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

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

[Alignment method for metabolite chromatographic peaks using an N-acyl glycine retention index system]

Peak alignment is a crucial data-processing step in untargeted metabolomics analysis that aims to integrate metabolite data from multiple liquid chromatography-mass spectrometry (LC-MS) batches for enhanced comparability and reliability. However, slight variations in the chromatographic separation conditions can result in retention time (RT) shifts between consecutive analyses, adversely affecting peak alignment accuracy. In this study, we present a retention index (RI)-based chromatographic peak-shift correction (CPSC) strategy to address RT shifts and align chromatographic peaks for metabolomics studies. A series of N-acyl glycine homologues (C2-C23) was synthesized as calibrants, and an LC RI system was established. This system effectively corrected RT shifts arising from variations in flow rate, gradient elution, instrument systems, and chromatographic columns. Leveraging the RI system, we successfully adjusted the RT of raw data to mitigate RT shifts and then implemented the Joint Aligner algorithm for peak alignment. We assessed the accuracy of the RI-based CPSC strategy using pooled human fecal samples as a test model. Notably, the application of the RI-based CPSC strategy to a long-term dataset spanning 157 d as an illustration revealed a significant enhancement in peak alignment accuracy from 15.5% to 80.9%, indicating its ability to substantially improve peak-alignment precision in multibatch LC-MS analyses.

Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a rare and fatal vascular disease with heterogeneous clinical manifestations. To date, molecular determinants underlying the development of PAH and related outcomes remain poorly understood. Herein, we identify pulmonary primary oxysterol and bile acid synthesis (PPOBAS) as a previously unrecognized pathway central to PAH pathophysiology. Mass spectrometry analysis of 2,756 individuals across five independent studies revealed 51 distinct circulating metabolites that predicted PAH-related mortality and were enriched within the PPOBAS pathway. Across independent single-center PAH studies, PPOBAS pathway metabolites were also associated with multiple cardiopulmonary measures of PAH-specific pathophysiology. Furthermore, PPOBAS metabolites were found to be increased in human and rodent PAH lung tissue and specifically produced by pulmonary endothelial cells, consistent with pulmonary origin. Finally, a poly-metabolite risk score comprising 13 PPOBAS molecules was found to not only predict PAH-related mortality but also outperform current clinical risk scores. This work identifies PPOBAS as specifically altered within PAH and establishes needed prognostic biomarkers for guiding therapy in PAH.

Eicosanoid and eicosanoid-related inflammatory mediators and exercise intolerance in heart failure with preserved ejection fraction

Systemic inflammation has been implicated in the pathobiology of heart failure with preserved ejection fraction (HFpEF). Here, we examine the association of upstream mediators of inflammation as ascertained by fatty-acid derived eicosanoid and eicosanoid-related metabolites with HFpEF status and exercise manifestations of HFpEF. Among 510 participants with chronic dyspnea and preserved LVEF who underwent invasive cardiopulmonary exercise testing, we find that 70 of 890 eicosanoid and related metabolites are associated with HFpEF status, including 17 named and 53 putative eicosanoids (FDR q-value < 0.1). Prostaglandin (15R-PGF2α, 11ß-dhk-PGF2α) and linoleic acid derivatives (12,13 EpOME) are associated with greater odds of HFpEF, while epoxides (8(9)-EpETE), docosanoids (13,14-DiHDPA), and oxylipins (12-OPDA) are associated with lower odds of HFpEF. Among 70 metabolites, 18 are associated with future development of heart failure in the community. Pro- and anti-inflammatory eicosanoid and related metabolites may contribute to the pathogenesis of HFpEF and serve as potential targets for intervention.

Novel Peak Shift Correction Method Based on the Retention Index for Peak Alignment in Untargeted Metabolomics

Peak alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based large-scale untargeted metabolomics workflows, as it enables the integration of metabolite peaks across multiple samples, which is essential for accurate data interpretation. Slight differences or fluctuations in chromatographic separation conditions, however, can cause the chromatographic retention time (RT) shift between consecutive analyses, ultimately affecting the accuracy of peak alignment between samples. Here, we introduce a novel RT shift correction method based on the retention index (RI) and apply it to peak alignment. We synthesized a series of N-acyl glycine (C2-C23) homologues via the amidation reaction between glycine with normal saturated fatty acids (C2-C23) as calibrants able to respond proficiently in both mass spectrometric positive- and negative-ion modes. Using these calibrants, we established an N-acyl glycine RI system. This RI system is capable of covering a broad chromatographic space and addressing chromatographic RT shift caused by variations in flow rate, gradient elution, instrument systems, and LC separation columns. Moreover, based on the RI system, we developed a peak shift correction model to enhance peak alignment accuracy. Applying the model resulted in a significant improvement in the accuracy of peak alignment from 15.5 to 80.9% across long-term data spanning a period of 157 days. To facilitate practical application, we developed a Python-based program, which is freely available at https://github.com/WHU-Fenglab/RI-based-CPSC.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

INTRODUCTION

Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis.

OBJECTIVES

Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data.

METHODS

A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively.

RESULTS

The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB.

CONCLUSION

Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer.

Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data

Emerging technologies now allow for mass spectrometry-based profiling of thousands of small molecule metabolites ('metabolomics') in an increasing number of biosamples. While offering great promise for insight into the pathogenesis of human disease, standard approaches have not yet been established for statistically analyzing increasingly complex, high-dimensional human metabolomics data in relation to clinical phenotypes, including disease outcomes. To determine optimal approaches for analysis, we formally compare traditional and newer statistical learning methods across a range of metabolomics dataset types. In simulated and experimental metabolomics data derived from large population-based human cohorts, we observe that with an increasing number of study subjects, univariate compared to multivariate methods result in an apparently higher false discovery rate as represented by substantial correlation between metabolites directly associated with the outcome and metabolites not associated with the outcome. Although the higher frequency of such associations would not be considered false in the strict statistical sense, it may be considered biologically less informative. In scenarios wherein the number of assayed metabolites increases, as in measures of nontargeted versus targeted metabolomics, multivariate methods performed especially favorably across a range of statistical operating characteristics. In nontargeted metabolomics datasets that included thousands of metabolite measures, sparse multivariate models demonstrated greater selectivity and lower potential for spurious relationships. When the number of metabolites was similar to or exceeded the number of study subjects, as is common with nontargeted metabolomics analysis of relatively small cohorts, sparse multivariate models exhibited the most-robust statistical power with more consistent results. These findings have important implications for metabolomics analysis in human disease.

DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

Metabolomic profiles in relation to benchmark polycyclic aromatic compounds (PACs) and trace elements in two seabird species from Arctic Canada

While exposure of birds to oil-related contaminants has been documented, the related adverse effects this exposure has on Arctic marine birds remain unexplored. Metabolomics can play an important role to explore biologically relevant metabolite biomarkers in relation to different stressors, even at benchmark levels of contamination. The aim of this study was to characterize the metabolomics profiles in relation to polycyclic aromatic compounds (PACs) and trace elements in the liver of two seabird species in the Canadian Arctic. In July 2018, black guillemots (Cepphus grylle) and thick-billed murres (Uria lomvia) were collected by hunters from a region where natural oil seeps occur in the seabed near Qikiqtarjuaq, Nunavut, Canada. A total of 121 metabolites were identified in liver tissue samples using reversed phase and hydrophilic interaction liquid chromatography coupled to high resolution mass spectrometry platforms to detect non-polar and polar metabolites, respectively. Sixty-nine metabolites showed excellent repeatability and linearity and were used to examine possible effects of oil-related contaminants exposure (PACs and trace elements). Metabolites including 3-hydroxy anthranilic acid, adenine, adenosine, adenosine mono-phosphate, ascorbic acid, butyrylcarnitine, cholic acid, guanosine, guanosine mono-phosphate, inosine, norepinephrine and threonine showed significant differences (more than two fold) between the two species. Elevated adenine and adenosine, along with decreased reduced/oxidized glutathione ratio, highlighted the potential for oxidative stress in murres. Lipid peroxidation and superoxide dismutase activity assays also confirmed these metabolomic findings. These results will help to characterize the baseline metabolomic profiles of Arctic seabird species with different foraging behaviour and trace element burden.

Photochemical (UV-vis/H2O2) degradation of carotenoids: Kinetics and molecular end products

Constraining the formation mechanisms of organic matter that persists in aquatic reservoirs is important for determining the reactivity and fate of carbon and nutrients in these environments. Recent studies have linked dissolved organic matter (DOM) accumulating in the ocean to linear terpenoid structures, and carotenoid degradation products have been proposed as potential precursors. The prevalence of reactive oxygen species in aquatic environments and their potential to be quenched by carotenoids led us to examine radical-assisted photochemical degradation of carotenoids as a potential mechanism for DOM formation and transformation. Experiments were conducted with aggregates of β-carotene, astaxanthin, fucoxanthin and meso-zeaxanthin in THF:H₂O under solar light irradiation assisted by hydrogen peroxide (UV-Vis/H₂O₂). Based on the fine structure of UV-Vis spectra, it was determined that β-carotene and meso-zeaxanthin formed J-type aggregates in experimental solutions, while astaxanthin and fucoxanthin formed H2-type aggregates, consistent with their structural characteristics. All carotenoids degraded under the combined influence of photolysis and OH scavenging, with fucoxanthin exhibiting the fastest degradation kinetics (k_PO = 3.69 10^-3 s^-1) and meso-zeaxanthin the slowest (k_PO = 4.37 10^-4 s^-1). The major degradation products detected by electrospray ionization (ESI) tandem mass spectrometry (MS/MS) were apo-aldehydes and apo-ketones, with the latter tending to accumulate, but epoxidation of the carotenoids also took place, and longer irradiation times resulted in lower molecular weight products. Reaction kinetics and accumulating carotenoid oxidation products identified in this study provide potential formation mechanisms and biomarkers for examining DOM cycling.

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.

Nontargeted mass spectrometry of dried blood spots for interrogation of the human circulating metabolome

Advances in high-resolution, nontargeted mass spectrometry allow for the simultaneous measure of thousands of metabolites in a single biosample. Application of these analytical approaches to population-scale human studies has been limited by the need for resource-intensive blood sample collection, preparation, and storage. Dried blood spotting, a technique developed decades ago for newborn screening, may offer a simple approach to overcome barriers in human blood acquisition and storage. In this study, we find that over 4,400 spectral features across diverse chemical classes may be efficiently and reproducibly extracted and relatively quantified from human dried blood spots using nontargeted metabolomic analysis employing HILIC and reversed-phase liquid chromatography coupled to Orbitrap mass spectrometry. Moreover, over 80% of metabolites were found to be chemically stable in dried blood spots stored at room temperature for up to a week. In direct relation to plasma samples, dried blood spots exhibited comparable representation of the human circulating metabolome, capturing both known and previously uncharacterized metabolites. Dried blood spot approaches provide an opportunity for rapid and facile human biosampling and storage and will enable widespread metabolomics study of populations, particularly in resource-limited areas.

A mathematical method for calibrating the signal drift in liquid chromatography - mass spectrometry analysis

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become the most versatile analytical tool for profiling small-molecule compounds and increasingly been applied in many fields. Nevertheless, LC-MS based quantification still face some challenges, such as signal drift in LC-MS, which may affect the validity of the obtained data and lead to misinterpretation of biological results. Here, we established a calibration method known as "RIM" to compensate the signal drift of LC-MS. To this end, a mixture of d₄-2-dimethylaminoethylamine (d₄-DMED)-coded normal fatty acids (C5-C23) was used as calibrants to construct RIM calibration. With the addition of calibrants, not only the MS signal drift, but also the mass accuracy and LC retention time can be calibrated, thereby improving the reliability of quantitative data. The effectiveness of RIM was carefully validated using a human serum extract spiked with 34 standards and then RIM was applied for rat brain untargeted metabolome research. In addition, to expand the functionality and flexibility of RIM for data handling, we generated a MATLAB-based RIM program, which implements the above concepts and allows automatic data process. Taken together, the proposed RIM method has potential application in large-scale quantitative study of complex samples.

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.

Mass Spectrometry-based Metabolomics in Translational Research

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

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

A metabolomics study on effects of polyaromatic compounds in oil sand extracts on the respiratory, hepatic and nervous systems using three human cell lines

Polyaromatic compounds (PACs) are by-products of combustion and are the major pollutants from the oil and gas industry. However, the mechanism of PACs induced toxicity still remains elusive. The aim of this study was to elucidate the effects of a typical mixture of PACs found in oil sand extract (OSE) on the respiratory, hepatic and nervous systems in humans using in vitro cell culture models followed by non-targeted metabolomics analysis. OSE collected from Alberta, Canada was fractionated into PAC and alkane fractions, and their effects after 24 h exposure on the cell viability measured by MTT assay in three human cell lines (A549, HepG2, and SK-N-SH) were studied. The PAC fractions showed significant dose-dependent cytotoxicity. A549 cells showed the highest sensitivity to OSE extracts, followed by SK-N-SH and HepG2. In contrast, the alkane fractions showed no effects on cell viability. The three human cell lines were further exposed with the PACs at 10% and 20% lethal concentration for 24 h. Metabolomics analysis of the cell extracts indicated that PACs treatments showed different disruptions on possible metabolic pathways on the three cell lines. PACs altered the sex steroid hormone metabolism and regulated the levels of leukotrienes metabolites in all three cell types. The amino acids L-cysteine, L-glutamine, L-tyrosine that are known to cause respiratory effects were significantly up-regulated in A549 cells. The PACs treated HepG2 cells showed down-regulation in metabolites responsible for the inflammatory mediation. Treatment of the differentiated SK-N-SH cells showed up-regulated metabolites involved with butanoate, fatty acid, and pyrimidine metabolism. Leukotriene metabolites were found to be significantly increased in all PACs treated cells. In conclusion, our results showed that PACs in OSE can alter the metabolism of the human lung, liver and neuronal cells and may induce toxicity in multiple target organs.

Deep Neural Networks for Classification of LC-MS Spectral Peaks

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics has emerged as a valuable tool for biological discovery, capable of assaying thousands of diverse chemical entities in a single biospecimen. Processing of nontargeted LC-MS spectral data requires identification and isolation of true spectral features from the random, false noise peaks that comprise a significant portion of total signals, using inexact peak selection algorithms and time-consuming visual inspection of data. To increase the fidelity and speed of data processing, herein we establish, optimize, and evaluate a machine learning pipeline employing deep neural networks as well as a simpler multiple logistic regression model for classification of spectral features from nontargeted LC-MS metabolomics data. Machine learning-based approaches were found to remove up to 90% of false peaks from complex nontargeted LC-MS data sets without reducing true positive signals and exhibit excellent reproducibility across multiple data sets. Application of machine learning for nontargeted LC-MS-based peak selection provides for robust and scalable peak classification and data filtering, enabling handling and processing of large scale, complex metabolomics data sets.

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.

Directed Non-targeted Mass Spectrometry and Chemical Networking for Discovery of Eicosanoids and Related Oxylipins

Eicosanoids and related oxylipins are critical, small bioactive mediators of human physiology and inflammation. While ∼1,100 distinct species have been predicted to exist, to date, less than 150 of these molecules have been measured in humans, limiting our understanding of their role in human biology. Using a directed non-targeted mass spectrometry approach in conjunction with chemical networking of spectral fragmentation patterns, we find over 500 discrete chemical signals highly consistent with known and putative eicosanoids and related oxylipins in human plasma including 46 putative molecules not previously described. In plasma samples from 1,500 individuals, we find members of this expanded oxylipin library hold close association with markers of inflammation, as well as clinical characteristics linked with inflammation, including advancing age and obesity. These experimental and computational approaches enable discovery of new chemical entities and will shed important insight into the role of bioactive molecules in human health and disease.

High-Throughput Measure of Bioactive Lipids Using Non-targeted Mass Spectrometry

Bioactive lipids represent critical intra- and intercellular signaling molecules, and have been implicated in both physiologic homeostasis and disease pathology. Measurement of bioactive lipids is vital toward understanding the role of these signaling intermediates in human biology. Current analytical methods for assessment of bioactive lipids in human biosamples are limited, however, in breath of analytes assayed as well as robustness and time required for measures across thousands of samples. Herein, we describe in comprehensive detail a rapid and robust analytical method using liquid chromatography-mass spectrometry (LC-MS) for non-targeted measurement of over 7000 bioactive lipids, including eicosanoids and eicosanoid-related metabolites, in human biosamples. These methods may be applied to the study of population scale cohorts to uncover previously unrecognized roles for bioactive lipid species in human biology.

Male lifespan extension with 17-α estradiol is linked to a sex-specific metabolomic response modulated by gonadal hormones in mice

Longevity in mammals is influenced by sex, and lifespan extension in response to anti‐aging interventions is often sex‐specific, although the mechanisms underlying these sexual dimorphisms are largely unknown. Treatment of mice with 17‐α estradiol (17aE2) results in sex‐specific lifespan extension, with an increase in median survival in males of 19% and no survival effect in females. Given the links between lifespan extension and metabolism, we performed untargeted metabolomics analysis of liver, skeletal muscle and plasma from male and female mice treated with 17aE2 for eight months. We find that 17aE2 generates distinct sex‐specific changes in the metabolomic profile of liver and plasma. In males, 17aE2 treatment raised the abundance of several amino acids in the liver, and this was further associated with elevations in metabolites involved in urea cycling, suggesting altered amino acid metabolism. In females, amino acids and urea cycling metabolites were unaffected by 17aE2. 17aE2 also results in male‐specific elevations in a second estrogenic steroid—estriol‐3‐sulfate—suggesting different metabolism of this drug in males and females. To understand the underlying endocrine causes for these sexual dimorphisms, we castrated males and ovariectomized females prior to 17aE2 treatment, and found that virtually all the male‐specific metabolite responses to 17aE2 are inhibited or reduced by male castration. These results suggest novel metabolic pathways linked to male‐specific lifespan extension and show that the male‐specific metabolomic response to 17aE2 depends on the production of testicular hormones in adult life.

The VITamin D and OmegA-3 TriaL-Depression Endpoint Prevention (VITAL-DEP): Rationale and design of a large-scale ancillary study evaluating vitamin D and marine omega-3 fatty acid supplements for prevention of late-life depression

RATIONALE

Depression is a leading cause of disease burden and disability for older adults; thus, prevention is a priority. Biologic and observational data support potential mental health benefits of vitamin D and omega-3 fatty acids; however, it is unclear whether these supplements can prevent late-life depression.

DESIGN

We describe the novel methodology of a large-scale study: VITAL-DEP (VITamin D and OmegA-3 TriaL-Depression Endpoint Prevention), an ancillary to the VITAL trial. Primary Aims of VITAL-DEP are to determine effects on prevention of depression and on trajectory of mood symptoms of long-term (mean=5years) supplementation with vitamin D (vitamin D3 [cholecalciferol], 2000IU/day) and marine omega-3 fatty-acids (eicosapentaenoic acid + docosahexaenoic acid, 1g/day), in a 2×2 factorial design, among 25,874 older adults. Secondary Aims will evaluate: vitamin D's effects among African-Americans (an at-risk group for vitamin D deficiency); both agents' effects among those with high-risk factors or sub-syndromal depression in a sub-set of ~1000 participants with detailed examinations at baseline and 2-year follow-up; whether baseline nutrient levels influence depression risk and/or modify agents' effects. Additional planned analyses will use pre-randomization blood samples available in ~17,000 participants to address whether key biomarkers and factors influence long-term mood and depression risk and/or the agents' effects.

CONCLUSION

VITAL-DEP applies all modalities of state-of-the-art prevention research - universal, selective and indicated. VITAL-DEP will clarify effects of supplemental vitamin D and/or omega-3 on mood, and inform clinical care and public health guidelines on the use of these agents for prevention of depression in mid-life and older adults.

Cardiovascular Metabolomics

Disturbances in cardiac metabolism underlie most cardiovascular diseases. Metabolomics, one of the newer omics technologies, has emerged as a powerful tool for defining changes in both global and cardiac-specific metabolism that occur across a spectrum of cardiovascular disease states. Findings from metabolomics studies have contributed to better understanding of the metabolic changes that occur in heart failure and ischemic heart disease and have identified new cardiovascular disease biomarkers. As technologies advance, the metabolomics field continues to evolve rapidly. In this review, we will discuss the current state of metabolomics technologies, including consideration of various metabolomics platforms and elements of study design; the emerging utility of stable isotopes for metabolic flux studies; and the use of metabolomics to better understand specific cardiovascular diseases, with an emphasis on recent advances in the field.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

An alignment algorithm for LC-MS-based metabolomics dataset assisted by MS/MS information

Liquid chromatography-mass spectrometry (LC-MS) is an important analytical platform for metabolomics study. Peak alignment of metabolomics dataset is one of the keys for a successful metabolomics study. In this work, a MS/MS-based peak alignment method for LC-MS metabolomics data was developed. A rigorous strategy for screening endogenous reference variables was proposed. Firstly, candidate endogenous reference variables were selected based on MS, MS/MS and retention time in all samples. Multiple robust endogenous reference variables were obtained through further evaluation and confirmation. Then retention time of each metabolite feature was corrected by local linear regression using the four nearest neighbor robust reference variables. Finally, peak alignment was carried out based on corrected retention time, MS and MS/MS. Comparing with the other two peak alignment methods, the developed method showed a good performance and was suitable for metabolomics data with larger retention time drift. Our approach provides a simple and robust alignment method which is reliable to align LC-MS metabolomics dataset.

New frontiers for mass spectrometry based upon structures for lossless ion manipulations

Structures for lossless ion manipulations (SLIM) provide a new paradigm for efficient, complex and extended gas phase ion manipulations. SLIM are created from electric fields generated by the application of DC and RF potentials to arrays of electrodes patterned on two parallel surfaces. The electric fields provide lossless ion manipulations, including effective ion transport and storage. SLIM modules have been developed using both constant and oscillatory electric fields (e.g. traveling waves) to affect the ion motion. Ion manipulations demonstrated to date with SLIM include: extended trapping, ion selection, ion dissociation, and ion mobility spectrometry (IMS) separations achieving unprecedented ultra high resolution. SLIM thus provide the basis for previously impractical manipulations, such as very long path length ion mobility separations where ions traverse a serpentine path multiple times, as well as new capabilities that extend the utility of these developments based on temporal and spatial compression of ion mobility separations and other ion distributions. The evolution of SLIM devices developed over the last three years is reviewed and we provide examples of various ion manipulations performed, and briefly discuss potential applications and new directions.