Instrumental and experimental effects in LC-MS-based metabolomics

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples. Metabolomics is emerging as a powerful tool generally for precision medicine. Particularly, integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However, metabolomics data are very complicated. Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis. In this review article, we comprehensively review various methods that are used to preprocess and pretreat metabolomics data, including MS-based data and NMR -based data preprocessing, dealing with zero and/or missing values and detecting outliers, data normalization, data centering and scaling, data transformation. We discuss the advantages and limitations of each method. The choice for a suitable preprocessing method is determined by the biological hypothesis, the characteristics of the data set, and the selected statistical data analysis method. We then provide the perspective of their applications in the microbiome and metabolome research.

Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Toxicometabolomics: Small Molecules to Answer Big Toxicological Questions

Given the high biological impact of classical and emerging toxicants, a sensitive and comprehensive assessment of the hazards and risks of these substances to organisms is urgently needed. In this sense, toxicometabolomics emerged as a new and growing field in life sciences, which use metabolomics to provide new sets of susceptibility, exposure, and/or effects biomarkers; and to characterize in detail the metabolic responses and altered biological pathways that various stressful stimuli cause in many organisms. The present review focuses on the analytical platforms and the typical workflow employed in toxicometabolomic studies, and gives an overview of recent exploratory research that applied metabolomics in various areas of toxicology.

Representing the Metabolome with High Fidelity: Range and Response as Quality Control Factors in LC-MS-Based Global Profiling

Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.

Lipidomic profiling of chorionic villi in the placentas of women with chronic venous disease

Background: Chronic venous disease (CVD) is a prevalent lower limb venous pathology that especially affects women, who also show an increased risk of this disease during pregnancy. Studies have shown significant structural changes in the placentas of women with CVD and several markers of tissue damage have been also described. Patients and Methods: To try to understand the different placental pathologies, research efforts have focused on examining metabolomic profiles as indicators of the repercussions of these vascular disorders. This study examines changes produced in the metabolomic profiles of chorionic villi in the placentas of women with CVD. In a study population of 12 pregnant women, 6 with and 6 without CVD, we compared through mass spectroscopy coupled to ultra-high performance liquid chromatography (UHPLC-MS), 240 metabolites in chorionic villus samples. Results: This study is the first to detect in the placental villi of pregnant women with CVD, modifications in lysophosphatidylcholines and amino acids along with diminished levels of other lipids such as triglycerides, sphingomyelins, and non-esterified omega 9 fatty acids, suggesting a role of these abnormalities in the pathogenesis of CVD. Conclusions: Our findings are a starting point for future studies designed to examine the impacts of CVD on maternal and fetal well-being.

MS2 and LC libraries for untargeted metabolomics: Enhancing method development and identification confidence

As part of the "omics" technologies in the life sciences, metabolomics is becoming increasingly important. In untargeted metabolomics, unambiguous metabolite identification and the inevitable coverage bias that comes with the selection of analytical conditions present major challenges. Reliable compound annotation is essential for translating metabolomics data into meaningful biological information. Here, we developed a fast and transferable method for generating in-house MS² libraries to improve metabolite identification. Using the new method we established an in-house MS² library that includes over 4,000 fragmentation spectra of 506 standard compounds for 6 different normalized collision energies (NCEs). Additionally, we generated a comprehensive liquid chromatography (LC) library by testing 57 different LC-MS conditions for 294 compounds. We used the library information to develop an untargeted metabolomics screen with maximum coverage of the metabolome that was successfully tested in a study of 360 human serum samples. The current work demonstrates a workflow for LC-MS/MS-based metabolomics, with enhanced metabolite identification confidence and the possibility to select suitable analysis conditions according to the specific research interest.

Histone variant macroH2A1 rewires carbohydrate and lipid metabolism of hepatocellular carcinoma cells towards cancer stem cells

Hepatocellular carcinomas (HCCs) contain a sub-population of cancer stem cells (CSCs) that are responsible for tumor relapse, metastasis, and chemoresistance. We recently showed that loss of macroH2A1, a variant of the histone H2A and an epigenetic regulator of stem-cell function, in HCC leads to CSC-like features such as resistance to chemotherapeutic agents and growth of large and relatively undifferentiated tumors in xenograft models. These HCC cells silenced for macroH2A1 also exhibited stem-like metabolic changes consistent with enhanced glycolysis. However, there is no consensus as to the metabolic characteristics of CSCs that render them adaptable to microenvironmental changes by conveniently shifting energy production source or by acquiring intermediate metabolic phenotypes. Here, we assessed long-term proliferation, energy metabolism, and central carbon metabolism in human hepatoma HepG2 cells depleted in macroH2A1. MacroH2A1-depleted HepG2 cells were insensitive to serum exhaustion and showed two distinct, but interdependent changes in glucose and lipid metabolism in CSCs: (1) massive upregulation of acetyl-coA that is transformed into enhanced lipid content and (2) increased activation of the pentose phosphate pathway, diverting glycolytic intermediates to provide precursors for nucleotide synthesis. Integration of metabolomic analyses with RNA-Seq data revealed a critical role for the Liver X Receptor pathway, whose inhibition resulted in attenuated CSCs-like features. These findings shed light on the metabolic phenotype of epigenetically modified CSC-like hepatic cells, and highlight a potential approach for selective therapeutic targeting.

Functional Metabolomics—A Useful Tool to Characterize Stress-Induced Metabolome Alterations Opening New Avenues towards Tailoring Food Crop Quality

The breeding of stress-tolerant cultivated plants that would allow for a reduction in harvest losses and undesirable decrease in quality attributes requires a new quality of knowledge on molecular markers associated with relevant agronomic traits, on quantitative metabolic responses of plants to stress challenges, and on the mechanisms controlling the biosynthesis of these molecules. By combining metabolomics with genomics, transcriptomics and proteomics datasets a more comprehensive knowledge of the composition of crop plants used for food or animal feed is possible. In order to optimize crop trait developments, to enhance crop yields and quality, as well as to guarantee nutritional and health factors that provide the possibility to create functional food or feedstuffs, knowledge about the plants’ metabolome is crucial. Next to classical metabolomics studies, this review focuses on several metabolomics-based working techniques, such as sensomics, lipidomics, hormonomics and phytometabolomics, which were used to characterize metabolome alterations during abiotic and biotic stress in order to find resistant food crops with a preferred quality or at least to produce functional food crops.

Quantification of run order effect on chromatography - mass spectrometry profiling data

Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

INTRODUCTION

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

OBJECTIVES

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

METHODS

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOF-MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

RESULTS

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

CONCLUSION

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

Mass spectrometry as a quantitative tool in plant metabolomics

Metabolomics is a research field used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include the analysis of a wide range of chemical species with very diverse physico-chemical properties, and therefore powerful analytical tools are required for the separation, characterization and quantification of this vast compound diversity present in plant matrices. In this review, challenges in the use of mass spectrometry (MS) as a quantitative tool in plant metabolomics experiments are discussed, and important criteria for the development and validation of MS-based analytical methods provided.This article is part of the themed issue 'Quantitative mass spectrometry'.

Mass spectrometry-based plant metabolomics: Metabolite responses to abiotic stress

Metabolomics is one omics approach that can be used to acquire comprehensive information on the composition of a metabolite pool to provide a functional screen of the cellular state. Studies of the plant metabolome include analysis of a wide range of chemical species with diverse physical properties, from ionic inorganic compounds to biochemically derived hydrophilic carbohydrates, organic and amino acids, and a range of hydrophobic lipid-related compounds. This complexitiy brings huge challenges to the analytical technologies employed in current plant metabolomics programs, and powerful analytical tools are required for the separation and characterization of this extremely high compound diversity present in biological sample matrices. The use of mass spectrometry (MS)-based analytical platforms to profile stress-responsive metabolites that allow some plants to adapt to adverse environmental conditions is fundamental in current plant biotechnology research programs for the understanding and development of stress-tolerant plants. In this review, we describe recent applications of metabolomics and emphasize its increasing application to study plant responses to environmental (stress-) factors, including drought, salt, low oxygen caused by waterlogging or flooding of the soil, temperature, light and oxidative stress (or a combination of them). Advances in understanding the global changes occurring in plant metabolism under specific abiotic stress conditions are fundamental to enhance plant fitness and increase stress tolerance. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:620-649, 2016.

SWATH acquisition mode for drug metabolism and metabolomics investigations

Aim: Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) has recently emerged as a powerful high resolution mass spectrometric data independent acquisition technique. In the present work, the potential and challenges of an integrated strategy based on LC-SWATH/MS for simultaneous drug metabolism and metabolomics studies was investigated. Methodology: The richness of SWATH data allows numerous data analysis approaches, including: detection of metabolites by prediction; metabolite detection by mass defect filtering; quantification from high-resolution MS precursor chromatograms or fragment chromatograms. Multivariate analysis can be applied to the data from the full scan or SWATH windows and allows changes in endogenous metabolites as well as xenobiotic metabolites, to be detected. Principal component variable grouping detects intersample variable correlation and groups variables with similar profiles which simplifies interpretation and highlights related ions and fragments. Principal component variable grouping can extract product ion spectra from the data collected by fragmenting a wide precursor ion window. Conclusion: It was possible to characterize 28 vinpocetine metabolites in urine, mostly mono- and di-hydroxylated forms, and detect endogenous metabolite expression changes in urine after the administration of a single dose of a model drug (vinpocetine) to rats.

Urine metabolomics of women from small villages exposed to high environmental cadmium levels

The present study aimed to identify urine metabolites in women exposed to high cadmium (Cd) levels. Twenty-one women exposed to environmental Cd and 12 age-matched controls were categorized as high exposure (urine Cd ≥ 15 μg/g creatinine; n = 9) or low exposure (15 μg/g creatinine > urine Cd > 5 μg/g creatinine; n = 12). Low-molecular weight metabolites in urine were analyzed by gas chromatography and mass spectrometry after derivatization. An orthogonal partial least-squares discriminant analysis model was constructed, and metabolites from the dimensional model were selected according to the variable importance in projection (>1). Metabolites differing significantly in abundance between different exposure groups were identified by searching mass spectral databases, and related pathways were analyzed using the Kyoto Encyclopedia of Genes and Genomes. Approximately 110 significantly different metabolites were detected with variable importance in projection > 1, and 48 of them were found to differ markedly in abundance among the 3 groups. Twenty-seven matched with known metabolites, including 22 significantly increased and 5 markedly decreased in the high-exposure group (p < 0.01). Kyoto Encyclopedia of Genes and Genomes results indicated that carbohydrate, amino acid, bone, and intestinal flora metabolism and the tricarboxylic acid cycle were affected by Cd exposure. The present study identified metabolites that differed in abundance in response to Cd exposure. Further studies may connect these biomarkers to early damage caused by Cd.

Removing the bottlenecks of cell culture metabolomics: fast normalization procedure, correlation of metabolites to cell number, and impact of the cell harvesting method

INTRODUCTION

Although cultured cells are nowadays regularly analyzed by metabolomics technologies, some issues in study setup and data processing are still not resolved to complete satisfaction: a suitable harvesting method for adherent cells, a fast and robust method for data normalization, and the proof that metabolite levels can be normalized to cell number.

OBJECTIVES

We intended to develop a fast method for normalization of cell culture metabolomics samples, to analyze how metabolite levels correlate with cell numbers, and to elucidate the impact of the kind of harvesting on measured metabolite profiles.

METHODS

We cultured four different human cell lines and used them to develop a fluorescence-based method for DNA quantification. Further, we assessed the correlation between metabolite levels and cell numbers and focused on the impact of the harvesting method (scraping or trypsinization) on the metabolite profile.

RESULTS

We developed a fast, sensitive and robust fluorescence-based method for DNA quantification showing excellent linear correlation between fluorescence intensities and cell numbers for all cell lines. Furthermore, 82-97 % of the measured intracellular metabolites displayed linear correlation between metabolite concentrations and cell numbers. We observed differences in amino acids, biogenic amines, and lipid levels between trypsinized and scraped cells.

CONCLUSION

We offer a fast, robust, and validated normalization method for cell culture metabolomics samples and demonstrate the eligibility of the normalization of metabolomics data to the cell number. We show a cell line and metabolite-specific impact of the harvesting method on metabolite concentrations.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Profiling the metabolome changes caused by cranberry procyanidins in plasma of female rats using (1) H NMR and UHPLC-Q-Orbitrap-HRMS global metabolomics approaches

SCOPE

The objective was to investigate the metabolome changes in female rats gavaged with partially purified cranberry procyanidins (PPCP) using (1) H NMR and UHPLC-Q-Orbitrap-HRMS metabolomics approaches, and to identify the contributing metabolites.

METHODS AND RESULTS

Twenty-four female Sprague-Dawley rats were randomly separated into two groups and administered PPCP or partially purified apple procyanidins (PPAP) for three times using a 250 mg extracts/kg body weight dose. Plasma was collected 6 h after the last gavage and analyzed using (1) H NMR and UHPLC-Q-Orbitrap-HRMS. No metabolome difference was observed using (1) H NMR metabolomics approach. However, LC-HRMS metabolomics data show that metabolome in the plasma of female rats administered PPCP differed from those gavaged with PPAP. Eleven metabolites were tentatively identified from a total of 36 discriminant metabolic features based on accurate masses and/or product ion spectra. PPCP caused a greater increase of exogenous metabolites including p-hydroxybenzoic acid, phenol, phenol-sulphate, catechol sulphate, 3, 4-dihydroxyphenylvaleric acid, and 4'-O-methyl-(-)-epicatechin-3'-O-beta-glucuronide in rat plasma. Furthermore, the plasma level of O-methyl-(-)-epicatechin-O-glucuronide, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(hydroxyphenyl)-ϒ-valerolactone-O-sulphate, 4-hydroxydiphenylamine, and peonidin-3-O-hexose were higher in female rats administered with PPAP.

CONCLUSION

The metabolome changes caused by cranberry procyanidins were revealed using an UHPLC-Q-Orbitrap-HRMS global metabolomics approach. Exogenous and microbial metabolites were the major identified discriminate biomarkers.

Analytical methods in untargeted metabolomics: state of the art in 2015

Metabolomics comprises the methods and techniques that are used to measure the small molecule composition of biofluids and tissues, and is actually one of the most rapidly evolving research fields. The determination of the metabolomic profile - the metabolome - has multiple applications in many biological sciences, including the developing of new diagnostic tools in medicine. Recent technological advances in nuclear magnetic resonance and mass spectrometry are significantly improving our capacity to obtain more data from each biological sample. Consequently, there is a need for fast and accurate statistical and bioinformatic tools that can deal with the complexity and volume of the data generated in metabolomic studies. In this review, we provide an update of the most commonly used analytical methods in metabolomics, starting from raw data processing and ending with pathway analysis and biomarker identification. Finally, the integration of metabolomic profiles with molecular data from other high-throughput biotechnologies is also reviewed.

Development of a Standard Reference Material for metabolomics research

The National Institute of Standards and Technology (NIST), in collaboration with the National Institutes of Health (NIH), has developed a Standard Reference Material (SRM) to support technology development in metabolomics research. SRM 1950 Metabolites in Human Plasma is intended to have metabolite concentrations that are representative of those found in adult human plasma. The plasma used in the preparation of SRM 1950 was collected from both male and female donors, and donor ethnicity targets were selected based upon the ethnic makeup of the U.S. population. Metabolomics research is diverse in terms of both instrumentation and scientific goals. This SRM was designed to apply broadly to the field, not toward specific applications. Therefore, concentrations of approximately 100 analytes, including amino acids, fatty acids, trace elements, vitamins, hormones, selenoproteins, clinical markers, and perfluorinated compounds (PFCs), were determined. Value assignment measurements were performed by NIST and the Centers for Disease Control and Prevention (CDC). SRM 1950 is the first reference material developed specifically for metabolomics research.

Chemical dereplication of marine actinomycetes by liquid chromatography-high resolution mass spectrometry profiling and statistical analysis

Discovery of novel bioactive metabolites from marine bacteria is becoming increasingly challenging, and the development of novel approaches to improve the efficiency of early steps in the microbial drug discovery process is therefore of interest. For example, current protocols for the taxonomic dereplication of microbial strains generally use molecular tools which do not take into consideration the ability of these selected bacteria to produce secondary metabolites. As the identification of novel chemical entities is one of the key elements driving drug discovery programs, this study reports a novel methodology to dereplicate microbial strains by a metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS). In order to process large and complex three dimensional LC-HRMS datasets, the reported method uses a bucketing and presence-absence standardization strategy in addition to statistical analysis tools including principal component analysis (PCA) and cluster analysis. From a closely related group of Streptomyces isolated from geographically varied environments, we demonstrated that grouping bacteria according to the chemical diversity of produced metabolites is reproducible and provides greatly improved resolution for the discrimination of microbial strains compared to current molecular dereplication techniques. Importantly, this method provides the ability to identify putative novel chemical entities as natural product discovery leads.

Analysis of chloroformate-derivatised amino acids, dipeptides and polyamines by LC-MS/MS

A liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed which, with sample preparation using a commercially available kit, allows rapid quantitation of 39 chloroformate-derivatised amino acids (AAs), polyamines (PAs) and dipeptides (DPs) in complex biological matrices. Lower limits of quantitation (LOQ) were 20-150nM for putrescine, spermine, spermidine, cadaverine, agmatine, and below 5μM for all analytes. Responses were linear for all analytes between 0.5 and 50μM. Quantitative measurements of all 39 metabolites were achieved within a 15min runtime. The method was evaluated with a Pseudomonas aeruginosa cell extract study (n=24) and a larger human urine study (n=308). Batch effects were observed in the urine study and an investigation of instrument and sample stability showed a wave-like pattern in the MS responses. Both the run order and inter-batch variation were successfully corrected by normalising to pooled urine quality control data. Thus, this method should be suitable for diverse biological matrices and for large as well as small sample sets.

Metabolomics discloses potential biomarkers for the noninvasive diagnosis of idiopathic portal hypertension

OBJECTIVES

Idiopathic portal hypertension (IPH) is a rare cause of portal hypertension that lacks a specific diagnostic test. Requiring ruling-out other causes of portal hypertension it is frequently misdiagnosed. This study evaluates whether using high-throughput techniques there is a metabolomic profile allowing a noninvasive diagnosis of IPH.

METHODS

Thirty-three IPH patients were included. Matched patients with cirrhosis (CH) and healthy volunteers (HV) were included as controls. Metabolomic analysis of plasma samples was performed using UPLC-time-of-flight-mass spectrometry. We computed Student's P-values, corrected by multiple comparison and VIP score (Variable Importance in the Projection). The metabolites were selected with an adjusted Benjamini Hochberg P value <0.05. We use markers with a greater VIP score, to build partial least squares projection to latent structures regression with discriminant analysis (PLS-DA) representative models to discriminate IPH from CH and from HV. The performance of the PLS-DA model was evaluated using R(2) and Q(2) parameter. An additional internal cross-validation was done.

RESULTS

PLS-DA analysis showed a clear separation of IPH from CH with a model involving 28 metabolites (Q(2)=0.67, area under the curve (AUC)=0.99) and a clear separation of IPH from healthy subjects with a model including 31 metabolites (Q(2)=0.75, AUC=0.98). After cross-validation, both models showed high rates of sensitivity (94.8 and 97.5), specificity (89.1 and 89.7), and AUC (0.98 and 0.98), reinforcing the strength of our findings.

CONCLUSIONS

A metabolomic profile clearly differentiating patients with IPH from CH and healthy subjects has been identified using subsets of 28 and 31 metabolites, respectively. Therefore, metabolomic analysis appears to be a valuable tool for the noninvasive diagnosis of IPH.

Metabolomics as a tool to investigate abiotic stress tolerance in plants

Metabolites reflect the integration of gene expression, protein interaction and other different regulatory processes and are therefore closer to the phenotype than mRNA transcripts or proteins alone. Amongst all –omics technologies, metabolomics is the most transversal and can be applied to different organisms with little or no modifications. It has been successfully applied to the study of molecular phenotypes of plants in response to abiotic stress in order to find particular patterns associated to stress tolerance. These studies have highlighted the essential involvement of primary metabolites: sugars, amino acids and Krebs cycle intermediates as direct markers of photosynthetic dysfunction as well as effectors of osmotic readjustment. On the contrary, secondary metabolites are more specific of genera and species and respond to particular stress conditions as antioxidants, Reactive Oxygen Species (ROS) scavengers, coenzymes, UV and excess radiation screen and also as regulatory molecules. In addition, the induction of secondary metabolites by several abiotic stress conditions could also be an effective mechanism of cross-protection against biotic threats, providing a link between abiotic and biotic stress responses. Moreover, the presence/absence and relative accumulation of certain metabolites along with gene expression data provides accurate markers (mQTL or MWAS) for tolerant crop selection in breeding programs.

Label-free quantitative proteomics trends for protein-protein interactions

Understanding protein interactions within the complexity of a living cell is challenging, but techniques coupling affinity purification and mass spectrometry have enabled important progress to be made in the past 15 years. As identification of protein-protein interactions is becoming easier, the quantification of the interaction dynamics is the next frontier. Several quantitative mass spectrometric approaches have been developed to address this issue that vary in their strengths and weaknesses. While isotopic labeling approaches continue to contribute to the identification of regulated interactions, techniques that do not require labeling are becoming increasingly used in the field. Here, we describe the major types of label-free quantification used in interaction proteomics, and discuss the relative merits of data dependent and data independent acquisition approaches in label-free quantification. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Metabolome analysis of gram-positive bacteria such as Staphylococcus aureus by GC-MS and LC-MS

The field of metabolomics has become increasingly important in the context of functional genomics. Together with other "omics" data, the investigation of the metabolome is an essential part of systems biology. Beside the analysis of human and animal biofluids, the investigation of the microbial physiology by methods of metabolomics has gained increased attention. For example, the analysis of metabolic processes during growth or virulence factor expression is crucially important to understand pathogenesis of bacteria. Common bioanalytical techniques for metabolome analysis include liquid and gas chromatographic methods coupled to mass spectrometry (LC-MS and GC-MS) and spectroscopic approaches such as NMR. In order to achieve metabolome data representing the physiological status of a microorganism, well-verified protocols for sampling and analysis are necessary. This chapter presents a detailed protocol for metabolome analysis of the Gram-positive bacterium Staphylococcus aureus. A detailed manual for cell sampling and metabolite extraction is given, followed by the description of the analytical procedures GC-MS and LC-MS. The advantages and limitations of each experimental setup are discussed. Here, a guideline specified for S. aureus metabolomics and information for important protocol steps are presented, to avoid common pitfalls in microbial metabolome analysis.

Mass spectrometry-based holistic analytical approaches for metabolite profiling in systems biology studies

Metabonomics and metabolomics represent one of the three major platforms in systems biology. To perform metabolomics it is necessary to generate comprehensive "global" metabolite profiles from complex samples, for example, biological fluids or tissue extracts. Analytical technologies based on mass spectrometry (MS), and in particular on liquid chromatography-MS (LC-MS), have become a major tool providing a significant source of global metabolite profiling data. In the present review we describe and compare the utility of the different analytical strategies and technologies used for MS-based metabolomics with a particular focus on LC-MS. Both the advantages offered by the technology and also the challenges and limitations that need to be addressed for the successful application of LC-MS in metabolite analysis are described. Data treatment and approaches resulting in the detection and identification of biomarkers are considered. Special emphasis is given to validation issues, instrument stability, and QA/quality control (QC) procedures.

Gas chromatography-mass spectrometric method for metabolic profiling of tobacco leaves

A gas chromatography-mass spectrometric method was developed for profiling of tobacco leaves. The differentiation among tobacco leaves planted in two different regions was investigated. Prior to analysis, the extraction solvent formulation was optimized and a combination of water, methanol and acetonitrile with a volume ratio of 3:1:1 was found to be optimal. The reproducibility of the method was satisfactory. Kendall tau-b rank correlation coefficients were equal to 1 (p<0.05) for 82% of the resolved peaks (up to 95% of the overall peak areas), indicating the good response correlation. Forty-four compounds including 9 saccharides, 9 alcohols, 9 amino acids, 16 organic acids and phosphoric acid were identified based on standard compounds. The method was successfully applied for profiling of tobacco leaves from Zimbabwe and Yunnan of China. Our result revealed that levels of saccharides and their derivatives including xylose, ribose, fructose, glucose, turanose, xylitol and glyceric acid were more abundant while sucrose, glucitol and D-gluconic acid were less abundant in tobacco leaves from Yunnan as compared to those from Zimbabwe. Amino acids such as L-alanine, L-tyrosine and L-threonine were found to be richer in Zimbabwe tobacco than in Yunnan tobacco.

Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is the most common form of chronic liver disease in most western countries. Current NAFLD diagnosis methods (e.g., liver biopsy analysis or imaging techniques) are poorly suited as tests for such a prevalent condition, from both a clinical and financial point of view. The present work aims to demonstrate the potential utility of serum metabolic profiling in defining phenotypic biomarkers that could be useful in NAFLD management. A parallel animal model/human NAFLD exploratory metabolomics approach was employed, using ultra performance liquid chromatography-mass spectrometry (UPLC-MS) to analyze 42 serum samples collected from nondiabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Multivariate statistical analysis of the data revealed a series of common biomarkers that were significantly altered in the NAFLD (GNMT-KO) subjects in comparison to their normal liver counterparts (WT). Many of the compounds observed could be associated with biochemical perturbations associated with liver dysfunction (e.g., reduced Creatine) and inflammation (e.g., eicosanoid signaling). This differential metabolic phenotyping approach may have a future role as a supplement for clinical decision making in NAFLD and in the adaption to more individualized treatment protocols.

Processing and analysis of GC/LC-MS-based metabolomics data

Data processing forms a crucial step in metabolomics studies, impacting upon data output quality, analysis potential and subsequent biological interpretation. This chapter provides an overview of data processing and analysis of GC-MS- and LC-MS-based metabolomics data. Data preprocessing steps are described, including the different software available for dealing with such complex datasets. Multivariate techniques for the subsequent analysis of metabolomics data, including principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA), are described with illustrations. Steps for the identification of potential biomarkers and the use of metabolite databases are also outlined.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

A component tracking algorithm for accelerated and improved liquid chromatography-mass spectrometry method development

A method for tracking of sample components during liquid chromatography-mass spectrometry (LC-MS) method development has been proposed. The method manages to, fully automatically and without user intervention, find the chromatographic peaks in the data sets, discriminate them to sample components and track them when the separation conditions have been changed. The algorithm utilises the resolution obtained from all considered data sets and has the ability to discriminate the non informative parts. The technique has a great sensitivity even in cases where a majority of the tracked components cannot easily be spotted by means of traditional total ion chromatogram (TIC) or base peak chromatogram (BPC) representations. The method was tested on an experimental sample using six different columns and an average of 79% of the suggested sample components could be successfully tracked at a minimum area of 0.05% of the main component in the sample. 66 components with 79-92% of the total suggested component area were able to be tracked between all data sets. The method could be used to rapidly investigate selectivity during different types of separation conditions.

Metabolomics study of human urinary metabolome modifications after intake of almond (Prunus dulcis (Mill.) D.A. Webb) skin polyphenols

Almond, as a part of the nut family, is an important source of biological compounds, and specifically, almond skins have been considered an important source of polyphenols, including flavan-3-ols and flavonols. Polyphenol metabolism may produce several classes of metabolites that could often be more biologically active than their dietary precursor and could also become a robust new biomarker of almond polyphenol intake. In order to study urinary metabolome modifications during the 24 h after a single dose of almond skin extract, 24 volunteers (n = 24), who followed a polyphenol-free diet for 48 h before and during the study, ingested a dietary supplement of almond skin phenolic compounds (n = 12) or a placebo (n = 12). Urine samples were collected before ((-2)-0 h) and after (0-2 h, 2-6 h, 6-10 h, and 10-24 h) the intake and were analyzed by liquid chromatography-mass spectrometry (LC-q-TOF) and multivariate statistical analysis (principal component analysis (PCA) and orthogonal projection to latent structures (OPLS)). Putative identification of relevant biomarkers revealed a total of 34 metabolites associated with the single dose of almond extract, including host and, in particular, microbiota metabolites. As far as we know, this is the first time that conjugates of hydroxyphenylvaleric, hydroxyphenylpropionic, and hydroxyphenylacetic acids have been identified in human samples after the consumption of flavan-3-ols through a metabolomic approach. The results showed that this non-targeted approach could provide new intake biomarkers, contributing to the development of the food metabolome as an important part of the human urinary metabolome.

Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping

Analytical errors caused by suboptimal performance of the chosen platform for a number of metabolites and instrumental drift are a major issue in large-scale metabolomics studies. Especially for MS-based methods, which are gaining common ground within metabolomics, it is difficult to control the analytical data quality without the availability of suitable labeled internal standards and calibration standards even within one laboratory. In this paper, we suggest a workflow for significant reduction of the analytical error using pooled calibration samples and multiple internal standard strategy. Between and within batch calibration techniques are applied and the analytical error is reduced significantly (increase of 25% of peaks with RSD lower than 20%) and does not hamper or interfere with statistical analysis of the final data.

An LC-MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption

Cocoa-phytochemicals have been related to the health-benefits of cocoa consumption. Metabolomics has been proposed as a powerful tool to characterize both the intake and the effects on the metabolism of dietary components. Human urine metabolome modifications after single cocoa intake were explored in a randomized, crossed, and controlled trial. After overnight fasting, 10 subjects consumed randomly either a single dose of cocoa powder with milk or water, or milk without cocoa. Urine samples were collected before the ingestion and at 0-6, 6-12, and 12-24-h after test-meals consumption. Samples were analyzed by HPLC-q-ToF, followed by multivariate data analysis. Results revealed an important effect on urinary metabolome during the 24 h after cocoa powder intake. These changes were not influenced by matrix as no global differences were found between cocoa powder consumption with milk or with water. Overall, 27 metabolites related to cocoa-phytochemicals, including alkaloid derivatives, polyphenol metabolites (both host and microbial metabolites) and processing-derived products such as diketopiperazines, were identified as the main contributors to the urinary modifications after cocoa powder intake. These results confirm that metabolomics will contribute to better characterization of the urinary metabolome in order to further explore the metabolism of phytochemicals and its relation with human health.

Comprehensive analytical strategy for biomarker identification based on liquid chromatography coupled to mass spectrometry and new candidate confirmation tools

A comprehensive analytical LC-MS(/MS) platform for low weight biomarkers molecule in biological fluids is described. Two complementary retention mechanisms were used in HPLC by optimizing the chromatographic conditions for a reversed-phase column and a hydrophilic interaction chromatography column. LC separation was coupled to mass spectrometry by using an electrospray ionization operating in positive polarity mode. This strategy enables us to correctly retain and separate hydrophobic as well as polar analytes. For that purpose artificial model study samples were generated with a mixture of 38 well characterized compounds likely to be present in biofluids. The set of compounds was used as a standard aqueous mixture or was spiked into urine at different concentration levels to investigate the capability of the LC-MS(/MS) platform to detect variations across biological samples. Unsupervised data analysis by principal component analysis was performed and followed by principal component variable grouping to find correlated variables. This tool allows us to distinguish three main groups whose variables belong to (a) background ions (found in all type of samples), (b) ions distinguishing urine samples from aqueous standard and blank samples, (c) ions related to the spiked compounds. Interpretation of these groups allows us to identify and eliminate isotopes, adducts, fragments, etc. and to generate a reduced list of m/z candidates. This list is then submitted to the prototype MZSearcher software tool which simultaneously searches several lists of potential metabolites extracted from metabolomics databases (e.g., KEGG, HMDB, etc) to propose biomarker candidates. Structural confirmation of these candidates was done off-line by fraction collection followed by nanoelectrospray infusion to provide high quality MS/MS data for spectral database queries.

Challenges in applying chemometrics to LC–MS-based global metabolite profile data

Metabolite profiling can provide insights into the metabolic status of complex living systems through the non-targeted analysis of metabolites in any biological sample. Metabolite profiling is complementary to genomics, transcriptomics and proteomics, and its applications span epidemiology, disease diagnosis, nutrition, pharmaceutical research, and toxicology. Metabolic phenotypes are a reflection of an organism’s environment, lifestyle, diet, gut microfloral composition and are also influenced by genetic factors, with important implications in genome-wide-association studies. Specialized analytical platforms, such as NMR spectroscopy and MS, are required to interrogate such metabolic complexity. The increased sophistication of such techniques has lead to a demand for improved data analysis approaches, including preprocessing and advanced chemometric techniques. This article discusses data generation, preprocessing, multivariate analysis and data interpretation for LC-MS-based metabolite profiling, focusing on challenges encountered and potential solutions.

Profiling of polar metabolites in biological extracts using diamond hydride-based aqueous normal phase chromatography

Highly polar metabolites, such as sugars and most amino acids are not retained by conventional RP LC columns. Without sufficient retention low concentration compounds are not detected due ion suppression and structural isomers are not resolved. In contrast, hydrophilic interaction chromatography (HILIC) and aqueous normal phase chromatography (ANP) retain compounds based on their hydrophilicity and therefore provides a means of separating highly polar compounds. Here, an ANP method based on the diamond hydride stationary phase is presented for profiling biological small molecules by LC. A rapid separation system based upon a fast gradient that delivers reproducible chromatography is presented. Approximately 1000 compounds were reproducibly detected in human urine samples and clear differences between these samples were identified. This chromatography was also applied to xylem fluid from soyabean (Glycine max) plants to which 400 compounds were detected. This method greatly increases the metabolite coverage over RP-only metabolite profiling in biological samples. We show that both forms of chromatography are necessary for untargeted comprehensive metabolite profiling and that the diamond hydride stationary phase provides a good option for polar metabolite analysis.