forms of excretions. The introduction of mass spectrometers coupled to gas chromatography at the end of the 1960's represented a major breakthrough for the elucidation of new metabolites. In the last two decades, this technique is being complemented by the use of liquid chromatography-mass spectrometry (LC-MS). In addition of becoming fundamental in clinical steroid determinations due to its excellent specificity, throughput and sensitivity, LC-MS has emerged as an exceptional tool for the discovery of new steroid metabolites. The aim of the present review is to provide an overview of the current LC-MS procedures used in the quest of novel metabolic products of steroidal hormones and exogenous steroids. Several aspects regarding LC separations are first outlined, followed by a description of the key processes that take place in the mass spectrometric analysis, i.e. the ionization of the steroids in the source and the fragmentation of the selected precursor ions in the collision cell. The different analyzers and approaches employed together with representative examples of each of them are described. Special emphasis is placed on triple quadrupole analyzers (LC-MS/MS), since they are the most commonly employed. Examples on the use of precursor ion scan, neutral loss scan and theoretical selected reaction monitoring strategies are also explained.

High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue

Formalin-fixed and paraffin-embedded (FFPE) tissue specimens are the gold standard for histological examination, and they provide valuable molecular information in tissue-based research. Metabolite assessment from archived tissue samples has not been extensively conducted because of a lack of appropriate protocols and concerns about changes in metabolite content or chemical state due to tissue processing. We present a protocol for the in situ analysis of metabolite content from FFPE samples using a high-mass-resolution matrix-assisted laser desorption/ionization fourier-transform ion cyclotron resonance mass spectrometry imaging (MALDI-FT-ICR-MSI) platform. The method involves FFPE tissue sections that undergo deparaffinization and matrix coating by 9-aminoacridine before MALDI-MSI. Using this platform, we previously detected ∼1,500 m/z species in the mass range m/z 50-1,000 in FFPE samples; the overlap compared with fresh frozen samples is 72% of m/z species, indicating that metabolites are largely conserved in FFPE tissue samples. This protocol can be reproducibly performed on FFPE tissues, including small samples such as tissue microarrays and biopsies. The procedure can be completed in a day, depending on the size of the sample measured and raster size used. Advantages of this approach include easy sample handling, reproducibility, high throughput and the ability to demonstrate molecular spatial distributions in situ. The data acquired with this protocol can be used in research and clinical practice.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Integration of Genome-Wide SNP Data and Gene-Expression Profiles Reveals Six Novel Loci and Regulatory Mechanisms for Amino Acids and Acylcarnitines in Whole Blood

Profiling amino acids and acylcarnitines in whole blood spots is a powerful tool in the laboratory diagnosis of several inborn errors of metabolism. Emerging data suggests that altered blood levels of amino acids and acylcarnitines are also associated with common metabolic diseases in adults. Thus, the identification of common genetic determinants for blood metabolites might shed light on pathways contributing to human physiology and common diseases. We applied a targeted mass-spectrometry-based method to analyze whole blood concentrations of 96 amino acids, acylcarnitines and pathway associated metabolite ratios in a Central European cohort of 2,107 adults and performed genome-wide association (GWA) to identify genetic modifiers of metabolite concentrations. We discovered and replicated six novel loci associated with blood levels of total acylcarnitine, arginine (both on chromosome 6; rs12210538, rs17657775), propionylcarnitine (chromosome 10; rs12779637), 2-hydroxyisovalerylcarnitine (chromosome 21; rs1571700), stearoylcarnitine (chromosome 1; rs3811444), and aspartic acid traits (chromosome 8; rs750472). Based on an integrative analysis of expression quantitative trait loci in blood mononuclear cells and correlations between gene expressions and metabolite levels, we provide evidence for putative causative genes: SLC22A16 for total acylcarnitines, ARG1 for arginine, HLCS for 2-hydroxyisovalerylcarnitine, JAM3 for stearoylcarnitine via a trans-effect at chromosome 1, and PPP1R16A for aspartic acid traits. Further, we report replication and provide additional functional evidence for ten loci that have previously been published for metabolites measured in plasma, serum or urine. In conclusion, our integrative analysis of SNP, gene-expression and metabolite data points to novel genetic factors that may be involved in the regulation of human metabolism. At several loci, we provide evidence for metabolite regulation via gene-expression and observed overlaps with GWAS loci for common diseases. These results form a strong rationale for subsequent functional and disease-related studies.

An Economic Evaluation of Neonatal Screening for Inborn Errors of Metabolism Using Tandem Mass Spectrometry in Thailand

Background

Inborn errors of metabolism (IEM) are a rare group of genetic diseases which can lead to several serious long-term complications in newborns. In order to address these issues as early as possible, a process called tandem mass spectrometry (MS/MS) can be used as it allows for rapid and simultaneous detection of the diseases. This analysis was performed to determine whether newborn screening by MS/MS is cost-effective in Thailand.

Method

A cost-utility analysis comprising a decision-tree and Markov model was used to estimate the cost in Thai baht (THB) and health outcomes in life-years (LYs) and quality-adjusted life year (QALYs) presented as an incremental cost-effectiveness ratio (ICER). The results were also adjusted to international dollars (I$) using purchasing power parities (PPP) (1 I$ = 17.79 THB for the year 2013). The comparisons were between 1) an expanded neonatal screening programme using MS/MS screening for six prioritised diseases: phenylketonuria (PKU); isovaleric acidemia (IVA); methylmalonic acidemia (MMA); propionic acidemia (PA); maple syrup urine disease (MSUD); and multiple carboxylase deficiency (MCD); and 2) the current practice that is existing PKU screening. A comparison of the outcome and cost of treatment before and after clinical presentations were also analysed to illustrate the potential benefit of early treatment for affected children. A budget impact analysis was conducted to illustrate the cost of implementing the programme for 10 years.

Results

The ICER of neonatal screening using MS/MS amounted to 1,043,331 THB per QALY gained (58,647 I$ per QALY gained). The potential benefits of early detection compared with late detection yielded significant results for PKU, IVA, MSUD, and MCD patients. The budget impact analysis indicated that the implementation cost of the programme was expected at approximately 2,700 million THB (152 million I$) over 10 years.

Conclusion

At the current ceiling threshold, neonatal screening using MS/MS in the Thai context is not cost-effective. However, the treatment of patients who were detected early for PKU, IVA, MSUD, and MCD, are considered favourable. The budget impact analysis suggests that the implementation of the programme will incur considerable expenses under limited resources. A long-term epidemiological study on the incidence of IEM in Thailand is strongly recommended to ascertain the magnitude of problem.

The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany

Background

The LIFE-Adult-Study is a population-based cohort study, which has recently completed the baseline examination of 10,000 randomly selected participants from Leipzig, a major city with 550,000 inhabitants in the east of Germany. It is the first study of this kind and size in an urban population in the eastern part of Germany. The study is conducted by the Leipzig Research Centre for Civilization Diseases (LIFE). Our objective is to investigate prevalences, early onset markers, genetic predispositions, and the role of lifestyle factors of major civilization diseases, with primary focus on metabolic and vascular diseases, heart function, cognitive impairment, brain function, depression, sleep disorders and vigilance dysregulation, retinal and optic nerve degeneration, and allergies.

Methods/design

The study covers a main age range from 40-79 years with particular deep phenotyping in elderly participants above the age of 60. The baseline examination was conducted from August 2011 to November 2014. All participants underwent an extensive core assessment programme (5-6 h) including structured interviews, questionnaires, physical examinations, and biospecimen collection. Participants over 60 underwent two additional assessment programmes (3-4 h each) on two separate visits including deeper cognitive testing, brain magnetic resonance imaging, diagnostic interviews for depression, and electroencephalography.

Discussion

The participation rate was 33 %. The assessment programme was accepted well and completely passed by almost all participants. Biomarker analyses have already been performed in all participants. Genotype, transcriptome and metabolome analyses have been conducted in subgroups. The first follow-up examination will commence in 2016.

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.

Fast filtration sampling protocol for mammalian suspension cells tailored for phosphometabolome profiling by capillary ion chromatography - tandem mass spectrometry

Capillary ion chromatography (capIC) is the premium separation technology for low molecular phosphometabolites and nucleotides in biological extracts. Removal of excessive amounts of salt during sample preparation stages is a prerequisite to enable high quality capIC separation in combination with reproducible and sensitive MS detection. Existing sampling protocols for mammalian cells used for GC-MS and LC-MS metabolic profiling can therefore not be directly applied to capIC separations. Here, the development of a fast filtration sampling protocol for mammalian suspension cells tailored for quantitative profiling of the phosphometabolome on capIC-MS/MS is presented. The whole procedure from sampling the culture to transfer of filter to quenching and extraction solution takes less than 10s. To prevent leakage it is critical that a low vacuum pressure is applied, and satisfactorily reproducibility was only obtained by usage of a vacuum pressure controlling device. A vacuum of 60mbar was optimal for filtration of multiple myeloma Jjn-3 cell cultures through 5μm polyvinylidene (PVDF) filters. A quick deionized water (DI-water) rinse step prior to extraction was tested, and significantly higher metabolite yields were obtained during capIC-MS/MS analyses in this extract compared to extracts prepared by saline and reduced saline (25%) washing steps only. In addition, chromatographic performance was dramatically improved. Thus, it was verified that a quick DI-water rinse is tolerated by the cells and can be included as the final stage during filtration. Over 30 metabolites were quantitated in JJN-3 cell extracts by using the optimized sampling protocol with subsequent capIC-MS/MS analysis, and up to 2 million cells can be used in a single filtration step for the chosen filter and vacuum pressure. The technical set-up is also highly advantageous for microbial metabolome filtration protocols after optimization of vacuum pressure and washing solutions, and the reduced salt content of the extract will also improve the quality of LC-MS analysis due to lower salt adduct ion formation.

Proton nuclear magnetic resonance spectroscopy of urine samples in preterm asphyctic newborn: a metabolomic approach

In order to highlight differences in the metabolic profile of healthy (control) compared with asphyxiated newborns, by using untargeted metabolomic approach coupled with (1)H NMR spectroscopy, we evaluated the effects of asphyxia on newborn urine metabolites. Our results showed that lactate, glucose and TMAO, together with threonine plus 3-hydroxyisovalerate are the metabolites more characterizing the asphyxiated group; lower contribute to discrimination is related to other metabolites such as dimethylglycine, dimethylamine, creatine, succinate, formate, urea and aconitate. After 24-48h from resuscitation preterm asphyctic neonates showed their recovery pattern that still can be differentiated by the controls.

Liquid chromatography tandem mass spectrometry in the clinical laboratory

Clinical laboratory medicine has seen the introduction and evolution of liquid chromatography tandem mass spectrometry in routine clinical laboratories over the last 10–15 years. There still exists a wide diversity of assays from very esoteric and highly specialist manual assays to more simplified kit-based assays. The technology is not static as manufacturers are continually making improvements. Mass spectrometry is now commonly used in several areas of diagnostics including therapeutic drug monitoring, toxicology, endocrinology, paediatrics and microbiology. Some of the most high throughput analyses or common analytes include vitamin D, immunosuppressant monitoring, androgen measurement and newborn screening. It also offers flexibility for the measurement of analytes in a variety of different matrices which would prove difficult with immunoassays. Unlike immunoassays or high-pressure liquid chromatography assays using ultraviolet or fluorescence detection, mass spectrometry offers better specificity and reduced interferences if attention is paid to potential isobaric compounds. Furthermore, multiplexing, which enables multiple analytes to be measured with the same volume of serum is advantageous, and the requirement for large sample volumes is decreasing as instrument sensitivity increases. There are many emerging applications in the literature. Using mass spectrometry to identify novel isoforms or modified peptides is possible as is quantification of proteins and peptides, with or without protein digests. Future developments by the manufacturers may also include mechanisms to improve the throughput of samples and strategies to decrease the level of skill required by the operators.

Photodissociation mass spectrometry: new tools for characterization of biological molecules

Photodissociation mass spectrometry combines the ability to activate and fragment ions using photons with the sensitive detection of the resulting product ions by mass spectrometry. This combination affords a versatile tool for characterization of biological molecules. The scope and breadth of photodissociation mass spectrometry have increased substantially over the past decade as new research groups have entered the field and developed a number of innovative applications that illustrate the ability of photodissociation to produce rich fragmentation patterns, to cleave bonds selectively, and to target specific molecules based on incorporation of chromophores. This review focuses on many of the key developments in photodissociation mass spectrometry over the past decade with a particular emphasis on its applications to biological molecules.

The metabolic signature associated with the Western dietary pattern: a cross-sectional study

Background

Metabolic profiles have been shown to be associated to obesity status and insulin sensitivity. Dietary intakes influence metabolic pathways and therefore, different dietary patterns may relate to modifications in metabolic signatures. The objective was to verify associations between dietary patterns and metabolic profiles composed of amino acids (AAs) and acylcarnitines (ACs).

Methods

210 participants were recruited in the greater Quebec City area between September 2009 and December 2011. Dietary patterns had been previously derived using principal component analysis (PCA). The Prudent dietary pattern was characterised by higher intakes of vegetables, fruits, whole grain products, non-hydrogenated fat and lower intakes of refined grain products, whereas the Western dietary pattern was associated with higher intakes of refined grain products, desserts, sweets and processed meats. Targeted metabolites were quantified in 37 participants with the Biocrates Absolute IDQ p150 (Biocrates Life Sciences AG, Austria) mass spectrometry method (including 14 amino acids and 41 acylcarnitines).

Results

PCA analysis with metabolites including AAs and ACs revealed two main components explaining the most variance in overall data (13.8%). PC1 was composed mostly of medium- to long-chain ACs (C16:2, C14:2, C14:2-OH, C16, C14:1-OH, C14:1, C10:2, C5-DC/C6-OH, C12, C18:2, C10, C4:1-DC/C6, C8:1 and C2) whereas PC2 included certain AAs and short-chain ACs (xLeu, Met, Arg, Phe, Pro, Orn, His, C0, C3, C4 and C5). The Western dietary pattern correlated negatively with PC1 and positively with PC2 (r = −0.34, p = 0.05 and r = 0.38, p = 0.03, respectively), independently of age, sex and BMI.

Conclusion

These findings suggest that the Western dietary pattern is associated with a specific metabolite signature characterized by increased levels of AAs including branched-chain AAs (BCAAs) and short-chain ACs.

Trial registration

NCT01343342

Androgen glucuronides analysis by liquid chromatography tandem-mass spectrometry: could it raise new perspectives in the diagnostic field of hormone-dependent malignancies?

Breast and prostate constitute organs of intense steroidogenic activity. Clinical and epidemiologic data provide strong evidence on the influence of androgens and estrogens on the risk of typical hormone-dependent malignancies, like breast and prostate cancer. Recent studies have focused on the role of androgen metabolites in regulating androgen concentrations in hormone-sensitive tissues. Steroid glucuronidation has been suggested to have a prominent role in controlling the levels and the biological activity of unconjugated androgens. It is well-established that serum levels of androgen glucuronides reflect androgen metabolism in androgen-sensitive tissues. Quantitative analysis of androgen metabolites in blood specimens is the only minimally invasive approach permitting an accurate estimate of the total pool of androgens. During the past years, androgen glucuronides analysis most often involved radioimmunoassays (RIA) or direct immunoassays, both methods bearing serious limitations. However, recent impressive technical advances in mass spectrometry, and particularly in high performance liquid chromatography coupled with mass spectrometry (LC-MS/MS), have overcome these drawbacks enabling the simultaneous, quantitative analysis of multiple steroids even at low concentrations. Blood androgen profiling by LC-MS/MS, a robust and reliable technique of high selectivity, sensitivity, specificity, precision and accuracy emerges as a promising new approach in the study of human pathology. The present review offers a contemporary insight in androgen glucuronides profiling through the application of LC-MS/MS, highlighting new perspectives in the study of steroids and their implication in hormone-dependent malignancies.

Spectral deconvolution for gas chromatography mass spectrometry-based metabolomics: current status and future perspectives

Mass spectrometry coupled to gas chromatography (GC-MS) has been widely applied in the field of metabolomics. Success of this application has benefited greatly from computational workflows that process the complex raw mass spectrometry data and extract the qualitative and quantitative information of metabolites. Among the computational algorithms within a workflow, deconvolution is critical since it reconstructs a pure mass spectrum for each component that the mass spectrometer observes. Based on the pure spectrum, the corresponding component can be eventually identified and quantified. Deconvolution is challenging due to the existence of co-elution. In this review, we focus on progress that has been made in the development of deconvolution algorithms and provide thoughts on future developments that will expand the application of GC-MS in metabolomics.

Stability of metabolites in dried blood spots stored at different temperatures over a 2-year period

Background: Quantitative LC–ESI-MS/MS, developed from newborn screening, is increasingly used for targeted metabolite profiling. Dried blood spots (DBS) provide easily obtainable biological samples but long-term stability data are sparse. DBS were stored at ambient temperature (room temperature [RT]; 21°C), -20 and -80°C. Metabolites were analyzed at 12 time points (0–104 weeks) by LC–ESI-MS/MS, using fully quantitative stable isotope dilution. Results: Principal component analysis showed alterations in metabolite stability at different temperatures, with major changes only at RT. Univariate analysis for individual analytes demonstrated increases or reductions in concentration. Conclusion: Significant changes are observed in certain DBS metabolites at RT, which are attenuated or not present when frozen. These data will help to inform the design, analysis and interpretation of future DBS studies.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

Feasibility of identifying the tobacco-related global metabolome in blood by UPLC-QTOF-MS

Metabolomics is likely an ideal tool to assess tobacco smoke exposure and the impact of cigarette smoke on human exposure and health. To assess reproducibility and feasibility of this by UPLC-QTOF-MS, three experiments were designed for the assessment of smokers' blood. Experiment I was an analysis of 8 smokers with 8 replicates. Experiment II was an analysis of 62 pooled quality control (QC) samples from 7 nonsmokers' plasma placed as every tenth sample among a study of 613 samples from 160 smokers. Finally, to examine the feasibility of metabolomic study in assessing smoke exposure, Experiment III consisted of 9 smokers and 10 nonsmokers' serum to evaluate differences in their global metabolome. There was minimal measurement and sample preparation variation in all experiments, although some caution is needed when analyzing specific parts of the chromatogram. When assessing QC samples in the large scale study, QC clustering indicated high stability, reproducibility, and consistency. Finally, in addition to the identification of nicotine metabolites as expected, there was a characteristic profile distinguishing smokers from nonsmokers. Metabolites selected from putative identifications were verified by MS/MS, showing the potential to identify metabolic phenotypes and new metabolites relating to cigarette smoke exposure and toxicity.