Increasing the Robustness of SIFT-MS Volatilome Fingerprinting by Introducing Notional Analyte Concentrations

Selected ion flow tube-mass spectrometry (SIFT-MS) is an analytical technique for volatile detection and quantification. SIFT-MS can be applied in a "white box" approach, measuring concentrations of target compounds, or as a "black box" fingerprinting technique, scanning all product ions during a full scan. Combining SIFT-MS full scan data acquired from multibatches or large-scale experiments remains problematic due to signal fluctuation over time. The standard approach of normalizing full scan data to the total signal intensity was insufficient. This study proposes a new approach to correct SIFT-MS fingerprinting data. In this concept, all of the product ions from a full scan are considered individual compounds for which notional concentrations can be calculated. Converting ion count rates into notional analyte concentrations accounts for any changes in the instrument parameters. The benefits of the proposed approach are demonstrated on three years of data from both multibatches and long-term experiments showing a significant reduction of system-induced fluctuations providing a better focus on the changes of interest.

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.

Metabolic deuterium oxide (D2O) labeling in quantitative omics studies: A tutorial review

Mass spectrometry (MS) is an invaluable tool for sensitive detection and characterization of individual biomolecules in omics studies. MS combined with stable isotope labeling enables the accurate and precise determination of quantitative changes occurring in biological samples. Metabolic isotope labeling, wherein isotopes are introduced into biomolecules through biosynthetic metabolism, is one of the main labeling strategies. Among the precursors employed in metabolic isotope labeling, deuterium oxide (D₂O) is cost-effective and easy to implement in any biological systems. This tutorial review aims to explain the basic principle of D₂O labeling and its applications in omics research. D₂O labeling incorporates D into stable C-H bonds in various biomolecules, including nucleotides, proteins, lipids, and carbohydrates. Typically, D₂O labeling is performed at low enrichment of 1%-10% D₂O, which causes subtle changes in the isotopic distribution of a biomolecule, instead of the complete separation between labeled and unlabeled samples in a mass spectrum. D₂O labeling has been employed in various omics studies to determine the metabolic flux, turnover rate, and relative quantification. Moreover, the advantages and challenges of D₂O labeling and its future prospects in quantitative omics are discussed. The economy, versatility, and convenience of D₂O labeling will be beneficial for the long-term omics studies for higher organisms.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

Multiple reaction monitoring mass spectrometry for the discovery of environmentally modulated proteins in an aquatic invertebrate sentinel species, Gammarus fossarum

Multiple reaction monitoring (MRM) mass spectrometry is emerging as a relevant tool for measuring customized molecular markers in freshwater sentinel species. While this technique is typically used for the validation of protein molecular markers preselected from shotgun experiments, recent gains of MRM multiplexing capacity offer new possibilities to conduct large-scale screening of animal proteomes. By combining the strength of active biomonitoring strategies and MRM technologies, this study aims to propose a new strategy for the discovery of candidate proteins that respond to environmental variability. For this purpose, 249 peptides derived from 147 proteins were monitored by MRM in 273 male gammarids caged in 56 environmental sites, representative of the diversity of French water bodies. A methodology is here proposed to identify a set of customized housekeeping peptides (HKPs) used to correct analytical batch effects and allow proper comparison of peptide levels in gammarids. A comparative analysis performed on HKPs-normalized data resulted in the identification of peptides highly modulated in the environment and derived from proteins likely involved in the environmental stress response. Overall, this study proposes a breakthrough approach to screen and identify potential proteins responding to relevant environmental conditions in sentinel species.

An Untargeted Metabolomics Approach on Carfilzomib-Induced Nephrotoxicity

BACKGROUND

Carfilzomib (Cfz) is an anti-cancer drug related to cardiorenal adverse events, with cardiovascular and renal complications limiting its clinical use. Despite the important progress concerning the discovery of the underlying causes of Cfz-induced nephrotoxicity, the molecular/biochemical background is still not well clarified. Furthermore, the number of metabolomics-based studies concerning Cfz-induced nephrotoxicity is limited.

METHODS

A metabolomics UPLC-HRMS-DIA methodology was applied to three bio-sample types i.e., plasma, kidney, and urine, obtained from two groups of mice, namely (i) Cfz (8 mg Cfz/ kg) and (ii) Control (0.9% NaCl) (n = 6 per group). Statistical analysis, involving univariate and multivariate tools, was applied for biomarker detection. Furthermore, a sub-study was developed, aiming to estimate metabolites' correlation among bio-samples, and to enlighten potential mechanisms.

RESULTS

Cfz mostly affects the kidneys and urine metabolome. Fifty-four statistically important metabolites were discovered, and some of them have already been related to renal diseases. Furthermore, the correlations between bio-samples revealed patterns of metabolome alterations due to Cfz.

CONCLUSIONS

Cfz causes metabolite retention in kidney and dysregulates (up and down) several metabolites associated with the occurrence of inflammation and oxidative stress.

Recent advances in proteomics and metabolomics in plants

Over the past decade, systems biology and plant-omics have increasingly become the main stream in plant biology research. New developments in mass spectrometry and bioinformatics tools, and methodological schema to integrate multi-omics data have leveraged recent advances in proteomics and metabolomics. These progresses are driving a rapid evolution in the field of plant research, greatly facilitating our understanding of the mechanistic aspects of plant metabolisms and the interactions of plants with their external environment. Here, we review the recent progresses in MS-based proteomics and metabolomics tools and workflows with a special focus on their applications to plant biology research using several case studies related to mechanistic understanding of stress response, gene/protein function characterization, metabolic and signaling pathways exploration, and natural product discovery. We also present a projection concerning future perspectives in MS-based proteomics and metabolomics development including their applications to and challenges for system biology. This review is intended to provide readers with an overview of how advanced MS technology, and integrated application of proteomics and metabolomics can be used to advance plant system biology research.

Metabolomics reveals sex-specific pathways associated with changes in adiposity and muscle mass in a cohort of Mexican adolescents

BACKGROUND

Alterations in body composition (BC) during adolescence relates to future metabolic risk, yet underlying mechanisms remain unclear.

OBJECTIVES

To assess the association between the metabolome with changes in adiposity (body mass index [BMI], waist circumference [WC], triceps skinfold [TS], fat percentage [BF%]) and muscle mass (MM).

METHODS

In Mexican adolescents (n = 352), untargeted serum metabolomics was profiled at baseline. and data were reduced by pairing hierarchical clustering with confirmatory factor analysis, yielding 30 clusters with 51 singleton metabolites. At the baseline and follow-up visits (1.6-3.5 years apart), anthropometry was collected to identify associations between baseline metabolite clusters and change in BC (∆) using seemingly unrelated and linear regression.

RESULTS

Between visits, MM increased in boys and adiposity increased in girls. Sex differences were observed between metabolite clusters and changes in BC. In boys, aromatic amino acids (AAA), branched chain amino acids (BCAA) and fatty acid oxidation metabolites were associated with increases in ∆BMI, and ∆BF%. Phospholipids were associated with decreases in ∆TS and ∆MM. Negative associations were observed for ∆MM in boys with a cluster including AAA and BCAA, whereas positive associations were found for a cluster containing tryptophan metabolites. Few associations were observed between metabolites and BC change in girls, with one cluster comprising methionine, proline and lipids associated with decreases in ∆BMI, ∆WC and ∆MM.

CONCLUSION

Sex-specific associations between the metabolome and change in BC were observed, highlighting metabolic pathways underlying adolescent physical growth.

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.

Maternal and neonatal one-carbon metabolites and the epigenome-wide infant response

Maternal prenatal status, as encapsulated by that to which a mother is exposed through diet and environment, is a key determinant of offspring health and disease. Alterations in DNA methylation (DNAm) may be a mechanism through which suboptimal prenatal conditions confer disease risk later in life. One-carbon metabolism (OCM) is critical to both fetal development and in supplying methyl donors needed for DNAm. Plasma concentrations of one-carbon metabolites across maternal first trimester (M1), maternal term (M3), and infant cord blood (CB) at birth were tested for association with DNAm patterns in CB from the Michigan Mother and Infant Pairs (MMIP) pregnancy cohort. The Illumina Infinium MethylationEPIC BeadChip was used to quantitatively evaluate DNAm across the epigenome. Global and single-site DNAm and metabolite models were adjusted for infant sex, estimated cell type proportions, and batch as covariates. Change in mean metabolite concentration across pregnancy (M1 to M3) was significantly different for S-adenosylhomocysteine (SAH), S-adenosylmethionine (SAM), betaine, and choline. Both M1 SAH and CB SAH were significantly associated with the global distribution of DNAm in CB, with indications of a shift toward less methylation. M3 SAH and CB SAH also displayed significant associations with locus-specific DNAm in infant CB (FDR<0.05). Our findings underscore the role of maternal one-carbon metabolites in shifting the global DNAm pattern in CB and emphasizes the need to closely evaluate how dietary status influences cellular methylation potential and ultimately offspring health.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Stepwise Covariance-Free Common Principal Components (CF-CPC) With an Application to Neuroscience

Finding the common principal component (CPC) for ultra-high dimensional data is a multivariate technique used to discover the latent structure of covariance matrices of shared variables measured in two or more k conditions. Common eigenvectors are assumed for the covariance matrix of all conditions, only the eigenvalues being specific to each condition. Stepwise CPC computes a limited number of these CPCs, as the name indicates, sequentially and is, therefore, less time-consuming. This method becomes unfeasible when the number of variables p is ultra-high since storing k covariance matrices requires O(k p 2) memory. Many dimensionality reduction algorithms have been improved to avoid explicit covariance calculation and storage (covariance-free). Here we propose a covariance-free stepwise CPC, which only requires O(k n) memory, where n is the total number of examples. Thus for n < < p, the new algorithm shows apparent advantages. It computes components quickly, with low consumption of machine resources. We validate our method CFCPC with the classical Iris data. We then show that CFCPC allows extracting the shared anatomical structure of EEG and MEG source spectra across a frequency range of 0.01-40 Hz.

Longitudinal metabolomics of human plasma reveals prognostic markers of COVID-19 disease severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determines disease severity. Through analysis of longitudinal samples, we confirm that most of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

Capillary Electrophoresis Mass Spectrometry-Based Metabolomics of Plasma Samples from Healthy Subjects in a Cross-Sectional Japanese Population Study

For large-scale metabolomics, such as in cohort studies, normalization protocols using quality control (QC) samples have been established when using data from gas chromatography and liquid chromatography coupled to mass spectrometry. However, normalization protocols have not been established for capillary electrophoresis-mass spectrometry metabolomics. In this study, we performed metabolome analysis of 314 human plasma samples using capillary electrophoresis-mass spectrometry. QC samples were analyzed every 10 samples. The results of principal component analysis for the metabolome data from only the QC samples showed variations caused by capillary replacement in the first principal component score and linear variation with continuous measurement in the second principal component score. Correlation analysis between diagnostic blood tests and plasma metabolites normalized by the QC samples was performed for samples from 188 healthy subjects who participated in a Japanese population study. Five highly correlated pairs were identified, including two previously unidentified pairs in normal healthy subjects of blood urea nitrogen and guanidinosuccinic acid, and gamma-glutamyl transferase and cysteine glutathione disulfide. These results confirmed the validity of normalization protocols in capillary electrophoresis-mass spectrometry using large-scale metabolomics and comprehensive analysis.

Early life stress exposure associated with reduced polyunsaturated-containing lipids in low-income children

BACKGROUND

Psychosocial stress in early childhood is associated with adult obesity and cardiometabolic disease. The association of psychosocial stress with the metabolome in childhood is unknown.

METHOD

Low-income children (n = 28, mean age 1.8 years), recruited from the community, participated. Psychosocial stress was measured by diurnal salivary cortisol (cortisol intercept and slope) and by mother-reported chaos in the home using the Confusion, Hubbub, and Order Scale (CHAOS). At mean age 6.1 years, anthropometry was collected and fasting metabolites measured using an untargeted metabolomics and shotgun lipidomics platform.

RESULTS

Cortisol slope was inversely associated with fatty acid (FA) 20:3, FA 20:4 and polyunsaturated fatty acids (PUFA) metabolites. A higher CHAOS score was associated with lower very long-chain PUFA metabolites and a trend towards lower long-chain PUFA containing triglycerides.

CONCLUSIONS

Psychosocial stress in early childhood, measured with both biological markers and parent report, was associated with lower PUFAs later in childhood. Future work should examine potential mechanisms of association, including dietary intake or direct effects on polyunsaturated fatty acid levels or metabolism.

IMPACT

In this longitudinal study, the key message is that diurnal cortisol patterns and greater parent-reported psychosocial stress exposure in early childhood are associated with lower plasma polyunsaturated fatty acid containing lipids 5 years later, potentially indicating altered dietary intake or metabolism associated with psychosocial stress. Untargeted metabolomics and lipidomics can be used to assess changes in metabolism response to psychosocial stress. Stress exposure in early childhood may be associated with the future metabolome. Future work should examine potential pathways of association, including dietary intake and direct effects on metabolism.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Longitudinal Metabolomics of Human Plasma Reveals Robust Prognostic Markers of COVID-19 Disease Severity

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that scarce medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we performed untargeted metabolomics profiling of 341 patients with plasma samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we then built a predictive model of disease severity. We determined that the levels of 25 metabolites measured at the time of hospital admission successfully predict future disease severity. Through analysis of longitudinal samples, we confirmed that these prognostic markers are directly related to disease progression and that their levels are restored to baseline upon disease recovery. Finally, we validated that these metabolites are also altered in a hamster model of COVID-19. Our results indicate that metabolic changes associated with COVID-19 severity can be effectively used to stratify patients and inform resource allocation during the pandemic.

Development of mass spectrometry-based relatively quantitative targeted method for amino acids and neurotransmitters: Applications in the diagnosis of major depression

Targeted metabolomics analysis based on triple quadrupole (QQQ) MS coupled with multiple reaction monitoring mode (MRM) is the gold standard for metabolite quantification and it is widely applied in metabolomics. However, standard compounds for each metabolite and the corresponding analogs are necessary for quantitative measurements. To identify the differentially present metabolites in various groups, determining the relative concentration of metabolites would be more efficient than accurate quantification. In this study, a relatively quantitative targeted method was established for metabonomics research, on the basis of hydrophilic interaction liquid chromatography (HILIC)/QQQ MS operated in MRM mode. The quality control-base random forest signal correction algorithm (QC-RFSC algorithm) was applied for quality control instead of the internal standard method. High quality relative quantification was achieved without internal standards, and integrated peak areas were successfully used for statistical and pathway analyses. Amino acids and neurotransmitters (dopamine, kynurenic acid, urocanic acid, tryptophan, kynurenine, tyrosine, valine, threonine, serine, alanine, glycine, glutamine, citrulline, GABA, glutamate, aspartate, arginine, ornithine and histidine) in serum samples were simultaneously determined with the newly developed method. To demonstrate the applicability of this method in large-scale analyses, we analyzed the above metabolites in serum from patients with major depression. The serum levels of glutamate, aspartate, threonine, glycine and alanine were significantly higher, and those of citrulline, kynurenic acid and urocanic acid were significantly lower, in patients with major depression than in controls. This is the first report of the difference in urocanic acid, a compound reported to improve glutamate biosynthesis and release in the central nervous system, between healthy controls and patients with major depression.

Maternal lipid levels across pregnancy impact the umbilical cord blood lipidome and infant birth weight

Major alterations in metabolism occur during pregnancy enabling the mother to provide adequate nutrients to support infant development, affecting birth weight (BW) and potentially long-term risk of obesity and cardiometabolic disease. We classified dynamic changes in the maternal lipidome during pregnancy and identified lipids associated with Fenton BW z-score and the umbilical cord blood (CB) lipidome. Lipidomics was performed on first trimester maternal plasma (M1), delivery maternal plasma (M3), and CB plasma in 106 mother-infant dyads. Shifts in the maternal and CB lipidome were consistent with the selective transport of long-chain polyunsaturated fatty acids (PUFA) as well as lysophosphatidylcholine (LysoPC) and lysophosphatidylethanolamine (LysoPE) species into CB. Partial correlation networks demonstrated fluctuations in correlations between lipid groups at M1, M3, and CB, signifying differences in lipid metabolism. Using linear models, LysoPC and LysoPE groups in CB were positively associated with BW. M1 PUFA containing triglycerides (TG) and phospholipids were correlated with CB LysoPC and LysoPE species and total CB polyunsaturated TGs. These results indicate that early gestational maternal lipid levels influence the CB lipidome and its relationship with BW, suggesting an opportunity to modulate maternal diet and improve long-term offspring cardiometabolic health.

Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.

Mitochondrial Nutrient Utilization Underlying the Association Between Metabolites and Insulin Resistance in Adolescents

CONTEXT

A person's intrinsic metabolism, reflected in the metabolome, may describe the relationship between nutrient intake and metabolic health.

OBJECTIVES

Untargeted metabolomics was used to identify metabolites associated with metabolic health. Path analysis classified how habitual dietary intake influences body mass index z-score (BMIz) and insulin resistance (IR) through changes in the metabolome.

DESIGN

Data on anthropometry, fasting metabolites, C-peptide, and dietary intake were collected from 108 girls and 98 boys aged 8 to 14 years. Sex-stratified linear regression identified metabolites associated with BMIz and homeostatic model assessment of IR using C-peptide (HOMA-CP), accounting for puberty, age, and muscle and fat area. Path analysis identified clusters of metabolites that underlie the relationship between energy-adjusted macronutrient intake with BMIz and HOMA-CP.

RESULTS

Metabolites associated with BMIz include positive associations with diglycerides among girls and positive associations with branched chain and aromatic amino acids in boys. Intermediates in fatty acid metabolism, including medium-chain acylcarnitines (AC), were inversely associated with HOMA-CP. Carbohydrate intake is positively associated with HOMA-CP through decreases in levels of AC, products of β-oxidation. Approaching significance, fat intake is positively associated with HOMA-CP through increases in levels of dicarboxylic fatty acids, products of omega-oxidation.

CONCLUSIONS

This cross-sectional analysis suggests that IR in children is associated with reduced fatty acid oxidation capacity. When consuming more grams of fat, there is evidence for increased extramitochondrial fatty acid metabolism, while higher carbohydrate intake appears to lead to decreases in intermediates of β-oxidation. Thus, biomarkers of IR and mitochondrial oxidative capacity may depend on macronutrient intake.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Amitriptyline at an Environmentally Relevant Concentration Alters the Profile of Metabolites Beyond Monoamines in Gilt-Head Bream

The antidepressant amitriptyline is a widely used selective serotonin reuptake inhibitor that is found in the aquatic environment. The present study investigates alterations in the brain and the liver metabolome of gilt-head bream (Sparus aurata) after exposure at an environmentally relevant concentration (0.2 µg/L) of amitriptyline for 7 d. Analysis of variance-simultaneous component analysis is used to identify metabolites that distinguish exposed from control animals. Overall, alterations in lipid metabolism suggest the occurrence of oxidative stress in both the brain and the liver-a common adverse effect of xenobiotics. However, alterations in the amino acid arginine are also observed. These are likely related to the nitric oxide system that is known to be associated with the mechanism of action of antidepressants. In addition, changes in asparagine and methionine levels in the brain and pantothenate, uric acid, and formylisoglutamine/N-formimino-L-glutamate levels in the liver could indicate variation of amino acid metabolism in both tissues; and the perturbation of glutamate in the liver implies that the energy metabolism is also affected. These results reveal that environmentally relevant concentrations of amitriptyline perturb a fraction of the metabolome that is not typically associated with antidepressant exposure in fish. Environ Toxicol Chem 2019;00:1-13. © 2019 SETAC.

Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data

Metabolomics is the science of characterizing and quantifying small molecule metabolites in biological systems. These metabolites give organisms their biochemical characteristics, providing a link between genotype, environment, and phenotype. With these opportunities also come data challenges, such as compound annotation, missing values, and batch effects. We present the steps of a general pipeline to process untargeted mass spectrometry data to alleviate the latter two challenges. We assume to have a matrix with metabolite abundances, with metabolites in rows and samples in columns. The steps in the pipeline include summarizing technical replicates (if available), filtering, imputing, transforming, and normalizing the data. In each of these steps, a method and parameters should be chosen based on assumptions one is willing to make, the question of interest, and diagnostic tools. Besides giving a general pipeline that can be adapted by the reader, our goal is to review diagnostic tools and criteria that are helpful when making decisions in each step of the pipeline and assessing the effectiveness of normalization and batch correction. We conclude by giving a list of useful packages and discuss some alternative approaches that might be more appropriate for the reader's data.

Population-Level Analysis to Determine Parameters That Drive Variation in the Plasma Metabolite Profiles

The plasma metabolome is associated with multiple phenotypes and diseases. However, a systematic study investigating clinical determinants that control the metabolome has not yet been conducted. In the present study, therefore, we aimed to identify the major determinants of the plasma metabolite profile. We used ultra-high performance liquid chromatography (UHPLC) coupled to quadrupole time of flight mass spectrometry (QTOF-MS) to determine 106 metabolites in plasma samples from 2503 subjects in a cross-sectional study. We investigated the correlation structure of the metabolite profiles and generated uncorrelated metabolite factors using principal component analysis (PCA) and varimax rotation. Finally, we investigated associations between these factors and 34 clinical covariates. Our results suggest that liver function, followed by kidney function and insulin resistance show the strongest associations with the plasma metabolite profile. The association of specific phenotypes with several components may suggest multiple independent metabolic mechanisms, which is further supported by the composition of the associated factors.

Non-targeted metabolomics reveals alterations in liver and plasma of gilt-head bream exposed to oxybenzone

The extensive use of the organic UV filter oxybenzone has led to its ubiquitous occurrence in the aquatic environment, causing an ecotoxicological risk to biota. Although some studies reported adverse effects, such as reproductive toxicity, further research needs to be done in order to assess its molecular effects and mechanism of action. Therefore, in the present work, we investigated metabolic perturbations in juvenile gilt-head bream (Sparus aurata) exposed over 14 days via the water to oxybenzone (50 mg/L). The non-targeted analysis of brain, liver and plasma extracts was performed by means of UHPLC-qOrbitrap MS in positive and negative modes with both C18 and HILIC separation. Although there was no mortality or alterations in general physiological parameters during the experiment, and the metabolic profile of brain was not affected, the results of this study showed that oxybenzone could perturb both liver and plasma metabolome. The pathway enrichment suggested that different pathways in lipid metabolism (fatty acid elongation, α-linolenic acid metabolism, biosynthesis of unsaturated fatty acids and fatty acid metabolism) were significantly altered, as well as metabolites involved in phenylalanine and tyrosine metabolism. Overall, these changes are signs of possible oxidative stress and energy metabolism modification. Therefore, this research indicates that oxybenzone has adverse effects beyond the commonly studied hormonal activity, and demonstrates the sensitivity of metabolomics to assess molecular-level effects of emerging contaminants.

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful "tips and tricks" along the analytical workflow.

Metabolomics analysis reveals that elevated atmospheric CO2 alleviates drought stress in cucumber seedling leaves

Elevated atmospheric CO₂ alleviates moderate to severe drought stresses at physiological level in cucumber. To investigate the underlying metabolic mechanisms, cucumber seedlings were treated with two [CO₂] and three water treatments combinations, and their leaves were analyzed using a non-targeted metabolomics approach. The results showed that elevated [CO₂] changed 79 differential metabolites which were mainly associated with alanine, aspartate and glutamate metabolism; arginine and proline metabolism; TCA cycle; and glycerophospholipid metabolism under moderate drought stress. Moreover, elevated [CO₂] promoted the accumulation of secondary metabolites; including isoferulic acid, m-coumaric acid and salicyluric acid. Under severe drought stress, elevated [CO₂] changed 26 differential metabolites which mainly involved in alanine, aspartate and glutamate metabolism; pyruvate metabolism; arginine and proline metabolism; glyoxylate and dicarboxylate metabolism; cysteine and methionine metabolism; starch and sucrose metabolism; glycolysis or gluconeogenesis; and pyrimidine metabolism. In addition, elevated [CO₂] accumulated carbohydrates, 1,2,3-trihydroxybenzene, pyrocatechol, glutamate, and l-gulonolactone, to allow adaption to severe drought. In conclusion, the metabolites and metabolic pathways associated with the alleviation of drought stresses by elevated [CO₂] were different according to the level of drought stress. Our results may provide a theoretical basis for CO₂ fertilization and application of exogenous metabolites to enhance drought tolerance of cucumber.

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.

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

Instrumental drift removal in GC-MS data for breath analysis: the short-term and long-term temporal validation of putative biomarkers for COPD

Breath analysis holds the promise of a non-invasive technique for the diagnosis of diverse respiratory conditions including chronic obstructive pulmonary disease (COPD) and lung cancer. Breath contains small metabolites that may be putative biomarkers of these conditions. However, the discovery of reliable biomarkers is a considerable challenge in the presence of both clinical and instrumental confounding factors. Among the latter, instrumental time drifts are highly relevant, as since question the short and long-term validity of predictive models. In this work we present a methodology to counter instrumental drifts using information from interleaved blanks for a case study of GC-MS data from breath samples. The proposed method includes feature filtering, and additive, multiplicative and multivariate drift corrections, the latter being based on component correction. Biomarker discovery was based on genetic algorithms in a filter configuration using Fisher's ratio computed in the partial least squares-discriminant analysis subspace as a figure of merit. Using our protocol, we have been able to find nine peaks that provide a statistically significant area under the ROC curve of 0.75 for COPD discrimination. The method developed has been successfully validated using blind samples in short-term temporal validation. However, the attempt to use this model for patient screening six months later was not successful. This negative result highlights the importance of increasing validation rigor when reporting biomarker discovery results.

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography - mass spectrometry (LC-MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards

With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm-RRmix-within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.

Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women part 2

BACKGROUND

Gestational diabetes mellitus (GDM) is a pathological state of glucose intolerance associated with adverse pregnancy outcomes and an increased risk of developing maternal type 2 diabetes later in life. The mechanisms underlying GDM development are not fully understood. We examined the pathophysiology of GDM through comprehensive metabolic profiling of maternal urine, using participants from a longitudinal cohort of normal pregnancies and pregnancies complicated by GDM.

METHODS

Based on ultra-performance liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry, an untargeted metabolomics study was performed to explore the differences in the urinary metabolome of GDM cases and healthy controls over the course of pregnancy. Multilevel statistical approaches were employed to address the complex metabolomic data obtained from a longitudinal cohort.

RESULTS

The results indicated that tryptophan and purine metabolism was associated with GDM. The tryptophan-kynurenine pathway was activated in the GDM subjects before placental hormones or the fetoplacental unit could have produced any physiological effect. Hypoxanthine, xanthine, xanthosine, and 1-methylhypoxanthine were all elevated in the urine metabolome of subjects with GDM. Catabolism of purine nucleosides leads ultimately to the production of uric acid, which discriminated the subjects with GDM from controls.

CONCLUSIONS

The results support the notion that GDM may be a predisposed condition, or prediabetic state, which is manifested during pregnancy. This challenges the conventional view of the pathogenesis of GDM, which assumes placental hormones are the major causes of insulin resistance in GDM.

Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data

Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ∼3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.

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.

Large-scale untargeted LC-MS metabolomics data correction using between-batch feature alignment and cluster-based within-batch signal intensity drift correction

INTRODUCTION

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used technique in untargeted metabolomics owing to broad coverage of metabolites, high sensitivity and simple sample preparation. However, data generated from multiple batches are affected by measurement errors inherent to alterations in signal intensity, drift in mass accuracy and retention times between samples both within and between batches. These measurement errors reduce repeatability and reproducibility and may thus decrease the power to detect biological responses and obscure interpretation.

OBJECTIVE

Our aim was to develop procedures to address and correct for within- and between-batch variability in processing multiple-batch untargeted LC-MS metabolomics data to increase their quality.

METHODS

Algorithms were developed for: (i) alignment and merging of features that are systematically misaligned between batches, through aggregating feature presence/missingness on batch level and combining similar features orthogonally present between batches; and (ii) within-batch drift correction using a cluster-based approach that allows multiple drift patterns within batch. Furthermore, a heuristic criterion was developed for the feature-wise choice of reference-based or population-based between-batch normalisation.

RESULTS

In authentic data, between-batch alignment resulted in picking 15 % more features and deconvoluting 15 % of features previously erroneously aligned. Within-batch correction provided a decrease in median quality control feature coefficient of variation from 20.5 to 15.1 %. Algorithms are open source and available as an R package ('batchCorr').

CONCLUSIONS

The developed procedures provide unbiased measures of improved data quality, with implications for improved data analysis. Although developed for LC-MS based metabolomics, these methods are generic and can be applied to other data suffering from similar limitations.

Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future

Metabolomics, a systems biology discipline representing analysis of known and unknown pathways of metabolism, has grown tremendously over the past 20 years. Because of its comprehensive nature, metabolomics requires careful consideration of the question(s) being asked, the scale needed to answer the question(s), collection and storage of the sample specimens, methods for extraction of the metabolites from biological matrices, the analytical method(s) to be employed and the quality control of the analyses, how collected data are correlated, the statistical methods to determine metabolites undergoing significant change, putative identification of metabolites and the use of stable isotopes to aid in verifying metabolite identity and establishing pathway connections and fluxes. This second part of a comprehensive description of the methods of metabolomics focuses on data analysis, emerging methods in metabolomics and the future of this discipline. Copyright © 2016 John Wiley & Sons, Ltd.

Improved batch correction in untargeted MS-based metabolomics

INTRODUCTION

Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account.

OBJECTIVES

This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.

METHODS

Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC-MS and GC-MS data sets of samples from Arabidopsis thaliana.

RESULTS

The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects-replacing them with very small numbers such as zero seems the worst of the approaches considered.

CONCLUSION

The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.