Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.

Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is one of the most popular technologies in metabolomics. The large-scale and unambiguous identification of metabolite structures remains a challenging task in LC-MS based metabolomics. Tandem mass spectral databases provide experimental and in silico MS/MS spectra to facilitate the identification of both known and unknown metabolites, which has become a gold standard method in metabolomics. In addition, metabolite knowledge databases offer valuable biological and pathway information of metabolites. In this chapter, we have briefly reviewed the most common and important tandem mass spectral and metabolite databases, and illustrated how they could be used for metabolite identification.

The METLIN small molecule dataset for machine learning-based retention time prediction

Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.

The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

From Samples to Insights into Metabolism: Uncovering Biologically Relevant Information in LC-HRMS Metabolomics Data

Untargeted metabolomics (including lipidomics) is a holistic approach to biomarker discovery and mechanistic insights into disease onset and progression, and response to intervention. Each step of the analytical and statistical pipeline is crucial for the generation of high-quality, robust data. Metabolite identification remains the bottleneck in these studies; therefore, confidence in the data produced is paramount in order to maximize the biological output. Here, we outline the key steps of the metabolomics workflow and provide details on important parameters and considerations. Studies should be designed carefully to ensure appropriate statistical power and adequate controls. Subsequent sample handling and preparation should avoid the introduction of bias, which can significantly affect downstream data interpretation. It is not possible to cover the entire metabolome with a single platform; therefore, the analytical platform should reflect the biological sample under investigation and the question(s) under consideration. The large, complex datasets produced need to be pre-processed in order to extract meaningful information. Finally, the most time-consuming steps are metabolite identification, as well as metabolic pathway and network analysis. Here we discuss some widely used tools and the pitfalls of each step of the workflow, with the ultimate aim of guiding the reader towards the most efficient pipeline for their metabolomics studies.

Streamlined MRM method transfer between instruments assisted with HRMS matching and retention-time prediction

Multiple reaction monitoring (MRM) mode using liquid-chromatography tandem mass spectrometry (e.g., LC-QqQ-MS/MS) has been extensively employed in the small molecule analysis with trace levels in complex samples owing to its high sensitivity. However, most of the reported MRM methods are developed using authentic standards, which are often costly yet not readily available. To address this question, a practical platform for the MRM method transfer between different LC-QqQ-MS/MS instruments, assisted by the high-resolution mass spectrometry (LC-HRMS) and retention time (RT) prediction, has been developed in this study. The reported platform can take advantage of both the high sensitivity of LC-MRM method and ion transition pairs from the previous publications. LC-HRMS can provide the accurate mass measurement of the compounds, though high-quality MS/MS fragments are usually difficult to obtain for chemicals at trace levels. Retention time matching and peaks matching between both instrumental platforms rule out isobaric candidates. With an additional retention time prediction filter from quantitative structure retention relationship (QSRR) model based on random forest feature selection (Pearson r² = 0.63), identification of small molecules is achieved at a high confidence level without using authentic standards. The developed platform has been validated with robustness by examining spiked environmental chemicals in sludge water samples, biological urine, and cell extracts.

Phytotoxicity induced by engineered nanomaterials as explored by metabolomics: Perspectives and challenges

Given the wide applications of engineered nanomaterials (ENMs) in various fields, the ecotoxicology of ENMs has attracted much attention. The traditional plant physiological activity (e.g., reactive oxygen species and antioxidant enzymes) are limited in that they probe one specific process of nanotoxicity, which may result in the loss of understanding of other important biological reactions. Metabolites, which are downstream of gene and protein expression, are directly related to biological phenomena. Metabolomics is an easily performed and efficient tool for solving the aforementioned problems because it involves the comprehensive exploration of metabolic profiles. To understand the roles of metabolomics in phytotoxicity, the analytical methods for metabolomics should be organized and discussed. Moreover, the dominant metabolites and metabolic pathways are similar in different plants, which determines the universal applicability of metabolomics analysis. The analysis of regulated metabolism will globally and scientifically help determine the ecotoxicology that is induced by ENMs. In the past several years, great developments in nanotoxicology have been achieved using metabolomics. However, many knowledge gaps remain, such as the relationships between biological responses that are induced by ENMs and the regulation of metabolism (e.g., carbohydrate, energy, amino acid, lipid and secondary metabolism). The phytotoxicity that is induced by ENMs has been explored by metabolomics, which is still in its infancy. The detrimental and defence mechanisms of plants in their response to ENMs at the level of metabolomics also deserve much attention. In addition, owing to the regulation of metabolism in plants by ENMs affected by multiple factors, it is meaningful to uniformly identify the key influencing factor.

Exploration of the microbiota and metabolites within body fluids could pinpoint novel disease mechanisms

Thanks to the emergence and recent advances in high-throughput sequencing technologies, it is becoming more evident every day that changes in the microbiome composition are linked to a myriad of health conditions. Despite this, the mechanisms of host-microbiota signalling remain largely unknown. The microbiome has an extensive metabolic activity that leads to the generation of a large number of compounds that are likely to influence host health. Therefore, the microbiome-host cross-talk is in part mediated by microbial-derived metabolites. Unlike metagenomics, which only provides information about microbial genes and thus the microbiome functional potential, metabolic phenotyping is well suited to capture their actual metabolic activity. Here, we provide an overview of these approaches and propose an integration of metagenomics, as a microbiome compositional readout, with faecal and plasma/urine metabolomics, as a functional readout, to unravel novel mechanisms linking the microbiome to host health and disease.

Untargeted and Targeted Discrimination of Honey Collected by Apis cerana and Apis mellifera Based on Volatiles Using HS-GC-IMS and HS-SPME-GC-MS

Fraudulent acts regarding honey authenticity that use Apis mellifera honey as a substitute for Apis cerana honey have garnered considerable concern in China and triggered a trust crisis from consumers. In this study, untargeted metabolomics analysis was carried out based on volatile fractions in honey from A. cerana and A. mellifera using headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS). Honey from A. cerana and A. mellifera was discriminated by HS-GC-IMS profiling, principal component analysis, and orthogonal partial least-squares discrimination analysis. Tentative markers were identified from p-values and the variable importance in projection analysis and confirmed using the retention index, mass fragments, and reference standards by gas chromatography-mass spectrometry (GC-MS). A targeted method was established using the headspace solid phase coupled with microextraction GC-MS (HS-SPME-GC-MS) to quantitate the markers. The results demonstrated that the developed untargeted and targeted metabolomics approach performed well when discriminating honey from A. cerana and A. mellifera.

Fingerprinting, Antimicrobial, Antioxidant, Anticancer, Cyclooxygenase and Metabolic Enzymes Inhibitory Characteristic Evaluations of Stachys viticina Boiss. Essential Oil

The present study aimed to identify the chemical constituents and to assess the in-vitro, antimicrobial, anticancer, antioxidant, metabolic enzymes and cyclooxygenase (COX) inhibitory properties of essential oil (EO) of Stachys viticina Boiss. leaves. The S. viticina EO was isolated and identified using microwave-ultrasonic and GC-MS techniques, respectively. Fifty-two compounds were identified, of which endo-borneol was the major component, followed by eucalyptol and epizonarene. The EO was evaluated against a panel of in-vitro bioassays. The EO displayed antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli and Epidermophyton floccosum, with MIC values of 0.039, 0.078 and 0.78 mg/mL, respectively. The EO exhibited cytotoxicity against HeLa (cervical adenocarcinoma) and Colo-205 (colon) cancer cell lines with percentages of inhibition of 95% and 90%, for EO concentrations of 1.25 and 0.5 mg/mL, respectively. Furthermore, it showed metabolic enzyme (α-amylase, α-glucosidase, and lipase) inhibitory (IC₅₀ = 45.22 ± 1.1, 63.09 ± 0.26, 501.18 ± 0.38 µg/mL, respectively) and antioxidant activity, with an IC₅₀ value of 19.95 ± 2.08 µg/mL. Moreover, the S. viticina EO showed high cyclooxygenase inhibitory activity against COX-1 and COX-2 with IC₅₀ values of 0.25 and 0.5 µg/mL, respectively, similar to those of the positive control (the NSAID etodolac). Outcomes amassed from this investigation illustrate that S. viticina EO represents a rich source of pharmacologically active molecules which can be further validated and explored clinically for its therapeutic potential and for the development and design of new natural therapeutic preparations.

Creating a Reliable Mass Spectral-Retention Time Library for All Ion Fragmentation-Based Metabolomics

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC-MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC-MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species

The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

An overview of tools, software, and methods for natural product fragment and mass spectral analysis

One major challenge in natural product (NP) discovery is the determination of the chemical structure of unknown metabolites using automated software tools from either GC–mass spectrometry (MS) or liquid chromatography–MS/MS data only. This chapter reviews the existing spectral libraries and predictive computational tools used in MS-based untargeted metabolomics, which is currently a hot topic in NP structure elucidation. We begin by focusing on spectral databases and the general workflow of MS annotation. We then describe software and tools used in MS, particularly those used to predict fragmentation patterns, mass spectral classifiers, and tools for fragmentation trees analysis. We then round up the chapter by looking at more advanced approaches implemented in tools for competitive fragmentation modeling and quantum chemical approaches.

DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted Metabolomics

SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra and decompose them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the experimental MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA .

On-Line Analysis of Exhaled Breath Focus Review

On-line analysis of exhaled breath offers insight into a person's metabolism without the need for sample preparation or sample collection. Due to its noninvasive nature and the possibility to sample continuously, the analysis of breath has great clinical potential. The unique features of this technology make it an attractive candidate for applications in medicine, beyond the task of diagnosis. We review the current methodologies for on-line breath analysis, discuss current and future applications, and critically evaluate challenges and pitfalls such as the need for standardization. Special emphasis is given to the use of the technology in diagnosing respiratory diseases, potential niche applications, and the promise of breath analysis for personalized medicine. The analytical methodologies used range from very small and low-cost chemical sensors, which are ideal for continuous monitoring of disease status, to optical spectroscopy and state-of-the-art, high-resolution mass spectrometry. The latter can be utilized for untargeted analysis of exhaled breath, with the capability to identify hitherto unknown molecules. The interpretation of the resulting big data sets is complex and often constrained due to a limited number of participants. Even larger data sets will be needed for assessing reproducibility and for validation of biomarker candidates. In addition, molecular structures and quantification of compounds are generally not easily available from on-line measurements and require complementary measurements, for example, a separation method coupled to mass spectrometry. Furthermore, a lack of standardization still hampers the application of the technique to screen larger cohorts of patients. This review summarizes the present status and continuous improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider application.

Linking in silico MS/MS spectra with chemistry data to improve identification of unknowns

Confident identification of unknown chemicals in high resolution mass spectrometry (HRMS) screening studies requires cohesive workflows and complementary data, tools, and software. Chemistry databases, screening libraries, and chemical metadata have become fixtures in identification workflows. To increase confidence in compound identifications, the use of structural fragmentation data collected via tandem mass spectrometry (MS/MS or MS²) is vital. However, the availability of empirically collected MS/MS data for identification of unknowns is limited. Researchers have therefore turned to in silico generation of MS/MS data for use in HRMS-based screening studies. This paper describes the generation en masse of predicted MS/MS spectra for the entirety of the US EPA's DSSTox database using competitive fragmentation modelling and a freely available open source tool, CFM-ID. The generated dataset comprises predicted MS/MS spectra for ~700,000 structures, and mappings between predicted spectra, structures, associated substances, and chemical metadata. Together, these resources facilitate improved compound identifications in HRMS screening studies. These data are accessible via an SQL database, a comma-separated export file (.csv), and EPA's CompTox Chemicals Dashboard.

Using metabolomics to assess the sub-lethal effects of zinc and boscalid on an estuarine polychaete worm over time

INTRODUCTION

Zinc is a heavy metal commonly detected in urban estuaries around Australia. Boscalid is a fungicide found in estuaries, both in water and sediment, it enters the system predominantly through agricultural run-off. Zinc is persistent while boscalid breaks down, with a half-life of 108 days. Both contaminants are widely distributed and their effects on ecosystems are not well understood.

OBJECTIVES

The aim of this study was to determine the metabolite changes in Simplisetia aequisetis (an estuarine polychaete) following laboratory exposure to a sub-lethal concentration of zinc or boscalid over a 2-week period.

METHODS

Individuals were collected at six time points over a 2-week period. Whole polychaete metabolites were extracted and quantified using a multi-platform approach. Polar metabolites were detected using a semi-targeted GC-MS analysis and amine containing compounds were analysed using a targeted LC-MS analysis. Total lipid energy content was also analysed for Simplisetia aequisetis.

RESULTS

The pathways that responded to zinc and boscalid exposure were alanine, aspartate and glutamate metabolism (AAG); glycine, serine and threonine metabolism (GST) and metabolites associated with the tricarboxylic acid cycle (TCA). Results showed that changes in total abundance of some metabolites could be detected as early as 24-h exposure. Changes were detected in the metabolites before commonly used total lipid energy assays identified effects.

CONCLUSION

A multi-platform approach provided a holistic overview of the metabolomic response to contaminants in polychaetes. This approach shows promise to be used in biomonitoring programs to provide early diagnostic indicators of contamination and exposure.

Non-targeted Screening in Environmental Monitoring Programs

Contaminant monitoring programs have been tasked with understanding the fate and transport of toxic chemicals in the environment. Mass spectrometry based methods have traditionally been developed to maximize sensitivity and accuracy of a select set of target compounds. As mass spectrometry methods have advanced, so has the breadth of questions proposed by environmental chemists. Incorporating these methods in chemical monitoring programs provides large data sets to explore the effects of complex mixtures on environmental systems.

"-Omics" workflow for paleolimnological and geological archives: A review

"-Omics" is a powerful screening method with applications in molecular biology, toxicology, wildlife biology, natural product discovery, and many other fields. Genomics, proteomics, metabolomics, and lipidomics are common examples included under the "-omics" umbrella. This screening method uses combinations of untargeted, semi-targeted, and targeted analyses paired with data mining to facilitate researchers' understanding of the genome, proteins, and small organic molecules in biological systems. Recently, however, the use of "-omics" has expanded into the fields of geology, specifically petrology, and paleolimnology. Specifically, untargeted analyses stand to transform these fields as petroleomics, and sediment-"omics" become more prevalent. "-Omics" facilitates the visualization of small molecule profiles from environmental matrices (i.e. oil and sediment). Small molecule profiles can provide improved understanding of small molecules distributions throughout the environment, and how those compositions can change depending on conditions (i.e. climate change, weathering, etc.). "-Omics" also facilities discovery of next-generation biomarkers that can be used for oil source identification and as proxies for reconstructing past environmental changes. Untargeted analyses paired with data mining and multivariate statistical analyses represents a powerful suite of tools for hypothesis generation, and new method development for environmental reconstructions. Here we present an introduction to "-omics" methodology, technical terms, and examples of applications to paleolimnology and petrology. The purpose of this review is to highlight the important considerations at each step in the "-omics" workflow to produce high quality and statistically powerful data for petrological and paleolimnological applications.

Rapid screening methods for yeast sub-metabolome analysis with a high-resolution ion mobility quadrupole time-of-flight mass spectrometer

RATIONALE

The wide chemical diversity and complex matrices inherent to metabolomics still pose a challenge to current analytical approaches for metabolite screening. Although dedicated front-end separation techniques combined with high-resolution mass spectrometry set the benchmark from an analytical point of view, the increasing number of samples and sample complexity demand for a compromise in terms of selectivity, sensitivity and high-throughput analyses.

METHODS

Prior to low-field drift tube ion mobility (IM) separation and quadrupole time-of-flight mass spectrometry (QTOFMS) detection, rapid ultrahigh-performance liquid chromatography separation was used for analysis of different concentration levels of dansylated metabolites present in a yeast cell extract. For identity confirmation of metabolites at the MS2 level, an alternating frame approach was chosen and two different strategies were tested: a data-independent all-ions acquisition and a quadrupole broad band isolation (Q-BBI) directed by IM drift separation.

RESULTS

For Q-BBI analysis, the broad mass range isolation was successfully optimized in accordance with the distinctive drift time to m/z correlation of the dansyl derivatives. To guarantee comprehensive sampling, a broad mass isolation window of 70 Da was employed. Fragmentation was performed via collision-induced dissociation, applying a collision energy ramp optimized for the dansyl derivatives. Both approaches were studied in terms of linear dynamic range and repeatability employing ethanolic extracts of Pichia pastoris spiked with 1 μM metabolite mixture. Example data obtained for histidine and glycine showed that drift time precision (<0.01 to 0.3% RSD, n = 5) compared very well with the data reported in an earlier IM-TOFMS-based study.

CONCLUSIONS

Chimeric mass spectra, inherent to data-independent analysis approaches, are reduced when using a drift time directed Q-BBI approach. Additionally, an improved linear dynamic working range was observed, representing, together with a rapid front-end separation, a powerful approach for metabolite screening.

Impact of sample preparation upon intracellular metabolite measurements in 3D cell culture systems

INTRODUCTION

Interest in cell culture metabolomics has increased greatly in recent years because of its many potential applications and advantages (e.g., in toxicology). The first critical step for exploring the cellular metabolome is sample preparation. For metabolomics studies, an ideal sample preparation would extract a maximum number of metabolites and would enable reproducible, accurate analysis of a large number of samples and replicates. In addition, it would provide consistent results across several studies over a relatively long time frame.

OBJECTIVES

This study was conducted to evaluate the impact of sample preparation strategies on monitoring intracellular metabolite responses, highlighting the potential critical step(s) in order to finally improve the quality of metabolomics studies.

METHODS

The sample preparation strategies were evaluated by calculating the sample preparation effect, matrix factor, and process efficiency (PE) for 16 tobacco exposition-related metabolites, including nicotine, nicotine-derived nitrosamine ketone, their major metabolites, and glutathione, using isotopically-labelled internal standards. Samples were analyzed by liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS).

RESULTS

A sample drying step increased losses or variability for some selected metabolites. By avoiding evaporation, good sample preparation recovery was obtained for these compounds. For some metabolites, the cell or culture type impacted PE and matrix factor.

CONCLUSION

In our sample preparation protocol, the drying-reconstitution step was identified as the main cause of metabolite losses or increased data variability during metabolomics analysis by LC-HRMS. Furthermore, PE was affected by the type of matrix. Isotopologue internal standards fully compensate losses or enhancements.

Identification of bioactive metabolites using activity metabolomics

The metabolome, the collection of small-molecule chemical entities involved in metabolism, has traditionally been studied with the aim of identifying biomarkers in the diagnosis and prediction of disease. However, the value of metabolome analysis (metabolomics) has been redefined from a simple biomarker identification tool to a technology for the discovery of active drivers of biological processes. It is now clear that the metabolome affects cellular physiology through modulation of other 'omics' levels, including the genome, epigenome, transcriptome and proteome. In this Review, we focus on recent progress in using metabolomics to understand how the metabolome influences other omics and, by extension, to reveal the active role of metabolites in physiology and disease. This concept of utilizing metabolomics to perform activity screens to identify biologically active metabolites - which we term activity metabolomics - is already having a broad impact on biology.

The role of analytical chemistry in exposure science: Focus on the aquatic environment

Exposure science, in its broadest sense, studies the interactions between stressors (chemical, biological, and physical agents) and receptors (e.g. humans and other living organisms, and non-living items like buildings), together with the associated pathways and processes potentially leading to negative effects on human health and the environment. The aquatic environment may contain thousands of compounds, many of them still unknown, that can pose a risk to ecosystems and human health. Due to the unquestionable importance of the aquatic environment, one of the main challenges in the field of exposure science is the comprehensive characterization and evaluation of complex environmental mixtures beyond the classical/priority contaminants to new emerging contaminants. The role of advanced analytical chemistry to identify and quantify potential chemical risks, that might cause adverse effects to the aquatic environment, is essential. In this paper, we present the strategies and tools that analytical chemistry has nowadays, focused on chromatography hyphenated to (high-resolution) mass spectrometry because of its relevance in this field. Key issues, such as the application of effect direct analysis to reduce the complexity of the sample, the investigation of the huge number of transformation/degradation products that may be present in the aquatic environment, the analysis of urban wastewater as a source of valuable information on our lifestyle and substances we consumed and/or are exposed to, or the monitoring of drinking water, are discussed in this article. The trends and perspectives for the next few years are also highlighted, when it is expected that new developments and tools will allow a better knowledge of chemical composition in the aquatic environment. This will help regulatory authorities to protect water bodies and to advance towards improved regulations that enable practical and efficient abatements for environmental and public health protection.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

The Study of Dried Ginger and Linggan Wuwei Jiangxin Decoction Treatment of Cold Asthma Rats Using GC-MS Based Metabolomics

Dried ginger is the monarch drug in Linggan Wuwei Jiangxin (LGWWJX) decoction, which is used to treat cold asthma. The purpose of this study was to investigate and compare the effects of dried ginger and LGWWJX decoction for treatment of cold asthma rats at the metabolomics level using gas chromatography–mass spectrometry (GC–MS). OVA and ice water-induced cold asthma were induced in SD rats. The effects of dried ginger and LGWWJX decoction were evaluated by general morphological observation, hematoxylin and eosin staining, inflammatory cell count, IgE, IL-4, IFN-γ quantitation, and visceral index. GC-MS-based metabolomics was performed and analyzed using multivariate statistical analysis. Biomarker identification, pathway analysis, correlations between identified biomarker, and efficacy indices were performed. The results showed that dried ginger and LGWWJX decoction had obvious effects on cold asthma rats. Thirty-seven metabolites (15 in serum and 22 in urine) associated with cold asthma were identified. These metabolites were mainly carbohydrates, fatty acids and their products, organic acids, and others. Seven pathways were identified by MetaboAnalyst 4.0 metabolic pathway analysis. After intervention with dried ginger and LGWWJX decoction, the majority of altered metabolites and metabolic pathways returned to control levels. LGWWJX decoction regulated more metabolites of carbohydrates and fatty acids, which contribute to energy metabolism and oxidative stress in cold asthma, than dried ginger. We concluded that dried ginger and LGWWJX decoction both were effective for treatment of cold asthma. LGWWJX decoction was more effective than dried ginger for treatment of cold asthma. This study evaluated the effects of dried ginger and LGWWJX decoction on cold asthma at the metabolomics level. It provides a reference for the research on the compatibility of Chinese Medicine.

Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.

ISiCLE: A Quantum Chemistry Pipeline for Establishing in Silico Collision Cross Section Libraries

High-throughput, comprehensive, and confident identifications of metabolites and other chemicals in biological and environmental samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent technological advances, metabolomics studies still result in the detection of a disproportionate number of features that cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics, the reliance on reference libraries constructed by analysis of authentic reference materials with limited commercial availability. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers (i.e., conformational isomers) using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs first-principles simulation, distinguished by the use of molecular dynamics, quantum chemistry, and ion mobility calculations, to generate structures and chemical property libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over 2 orders of magnitude. Calculated CCS values were validated against 1983 experimentally measured CCS values and compared to previously reported CCS calculation approaches. Average calculated CCS error for the validation set is 3.2% using standard parameters, outperforming other density functional theory (DFT)-based methods and machine learning methods (e.g., MetCCS). An online database is introduced for sharing both calculated and experimental CCS values ( metabolomics.pnnl.gov ), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described, including providing evidence for the presence of an environmental degradation product, the separation of molecular isomers, and an initial characterization of complex blinded mixtures of exposure chemicals. This work represents a method to address the limitations of small molecule identification and offers an alternative to generating chemical identification libraries experimentally by analyzing authentic reference materials. All code is available at github.com/pnnl .

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics

Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.

Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics

Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in-source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu .

Metabolomics Analysis of Leishmania by Capillary Electrophoresis and Mass Spectrometry

Capillary electrophoresis coupled to mass spectrometry is an analytical platform ideal for the analysis of ionic or polar metabolites. It constitutes a perfect complement to reversed-phase liquid chromatography, offering a good alternative to polar stationary phases where reproducibility is not guaranteed. Herein, we describe a robust standardized methodology for the fingerprinting analysis of Leishmania, a taxonomic genus which comprises more than 20 protozoa species.

Advances in mass spectrometry based single-cell metabolomics

Single cell metabolomics using mass spectrometry can contribute to understanding biological activities in health and disease.

Capillary Electrophoresis Mass Spectrometry as a Tool for Untargeted Metabolomics

Although capillary electrophoresis (CE) coupled to mass spectrometry (MS) is a separation technique not extensively implemented, it offers differential possibilities in the study of polar and ionic metabolites in complex matrices with minimum sample treatment. However, in order to get successful results, some efforts at early stages and following specific recommendations are necessary.In this chapter, we describe our updated and well-tested methods for untargeted metabolomics using CE-MS-TOF for common biological samples: urine, serum or plasma, feces, tissues, and cells. Sample treatment, as well as separation and detection conditions are described in detail and other steps in the workflow for untargeted metabolomics are also explained. Special attention is paid to instrumental setup and advices for daily practice.Characteristic electropherograms obtained with each type of sample are depicted as well as groups of metabolites easily measured by this technique. Their global or individual comparisons have been given undoubtedly important information to unveil altered metabolic pathways, diagnosis, and prognosis or biomarker discovery in the study of diseases or conditions over decades.

UHPLC-QTOF-MS/MS-SPE-NMR: A Solution to the Metabolomics Grand Challenge of Higher-Throughput, Confident Metabolite Identifications

Metabolomics represents a powerful, complementary approach for studying biological system responses to various biotic and abiotic stimuli. A major challenge in metabolomics is the lack of reliable annotations for all metabolites detected in complex MS and/or NMR data. To meet this challenge, we have developed an integrated UHPLC-QTOF-MS/MS-SPE-NMR system for higher-throughput metabolite identifications, which provides advanced biological context and enhances the scientific value of metabolomics data for understanding systems biology. This integrated instrumental method is less labor-intensive and more cost-effective than conventional individual methods (LC; MS; SPE; NMR). It enables the simultaneous purification and identification of primary and secondary metabolites present in biological samples. In this chapter, we describe the configuration and use of UHPLC-MS/MS-SPE-NMR in metabolite analyses ranging from sample extraction to higher-throughput metabolite annotation. With the integrated UHPLC-QTOF-MS/MS-SPE-NMR method, we have purified and confidently identified more than 100 previously known as well as unknown triterpene and flavonoid glycosides while noting that most of the identified compounds are not commercially available.

Annotating Nontargeted LC-HRMS/MS Data with Two Complementary Tandem Mass Spectral Libraries

Tandem mass spectral databases are indispensable for fast and reliable compound identification in nontargeted analysis with liquid chromatography–high resolution tandem mass spectrometry (LC-HRMS/MS), which is applied to a wide range of scientific fields. While many articles now review and compare spectral libraries, in this manuscript we investigate two high-quality and specialized collections from our respective institutes, recorded on different instruments (quadrupole time-of-flight or QqTOF vs. Orbitrap). The optimal range of collision energies for spectral comparison was evaluated using 233 overlapping compounds between the two libraries, revealing that spectra in the range of CE 20–50 eV on the QqTOF and 30–60 nominal collision energy units on the Orbitrap provided optimal matching results for these libraries. Applications to complex samples from the respective institutes revealed that the libraries, combined with a simple data mining approach to retrieve all spectra with precursor and fragment information, could confirm many validated target identifications and yield several new Level 2a (spectral match) identifications. While the results presented are not surprising in many ways, this article adds new results to the debate on the comparability of Orbitrap and QqTOF data and the application of spectral libraries to yield rapid and high-confidence tentative identifications in complex human and environmental samples.

Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair

In recent years, the study of metabolomics has begun to receive increasing international attention, especially as it pertains to medical research. This is due in part to the potential for discovery of new biomarkers in the metabolome and to a new understanding of the "exposome", which refers to the endogenous and exogenous compounds that reflect external exposures. Consequently, metabolomics research into pregnancy-related issues has increased. Biomarkers discovered through metabolomics may shed some light on the etiology of certain pregnancy-related complications and their adverse effects on future maternal health and infant development and improve current clinical management. The discoveries and methods used in these studies will be compiled and summarized within the following paper. A further focus of this paper is the use of hair as a biological sample, which is gaining increasing attention across diverse fields due to its noninvasive sampling method and the metabolome stability. Its significance in exposome studies will be considered in this review, as well as the potential to associate exposures with adverse pregnancy outcomes. Currently, hair has been used in only two metabolomics studies relating to fetal growth restriction (FGR) and gestational diabetes mellitus (GDM).

Metabolomics activity screening for identifying metabolites that modulate phenotype

Metabolomics, in which small-molecule metabolites (the metabolome) are identified and quantified, is broadly acknowledged to be the omics discipline that is closest to the phenotype. Although appreciated for its role in biomarker discovery programs, metabolomics can also be used to identify metabolites that could alter a cell's or an organism's phenotype. Metabolomics activity screening (MAS) as described here integrates metabolomics data with metabolic pathways and systems biology information, including proteomics and transcriptomics data, to produce a set of endogenous metabolites that can be tested for functionality in altering phenotypes. A growing literature reports the use of metabolites to modulate diverse processes, such as stem cell differentiation, oligodendrocyte maturation, insulin signaling, T-cell survival and macrophage immune responses. This opens up the possibility of identifying and applying metabolites to affect phenotypes. Unlike genes or proteins, metabolites are often readily available, which means that MAS is broadly amenable to high-throughput screening of virtually any biological system.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Contribution of Untargeted Metabolomics for Future Assessment of Biotech Crops

The nutritional value and safety of food crops are ultimately determined by their chemical composition. Recent developments in the field of metabolomics have made it possible to characterize the metabolic profile of crops in a comprehensive and high-throughput manner. Here, we propose that state-of-the-art untargeted metabolomics technology should be leveraged for safety assessment of new crop products. We suggest generally applicable experimental design principles that facilitate the efficient and rigorous identification of both intended and unintended metabolic alterations associated with a newly engineered trait. Our proposition could contribute to increased transparency of the safety assessment process for new biotech crops.