Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Indole-3-pyruvate: Analysis and Control of Tautomerism and Reactivity

It is well-known in biochemistry that structure confers function, meaning that chemical structural elucidation is critical to truly understanding the function of a given metabolite. Indole-3-pyruvate (IPyA) exists in an equilibrium between the keto and enol tautomeric forms. IPyA is suggested to play a role in immune function; however, determining whether the tautomeric forms function differently can only be studied if an analytical method is capable of distinguishing between the two forms. Herein, we describe the use of UHPLC-HRMS to gain insight into the physical variables that govern IPyA tautomer equilibrium, reactivity, and detection limit. We use hydrogen-deuterium exchange (HDX) to identify enol and keto peaks, and we show that tautomers exhibit a valley of fronting followed by a tailing peak shape (though separation is still attainable) and identical MS/MS spectra. We observed drastically different ratios of keto and enol forms in different solvents, which is an important consideration for in vitro studies. IPyA was found to be highly unstable with accelerated reactivity in peroxides. Through in vitro reactivity studies, IPyA produced a myriad of known and unknown metabolites via nonenzymatic processes, many of which were mapped in vivo via the analysis of human plasma. Finally, we show that vitamin C (ascorbic acid) can slow this reactivity and enable sensitive detection in whole blood.

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C. difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

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

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

Effect of phenolics on soil microbe distribution, plant growth, and gall formation

Phenolic compounds, abundant secondary metabolites in plants, profoundly influence soil ecosystems, plant growth, and interactions with herbivores. In this study, we explore the intricate relationships between phenolics, soil microbes, and gall formation in Ageratina adenophora (A. adenophora), an invasive plant species in China known for its allelopathic traits. Using metabolomic and microbial profiling, significant differences in soil microbial composition and metabolite profiles were observed between bulk and rhizosphere soil samples. Phenolics influenced bacterial communities, with distinct microbial populations enriched in each soil type. Additionally, phenolics impacted soil metabolic processes, with variations observed in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis between different soil treatments. Analysis of phenolic content in plant and soil samples revealed considerable variations, with higher concentrations observed in certain plant tissues and soil types. Bioactive phenols extracted from plant and soil samples were identified using gas chromatography/mass spectrometry (GC-MS), providing insights into the diverse chemical composition of these compounds. Furthermore, the effects of phenolics on plant growth and gall formation were investigated. Phenols exhibited both stimulatory and inhibitory effects on plant growth, with optimal concentrations promoting emergence but higher concentrations hindering growth. Gall formation was influenced by phenolic concentrations, leading to structural alterations in stem tissue and gall morphology. Histochemical analysis revealed starch and lipid accumulation in gall tissues, indicating metabolic changes induced by phenolics. The presence of phenolics disrupted tissue structures and influenced vascular bundle orientation in gall tissues. Overall, our study highlights the multifaceted roles of phenolic compounds in soil ecosystems, plant development, and gall formation, facilitating the utilization of secondary metabolites in agriculture.

Hydrophilic Interaction Liquid Chromatography-Hydrogen/Deuterium Exchange-Mass Spectrometry (HILIC-HDX-MS) for Untargeted Metabolomics

Liquid chromatography with mass spectrometry (LC-MS)-based metabolomics detects thousands of molecular features (retention time-m/z pairs) in biological samples per analysis, yet the metabolite annotation rate remains low, with 90% of signals classified as unknowns. To enhance the metabolite annotation rates, researchers employ tandem mass spectral libraries and challenging in silico fragmentation software. Hydrogen/deuterium exchange mass spectrometry (HDX-MS) may offer an additional layer of structural information in untargeted metabolomics, especially for identifying specific unidentified metabolites that are revealed to be statistically significant. Here, we investigate the potential of hydrophilic interaction liquid chromatography (HILIC)-HDX-MS in untargeted metabolomics. Specifically, we evaluate the effectiveness of two approaches using hypothetical targets: the post-column addition of deuterium oxide (D2O) and the on-column HILIC-HDX-MS method. To illustrate the practical application of HILIC-HDX-MS, we apply this methodology using the in silico fragmentation software MS-FINDER to an unknown compound detected in various biological samples, including plasma, serum, tissues, and feces during HILIC-MS profiling, subsequently identified as N1-acetylspermidine.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Metabolomics analysis of pathways underlying radiation-induced salivary gland dysfunction stages

Salivary gland hypofunction is an adverse side effect associated with radiotherapy for head and neck cancer patients. This study delineated metabolic changes at acute, intermediate, and chronic radiation damage response stages in mouse salivary glands following a single 5 Gy dose. Ultra-high performance liquid chromatography-mass spectrometry was performed on parotid salivary gland tissue collected at 3, 14, and 30 days following radiation (IR). Pathway enrichment analysis, network analysis based on metabolite structural similarity, and network analysis based on metabolite abundance correlations were used to incorporate both metabolite levels and structural annotation. The greatest number of enriched pathways are observed at 3 days and the lowest at 30 days following radiation. Amino acid metabolism pathways, glutathione metabolism, and central carbon metabolism in cancer are enriched at all radiation time points across different analytical methods. This study suggests that glutathione and central carbon metabolism in cancer may be important pathways in the unresolved effect of radiation treatment.

Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability

Feature finding is a common way to process untargeted mass spectrometry (MS) data to obtain a list of chemicals present in a sample. Most feature finding algorithms naïvely search for patterns of unique descriptors (e.g., m/z, retention time, and mobility) and provide a list of unannotated features. There is a need for solutions in processing untargeted MS data, independent of chemical or origin, to assess features based on measurement quality with the aim of improving interpretation. Here, we report the signal response evaluation as a method by which to assess the individual features observed in untargeted MS data. The basis of this method is the ubiquitous relationship between the amount and response in all MS measurements. Three different metrics with user-defined parameters can be used to assess the monotonic or linear relationship of each feature in a dilution series or multiple injection volumes. We demonstrate this approach in metabolomics data obtained from a uniform biological matrix (NIST SRM 1950) and a variable biological matrix (murine kidney tissue). The code is provided to facilitate implementation of this data processing method.

Untargeted hair lipidomics: comprehensive evaluation of the hair-specific lipid signature and considerations for retrospective analysis

Lipidomics investigates the composition and function of lipids, typically employing blood or tissue samples as the primary study matrices. Hair has recently emerged as a potential complementary sample type to identify biomarkers in early disease stages and retrospectively document an individual's metabolic status due to its long detection window of up to several months prior to the time of sampling. However, the limited coverage of lipid profiling presented in previous studies has hindered its exploitation. This study aimed to evaluate the lipid coverage of hair using an untargeted liquid chromatography-high-resolution mass spectrometry lipidomics platform. Two distinct three-step exhaustive extraction experiments were performed using a hair metabolomics one-phase extraction technique that has been recently optimized, and the two-phase Folch extraction method which is recognized as the gold standard for lipid extraction in biological matrices. The applied lipidomics workflow improved hair lipid coverage, as only 99 species could be annotated using the one-phase extraction method, while 297 lipid species across six categories were annotated with the Folch method. Several lipids in hair were reported for the first time, including N-acyl amino acids, diradylglycerols, and coenzyme Q10. The study suggests that hair lipids are not solely derived from de novo synthesis in hair, but are also incorporated from sebum and blood, making hair a valuable matrix for clinical, forensic, and dermatological research. The improved understanding of the lipid composition and analytical considerations for retrospective analysis offers valuable insights to contextualize untargeted hair lipidomic analysis and facilitate the use of hair in translational studies.

Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.

Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid

A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li⁺, Na⁺, K⁺ and alkaline earth Ca²⁺, Mg²⁺ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.

Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

Liquid chromatography and differential mobility spectrometry-data-independent mass spectrometry for comprehensive multidimensional separations in metabolomics

The benefits of combining drift time ion mobility (DTIMS) with liquid chromatography-high-resolution mass spectrometry (HRMS) have been reported for metabolomics but the use of differential time mobility spectrometry (DMS) is less obvious due to the need for rapid scanning of the DMS cell. Drift DTIMS provides additional precursor ion selectivity and collisional cross-section information but the separation resolution between analytes remains cell- and component-dependent. With DMS, the addition of 2-propanol modifier can improve the selectivity but on cost of analyte MS response. In the present work, we investigate the liquid chromatography-mass spectrometry (LC-MS) analysis of a mix of 50 analytes, representative for urine and plasma metabolites, using scanning DMS with the single modifiers cyclohexane (Ch), toluene (Tol), acetonitrile (ACN), ethanol (EtOH), and 2-propanol (IPA), and a binary modifier mixture (cyclohexane/2-propanol) with emphasis on selectivity and signal sensitivity. 1.5% IPA in the N₂ stream was found to suppress the signal of 50% of the analytes which could be partially recovered with the use of IPA to 0.05% as a Ch/IPA mixture. The potential to use the separation voltage/compensation voltage/modifier (SV/CoV/Mod) feature as an additional analyte identifier for qualitative analysis is also presented and applied to a data-independent LCxDMS-SWATH-MS workflow for the analysis of endogenous metabolites and drugs of abuse in human urine samples from traffic control.

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

BACKGROUND

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.

AIM OF REVIEW

In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Progress and opportunities in microbial community metabolomics

The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.

The Thing Metabolome Repository family (XMRs): comparable untargeted metabolome databases for analyzing sample-specific unknown metabolites

The identification of unknown chemicals has emerged as a significant issue in untargeted metabolome analysis owing to the limited availability of purified standards for identification; this is a major bottleneck for the accumulation of reusable metabolome data in systems biology. Public resources for discovering and prioritizing the unknowns that should be subject to practical identification, as well as further detailed study of spending costs and the risks of misprediction, are lacking. As such a resource, we released databases, Food-, Plant- and Thing-Metabolome Repository (http://metabolites.in/foods, http://metabolites.in/plants, and http://metabolites.in/things, referred to as XMRs) in which the sample-specific localization of unknowns detected by liquid chromatography-mass spectrometry in a wide variety of samples can be examined, helping to discover and prioritize the unknowns. A set of application programming interfaces for the XMRs facilitates the use of metabolome data for large-scale analysis and data mining. Several applications of XMRs, including integrated metabolome and genome analyses, are presented. Expanding the concept of XMRs will accelerate the identification of unknowns and increase the discovery of new knowledge.

Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets

Covalent or non-covalent heterogeneous multimerization of molecules associated with extracts from biological samples analyzed via LC-MS are quite difficult to recognize/annotate and therefore the prevalence of multimerization remains largely unknown. In this study, we utilized 13C labeled and unlabeled Pichia pastoris extracts to recognize heterogeneous multimers. More specifically, between 0.8% and 1.5% of the biologically-derived features detected in our experiments were confirmed to be heteromers, about half of which we could successfully annotate with monomeric partners. Interestingly, we found specific chemical classes such as nucleotides to disproportionately contribute to heteroadducts. Furthermore, we compiled these compounds into the first MS/MS library that included data from heteromultimers to provide a starting point for other labs to improve the annotation of such ions in other metabolomics data sets. Then, the detected heteromers were also searched in publicly accessible LC-MS datasets available in Metabolights, Metabolomics WB and GNPS/MassIVE to demonstrate that these newly annotated ions are also relevant to other public datasets. Furthermore, in additional datasets (Triticum aestivum, Fusarium graminearum, and Trichoderma reesei) our developed workflow also detected 0.5%-4.9% of metabolite features to originate from heterodimers, demonstrating heteroadducts to be present in metabolomics studies at a low percentage.

How Soft Is Secondary Electrospray Ionization?

Secondary electrospray ionization (SESI) mass spectrometry (MS) is a direct infusion technique often used for untargeted metabolomics, e.g., for online breath analysis. SESI is thought to be a soft ionization method, which is important to avoid interference from in-source fragments and to simplify compound annotation. In this work, benzylammonium ions, formed from volatile benzylamines, with known bond dissociation enthalpies were used as thermometer ions to investigate the internal energy distribution of ions that are produced by SESI. It is shown that SESI is softer than electrospray ionization (ESI), and therefore, SESI indeed qualifies as a soft ionization technique. However, we also found that the standard MS instrument settings used in the SESI community are relatively harsh. Proper soft tuning of the instrument is essential to fully benefit from the softness that SESI can provide. Moreover, there is evidence from in-source collision-induced dissociation (CID) experiments that analytes can be solvated in SESI under soft conditions, which supports a recently proposed SESI mechanism referred to as ligand switching.

Structure similarity and molecular networking analysis for the discovery of polyphenol biotransformation products of gut microbes

Intestinal bacteria can metabolize polyphenols into highly bioavailable derivatives, which provide potential health-promoting effects to the host. However, the metabolic pathways and related products in this process are still largely unclear. Polyphenols are generally characterized by the presence of many phenolic structural units, which makes it possible to explore correlations among compounds based on similar molecular networks. In this study, we developed a standard-oriented/database-assisted molecular networking (SODA-MN) method for iterative compound annotation analysis to explore the metabolic profiles of polyphenol-rich black raspberry extract metabolized by representative gut bacteria. Starting from a group of polyphenol metabolites, the SODA-MN method predicted the possible polyphenol derivatives by adding or deducting common biotransformation groups and iterative annotating of structure similarity based on fragmentation spectra. Our results showed that 48 polyphenol derivatives in the first round of analysis alone (fragmentation match≥5, spec score >0.5) can be annotated, which were associated with 13 detected polyphenol standards that served as seed compounds. Meanwhile, this method was applied to a time-course study to show the time-dependent changes of polyphenols metabolized by a mix of gut bacteria. In addition, the metabolic capabilities of polyphenols among four representative gut bacteria were compared via our newly developed method and differential polyphenol metabolites were detected. In summary, the SODA-MN method provides a new approach for the annotation of unknown compounds by structure similarity and molecular networking analysis. Our analysis results could provide identification of key polyphenol derivatives that may contribute to the mechanistic investigations of their functions and assist our understanding of how polyphenols and gut bacteria interact to promote human health.

A multidimensional metabolomics workflow to image biodistribution and evaluate pharmacodynamics in adult zebrafish

An integrated evaluation of the tissue distribution and pharmacodynamic properties of a therapeutic is essential for successful translation to the clinic. To date, however, cost-effective methods to measure these parameters at the systems level in model organisms are lacking. Here, we introduce a multidimensional workflow to evaluate drug activity that combines mass spectrometry-based imaging, absolute drug quantitation across different biological matrices, in vivo isotope tracing and global metabolome analysis in the adult zebrafish. As a proof of concept, we quantitatively determined the whole-body distribution of the anti-rheumatic agent hydroxychloroquine sulfate (HCQ) and measured the systemic metabolic impacts of drug treatment. We found that HCQ distributed to most organs in the adult zebrafish 24 h after addition of the drug to water, with the highest accumulation of both the drug and its metabolites being in the liver, intestine and kidney. Interestingly, HCQ treatment induced organ-specific alterations in metabolism. In the brain, for example, HCQ uniquely elevated pyruvate carboxylase activity to support increased synthesis of the neuronal metabolite, N-acetylaspartate. Taken together, this work validates a multidimensional metabolomics platform for evaluating the mode of action of a drug and its potential off-target effects in the adult zebrafish. This article has an associated First Person interview with the first author of the paper.

AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications

Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.

Mutations in Rht-B1 Locus May Negatively Affect Frost Tolerance in Bread Wheat

The wheat semi-dwarfing genes Rht (Reduced height) are widely distributed among the contemporary wheat varieties. These genes also exert pleiotropic effects on plant tolerance towards various abiotic stressors. In this work, frost tolerance was studied in three near-isogenic lines of the facultative variety ‘April Bearded’ (AB), carrying the wild type allele Rht-B1a (tall phenotype), and the mutant alleles Rht-B1b (semi-dwarf) and Rht-B1c (dwarf), and was further compared with the tolerance of a typical winter type variety, ‘Mv Beres’. The level of freezing tolerance was decreasing in the order ‘Mv Beres’ > AB Rht-B1a > AB Rht-B1b > AB Rht-B1c. To explain the observed differences, cold acclimation-related processes were studied: the expression of six cold-related genes, the phenylpropanoid pathway, carbohydrates, amino acids, polyamines and compounds in the tricarboxylic acid cycle. To achieve this, a comprehensive approach was applied, involving targeted analyses and untargeted metabolomics screening with the help of gas chromatography/liquid chromatography—mass spectrometry setups. Several cold-related processes exhibited similar changes in these genotypes; indeed, the accumulation of eight putrescine and agmatine derivatives, 17 flavones and numerous oligosaccharides (max. degree of polymerization 18) was associated with the level of freezing tolerance in the ‘April Bearded’ lines. In summary, the mutant Rht alleles may further decrease the generally low frost tolerance of the Rht-B1a, and, based on the metabolomics study, the mechanisms of frost tolerance may differ for a typical winter variety and a facultative variety. Present results point to the complex nature of frost resistance.

The Origin and Implications of Artifact Ions in Bioanalytical LC–MS

Liquid chromatography–mass spectrometry (LC–MS) with electrospray ionization (ESI) is a widely used bioanalytical technique with both qualitative and quantitative applications. Ions are created by electrically charging a stream of droplets from the LC system, which evaporate and leave ions that are transferred to the mass spectrometer. Ideally, these are only from the analyte, but background ions, such as metals, impurities and coeluted species, can react with analytes producing adducts, such as [M + Na]+, [M + K]+, and multimers (2M + H+, 3M + H+, and so forth). Although well known, the extent of adduct ion formation and the implications for quantitative analysis and analyte characterization by tandem MS (MS/MS) are not fully appreciated. We summarize the problem and identify areas that should be considered when developing or using electrospray LC–MS.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (t_R), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The t_R values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

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.

MAFFIN: Metabolomics Sample Normalization Using Maximal Density Fold Change with High-Quality Metabolic Features and Corrected Signal Intensities

MOTIVATION

Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms.

RESULTS

MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics data sets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (1) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (2) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control (QC) samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis (PCA) and more confirmed significantly altered metabolites.

AVAILABILITY AND IMPLEMENTATION

The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction, and demo data are available at https://github.com/HuanLab/MAFFIN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Resolving isobaric interferences in direct infusion tandem mass spectrometry

RATIONALE

The co-fragmentation of precursors in direct infusion (DI) tandem high-resolution mass spectrometry (HRMS) can complicate the fragment spectra and consequently lead to false hits during compound identification.

METHODS

The method herein described, termed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra), modulates the intensities of precursors and fragments by stepwise movement of the quadrupole isolation window over the mass-to-charge (m/z) range of the precursors. The modulated signals are then deconvoluted by a linear regression model to reconstruct the fragment spectra with less interference. The hardware to demonstrate the use of IQAROS was an orbitrap with electrospray ionization (ESI) or secondary electrospray ionization (SESI), although the method can also be applied to other ionization techniques or mass analyzers.

RESULTS

Assessing the performance of IQAROS with isobaric standards revealed that the reconstructed fragment spectra match with spectra acquired from the pure standards and that more compounds were correctly identified compared with the classical approach with the quadrupole centered at the m/z value of the precursor of interest. Moreover, the strength of IQAROS is exemplified by the identification of two isobaric biomarkers directly from a breath sample with SESI-HRMS.

CONCLUSIONS

With IQAROS, cleaner fragment spectra of co-fragmenting isobars during DI-HRMS analysis can be obtained. IQAROS can easily be set up by the standard graphical user interface of the instrument. Therefore, it facilitates the characterization of features of interest in samples analyzed by DI-HRMS, for example, in high-throughput or real-time metabolomics.

An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body

Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.

Artifactual FA dimers mimic FAHFA signals in untargeted metabolomics pipelines

FA esters of hydroxy FAs (FAHFAs) are lipokines with extensive structural and regional isomeric diversity that impact multiple physiological functions, including insulin sensitivity and glucose homeostasis. Because of their low molar abundance, FAHFAs are typically quantified using highly sensitive LC-MS/MS methods. Numerous relevant MS databases house in silico-spectra that allow identification and speciation of FAHFAs. These provisional chemical feature assignments provide a useful starting point but could lead to misidentification. To address this possibility, we analyzed human serum with a commonly applied high-resolution LC-MS untargeted metabolomics platform. We found that many chemical features are putatively assigned to the FAHFA lipid class based on exact mass and fragmentation patterns matching spectral databases. Careful validation using authentic standards revealed that many investigated signals provisionally assigned as FAHFAs are in fact FA dimers formed in the LC-MS pipeline. These isobaric FA dimers differ structurally only by the presence of an olefinic bond. Furthermore, stable isotope-labeled oleic acid spiked into human serum at subphysiological concentrations showed concentration-dependent formation of a diverse repertoire of FA dimers that analytically mimicked FAHFAs. Conversely, validated FAHFA species did not form spontaneously in the LC-MS pipeline. Together, these findings underscore that FAHFAs are endogenous lipid species.  However, nonbiological FA dimers forming in the setting of high concentrations of FFAs can be misidentified as FAHFAs. Based on these results, we assembled a FA dimer database to identify nonbiological FA dimers in untargeted metabolomics datasets.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box"

Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).

NP Analyst: An Open Online Platform for Compound Activity Mapping

Few tools exist in natural products discovery to integrate biological screening and untargeted mass spectrometry data at the library scale. Previously, we reported Compound Activity Mapping as a strategy for predicting compound bioactivity profiles directly from primary screening results on extract libraries. We now present NP Analyst, an open online platform for Compound Activity Mapping that accepts bioassay data of almost any type, and is compatible with mass spectrometry data from major instrument manufacturers via the mzML format. In addition, NP Analyst will accept processed mass spectrometry data from the MZmine 2 and GNPS open-source platforms, making it a versatile tool for integration with existing discovery workflows. We demonstrate the utility of this new tool for both the dereplication of known compounds and the discovery of novel bioactive natural products using a challenging low-resolution antimicrobial bioassay data set. This new platform is available at www.npanalyst.org.

Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners

Background

Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO₂max) and longer-term outcomes, including the development of diabetes and its complications.

Methods

Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO₂max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.

Results

NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO₂max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO₂max.

Conclusion

These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO₂max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.

Trial registration

ClinicalTrials.gov, NCT02150889.

Funding

NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.

Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features

Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.