Identification of small molecules using accurate mass MS/MS search

Metabolomics of Chinese Hamster Ovary Cells

Increasing demand of protein biotherapeutics produced using Chinese hamster ovary (CHO) cell lines necessitates improvement in the production yield of the bioprocess. Various cell engineering, improved media formulation and process-design based approaches utilizing the power of OMICS technologies, specifically, genomics and proteomics, have been employed; however, the potential of metabolomics largely remains unexplored. Metabolomics enables the detection, identification, and/or quantitation of small molecules, commonly known as metabolites, in and around the cells and may help to unlock the cellular molecular mechanism(s) that regulates cell growth and productivity in the bioprocess and improves cellular performance during the bioprocess. Currently, liquid chromatography (LC)/gas chromatography (CG)- coupled with mass-spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the most commonly used approaches for metabolomics. Therefore, in this chapter, we have discussed the standard procedures of investigating CHO metabolites using LC/GC-MS and/or NMR-based approaches.

Isomer Differentiation by UHPLC-Q-Exactive-Orbitrap MS led to Enhanced Identification of Daphnane Diterpenoids in Daphne tangutica

INTRODUCTION

Liquid chromatography-mass spectrometry (LC-MS) has enhanced the rapid, accurate analysis of complex plant extracts, eliminating the need for extensive isolation. Tandem mass spectrometry (MS/MS) further enhances this process by providing detailed structural information. However, differentiating structural isomers remains a challenge due to their minor spectral and structural differences.

OBJECTIVE

This study aimed to extend the applicability of LC-MS/MS for the structural identification of daphnane diterpenoids, with a particular focus on distinguishing functional isomers.

METHODS

LC-MS analyses were performed using an UHPLC-Q-Exactive-Orbitrap MS. The MS conditions for distinguishing isomers were optimized using in-source CID and HCD modes with reference compounds. A qualitative analysis was then conducted on the extract of Daphne tangutica. The chemical structures of the detected daphnane diterpenoids were estimated by analyzing the fragmentation patterns in both the mass spectra and product ion spectra. These identifications were further validated by isolation and comparison with an in-house daphnane diterpenoid library.

RESULTS

By optimizing MS conditions, especially in the negative ion mode, it was possible to accurately distinguish structural isomers such as yuanhuajine and gniditrin. Qualitative analysis of D. tangutica identified a total of 28 daphnanes, including seven previously unreported compounds. Furthermore, a novel geometric isomer of gniditrin was isolated by conducting isolation on the crude diterpenoid fraction.

CONCLUSION

This study demonstrated that LC-MS/MS analysis can effectively distinguish functional isomers of daphnane diterpenoids, thereby enhancing the identification of daphnanes in plant extracts and highlighting its potential as a powerful tool for phytochemical analysis.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Omics based technology application in poultry meat research

Omics techniques, including genomics, transcriptomics, proteomics, metabolomics, and lipidomics, analyze entire sets of biological molecules to seek comprehensive knowledge on a particular phenotype. These approaches have been extensively utilized to identify both biomarkers and biological mechanisms for various physiological conditions in livestock and poultry. The purpose of this symposium was not only to focus on how recent omics technologies can be used to gather, integrate, and interpret data produced by various methodologies in poultry research, but also to highlight how omics and bioinformatics have increased our understanding of poultry meat quality problems and other complex traits. This Poultry Science Association symposium paper includes 5 sections that cover: 1) functional annotation of cis-regulatory elements in the genome informs genetic control of complex traits in poultry, 2) mass spectrometry for proteomics, metabolomics, and lipidomics, 3) proteomic approaches to investigate meat quality, 4) spatial transcriptomics and metabolomics studies of wooden breast disease, and 5) multiomics analyses on chicken meat quality and spaghetti meat. These topics provide insights into the molecular components that contribute to the structure, function, and dynamics of the underlying mechanisms influencing meat quality traits, including chicken breast myopathies. This information will ultimately contribute to improving the quality and composition of poultry products.

A mass spectrometry database for the identification of marine animal saponin-related metabolites

Saponins encompass a diverse group of naturally occurring glycoside molecules exhibiting amphiphilic properties and a broad range of biological activities. There is a resurgence of interest in those saponins produced by marine organisms based on their potential therapeutic benefits, application in food products and most recently their potential involvement in intra- and inter-species chemical communication. The continual advancements in liquid chromatography techniques and mass spectrometry technologies have allowed for greater detection rates, as well as improved isolation and elucidation of saponins. These factors have significantly contributed to the expansion in the catalogue of known saponin structures isolated from marine invertebrates; however, there currently exists no specific chemical library resource to accelerate the discovery process. In this study, a Marine Animal Saponin Database (MASD v1.0) has been developed to serve as a valuable chemical repository for known marine saponin-related data, including chemical formula, molecular mass and biological origin of nearly 1000 secondary metabolites associated with saponins produced by marine invertebrates. We demonstrate its application with an exemplar asteroid extract (Acanthaster cf. solaris, also known as crown-of-thorns starfish; COTS), identifying saponins from the MASD v1.0 that have been previously reported from COTS, as well as 21 saponins isolated from multiple other related asteroid species. This database will help facilitate future research endeavours, aiding researchers in exploring the vast chemical diversity of saponins produced by marine organisms and providing ecological insights, and the realisation of their potential for various applications, including as pharmaceuticals.

Multimodal chemical speciation techniques based on simultaneous high resolution molecular/atomic mass spectrometry applied to online target/non-target analysis: A tutorial review

BACKGROUND

If identifying target species is challenging regarding chemical speciation, non-target species present even more significant difficulties. Thus, to improve the performance of the methods, multimodal online coupling involving atomic and molecular mass spectrometry (LC-ICP-MS-ESI-HRMS) is an advance in this direction. Then, this kind of coupling is highlighted in this Tutorial Review, as well as some references emphasizing its potentialities and possible limitations. Some crucial definitions of speciomics, chemical speciation, and others are also included.

RESULTS

The main parameters that influence the coupling of an inductively coupled plasma mass spectrometer with a high-resolution mass spectrometer through a chromatographic system are critically commented on, and a diversity of results is demonstrated by using a turtle liver (Caretta caretta) as a model sample. The parameters were discussed in detail in a step-by-step manner: ICP-MS/MS acquisition modes and instrumental parameters, HRMS acquisition modes and instrumental parameters, and data processing strategies (Full MS - Top N, All Ion Fragmentation - AIF, Parallel Reaction Monitoring - PRM). Additionally, this Tutorial Review also demonstrates a diversity of results through target and non-target analysis.

SIGNIFICANCE

Constituting a guide for those who are interested in a non-targeted analysis of molecular non-volatile/semi-volatile compounds, this Tutorial Review presents trans and multidisciplinary proposals for those communities involving chemistry, biochemistry, medicine, biology, environmental, pharmaceutical, food safety, and omics, among others, where metal (also metalloids or semi-metals and non-metals or heteroatoms) and molecular species are necessary for a good understanding of the studied system. This kind of coupling also allows the discovery of novel biological active elemental species in diverse matrices.

Production of marine-derived bioactive peptide molecules for industrial applications: A reverse engineering approach

This review examines a wide range of marine microbial-derived bioactive peptide molecules, emphasizing the significance of reverse engineering in their production. The discussion encompasses the advancements in Marine Natural Products (MNPs) bio-manufacturing through the integration of omics-driven microbial engineering and bioinformatics. The distinctive features of non-ribosomally synthesised peptides (NRPs), and ribosomally synthesised precursor peptides (RiPP) biosynthesis is elucidated and presented. Additionally, the article delves into the origins of common peptide modifications. It highlights various genome mining approaches for the targeted identification of Biosynthetic Gene Clusters (BGCs) and novel RiPP and NRPs-derived peptides. The review aims to demonstrate the advancements, prospects, and obstacles in engineering both RiPP and NRP biosynthetic pathways.

Changes in the small-molecule fingerprints of rice planted near an industrial explosion site in Taiwan

A fire and explosion accident at a petrochemical complex sparked concerns over the rice health and production in nearby paddy fields. To unveil the potential effects, this study investigated small molecule changes in rice harvested in nearby counties using non-target analysis. Rice grains were harvested three, eight, 15, and 20 months after the accident from a total of ten townships. Small-molecule (m/z 70-1100) data in brown rice (n = 27) were acquired using high-resolution mass spectrometry (HRMS). Partial least squares discriminant analysis (PLS-DA) models were constructed to illustrate the temporal and spatial trends of rice's small-molecule fingerprints, and markers of production locations were identified. The small-molecule fingerprint in the rice directly exposed to the accident and harvested three months after the explosion differed significantly from those planted after the accident (PLS-DA model Q2 = 0.943, Q2/R2Y = 0.962), probably indicating the exclusion of long-term effects. Besides, in the rice directly exposed to the accident, the rice collected from near the explosion site (< 15 km) exhibited reduced jasmonic acid and increased imidacloprid levels (log2 fold change: -1.53 and 5.46, respectively), compared to that from farther locations. The result would suggest compromised disease defence in rice grown under the stress of explosion. In addition, lipid and amino acid metabolism perturbations are deemed relevant to plant development.

JESTR: Joint Embedding Space Technique for Ranking Candidate Molecules for the Annotation of Untargeted Metabolomics Data

Motivation

A major challenge in metabolomics is annotation: assigning molecular structures to mass spectral fragmentation patterns. Despite recent advances in molecule-to-spectra and in spectra-to-molecular fingerprint prediction (FP), annotation rates remain low.

Results

We introduce in this paper a novel paradigm (JESTR) for annotation. Unlike prior approaches that explicitly construct molecular fingerprints or spectra, JESTR leverages the insight that molecules and their corresponding spectra are views of the same data and effectively embeds their representations in a joint space. Candidate structures are ranked based on cosine similarity between the embeddings of query spectrum and each candidate. We evaluate JESTR against mol-to-spec and spec-to-FP annotation tools on three datasets. On average, for rank@[1-5], JESTR outperforms other tools by 23.6% - 71.6%. We further demonstrate the strong value of regularization with candidate molecules during training, boosting rank@1 performance by 11.4% and enhancing the model's ability to discern between target and candidate molecules. Through JESTR, we offer a novel promising avenue towards accurate annotation, therefore unlocking valuable insights into the metabolome.

Availability

Code and dataset available at https://github.com/HassounLab/JESTR1/.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

Tumor-Promoted Changes in Pediatric Brain Histology Can Be Distinguished from Normal Parenchyma by Desorption Electrospray Ionization Mass Spectrometry Imaging

Background: Central nervous system (CNS) tumors are the second most frequent type of neoplasm in childhood and adolescence, after leukemia. Despite the incorporation of molecular classification and improvement of protocols combining chemotherapy, surgery, and radiotherapy, CNS tumors are still the most lethal neoplasm in this age group. Mass spectrometry imaging (MSI) is a powerful tool to map the distribution of molecular species in tissue sections. Among MSI techniques, desorption electrospray ionization (DESI-MSI) has been demonstrated to enable reliable agreement with the pathological evaluation of different adult cancer types, along with an acceptable time scale for intraoperative use. Methods: In the present work, we aimed to investigate the chemical profile obtained by DESI-MSI as an intraoperative surgical management tool by profiling 162 pediatric brain biopsies and reporting the results according to the histopathology and molecular profile of the tumors. Results: The 2D chemical images obtained by DESI-MSI allowed us to distinguish tumor-transformed tissue from non-tumor tissue with an accuracy of 96.8% in the training set and 94.3% in the validation set after statistical modeling of our data using Lasso. In addition, high-grade and low-grade tumors also displayed a distinct chemical profile when analyzed by DESI-MSI. We also provided evidence that the chemical profile of brain tumors obtained by DESI-MSI correlates with methylation-based molecular classes and specific immunophenotypes found in brain biopsies. Conclusions: The results presented herein support the incorporation of DESI-MSI analysis as an intraoperative assistive tool in prospective clinical trials for pediatric brain tumors management in the near future.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

High-performance liquid chromatography coupled to Orbitrap mass spectrometry for screening of common new psychoactive substances and other drugs in biological samples

The complexity of the drug market and the constant updating of drugs have been challenging issues for drug regulatory authorities. With the emergence of new psychoactive substances (NPS) and the nonmedical use of prescription drugs, forensic and toxicology laboratories have had to adopt new drug screening methods and advanced instrumentation. Using high-performance liquid chromatography coupled with Orbitrap mass spectrometry, we developed a screening method for common NPS and other drugs. Two milliliters of mixed solvent of n-hexane and ethyl acetate (1:1, v:v) were added to 500 μL of blood or urine sample for liquid-liquid extraction, and methanol extraction was used for hair samples. The developed method was applied to 3897 samples (including 332 blood samples, 885 urine samples, and 2680 hair samples) taken from drug addicts in a province of China during 2019-2021. For urine and blood samples, the limits of detection (LODs) ranged from 1.68 pg/mL to 10.7 ng/mL. For hair samples, the LODs ranged from 3.30 × 10-5 to 4.21 × 10-3 ng/mg. The matrix effects of urine, blood, and hair samples were in the range of 47.6%-121%, 39.8%-139%, and 6.35%-118%, respectively. And the intra-day precision was 3.5%-6.0% and the inter-day precision was 4.18%-9.90%. Analysis of the actual samples showed an overall positive detection rate of 58.9%, with 5.32% of the samples indicating the use of multiple drugs.

MSBERT: Embedding Tandem Mass Spectra into Chemically Rational Space by Mask Learning and Contrastive Learning

Tandem mass spectrometry (MS/MS) is a powerful technique for chemical analysis in many areas of science. The vast MS/MS spectral data generated in liquid chromatography-mass spectrometry (LC-MS) experiments require efficient analysis and interpretation methods for the following compound identification. In this study, we propose MSBERT based on self-supervised learning strategies to embed MS/MS spectra into reasonable embeddings for efficient compound identification. It adopts the transformer encoder as the backbone for mask learning and uses the same spectra with different masks for contrastive learning. MSBERT is trained on the GNPS data set and tested on the GNPS data set, the MoNA data set, and the MTBLS1572 data set. It exhibits enhanced library matching and analogous compound searching capabilities compared to existing methods. The recalls at 1, 5, and 10 on a GNPS test subset with structures not in the training set are 0.7871, 0.8950, and 0.9080, respectively. The results are better than those of Spec2Vec with 0.6898, 0.8276, and 0.8620, and DreaMS with 0.7158, 0.8327, and 0.8635. The rationality of embeddings is demonstrated by t-SNE visualization, structural similarity, spectra clustering, compound identification, and analogous compound searching. A user-friendly web server is provided for efficient spectral analysis, and the source code for MSBERT is available at https://github.com/zhanghailiangcsu/MSBERT.

In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry

Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.

Advancing the Prediction of MS/MS Spectra Using Machine Learning

Tandem mass spectrometry (MS/MS) is an important tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. While popular, this strategy is limited by the contents of existing reference libraries. In response to this limitation, various methods are being developed for the in silico generation of spectra to augment existing libraries. Recently, machine learning and deep learning techniques have been applied to predict spectra with greater speed and accuracy. Here, we investigate the challenges these algorithms face in achieving fast and accurate predictions on a wide range of small molecules. The challenges are often amplified by the use of generic machine learning benchmarking tactics, which lead to misleading accuracy scores. Curating data sets, only predicting spectra for sufficiently high collision energies, and working more closely with experimental mass spectrometrists are recommended strategies to improve overall prediction accuracy in this nuanced field.

Identification of organic contaminants in water and related matrices using untargeted liquid chromatography high-resolution mass spectrometry screening with MS/MS libraries

Nontargeted and suspect screening with liquid chromatography-high resolution mass spectrometry (LC-HRMS) has become an indispensable tool for quality assessment in the aquatic environment - complementary to targeted analysis of organic (micro)contaminants. An LC-HRMS method is presented, suitable for the analysis of a wide variety of water related matrices: surface water, groundwater, wastewater, sediment and sludge, including extracts from passive samplers and on-site solid phase enrichment, while focusing on the data processing aspect of the method. A field study is included to demonstrate the practical application and versatility of the whole process. HRMS/MS data were recorded following LC separation in both (ESI) positive and negative ionization modes using data dependent as well as data independent acquisition. Two vendor (Agilent's Personal Compound Database and Library and from National Institute of Standards and Technology) and one open (MassBank/EU) tandem mass spectral libraries were utilized for the identification of compounds via mass spectral match. The development of a novel software tool for parsing, grouping and reduction of MS/MS features in data files converted to mascot generic format (MGF) helped to substantially decrease the amount of time and effort needed for MS library search. While applying the method, in the course of the entire field study, 18771 detections (from 870 individual compounds) in total were recorded in 275 samples, resulting in 68.3 identified compounds per sample, on average. Among the top ten most frequently detected contaminants across all samples and sample types were pharmaceutical compounds carbamazepine, 4-acetamidoantipyrine, 4-formylaminoantipyrine, tramadol, lamotrigine and phenazone and industrial contaminants toluene-2-sulfonamide, tolytriazole, tris(2-butoxyethyl) phosphate and benzotriazole. Exploratory data analysis methods and tools enabled us to discover organic pollutant occurrence patterns within the comprehensive sets of qualitative data collected from various projects between the years 2018-2023. The results may be used as valuable inputs for future water quality monitoring programs.

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Advances in metabolomics profiling of pediatric kidney diseases: A review

Pediatric renal diseases encompass a diverse array of pathological conditions, often engendering enduring ramifications. Metabolomics, an emergent branch of omics sciences, endeavors to holistically delineate alterations in metabolite compositions through the amalgamation of sophisticated analytical chemistry techniques and robust statistical methodologies. Recent advancements in metabolomics research within the realm of pediatric nephrology have been substantial, offering promising avenues for the identification of robust biomarkers, the elaboration of novel therapeutic targets, and the intricate elucidation of molecular mechanisms. The present discourse aims to critically review the progress in metabolomics profiling pertinent to pediatric renal disorders over the previous 12 years.

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

QC-GN2oMS2: a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

FragHub: A Mass Spectral Library Data Integration Workflow

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

Phytochemical drivers of insect herbivory: a functional toolbox to support agroecological diversification

Plant metabolism is a key feature of biodiversity that remains underexploited in functional frameworks used in agroecology. Here, we study how phytochemical diversity considered at three organizational levels can promote pest control. In a factorial field experiment, we manipulated plant diversity in three monocultures and three mixed crops of oilseed rape to explore how intra- and interspecific phytochemical diversity affects pest infestation. We combined recent progress in metabolomics with classic metrics used in ecology to test a box of hypotheses grounded in plant defence theory. According to the hypothesis of 'phytochemically mediated coevolution', our study stresses the relationships between herbivore infestation and particular classes of specialized metabolites like glucosinolates. Among 178 significant relationships between metabolites and herbivory rates, only 20% were negative. At the plant level, phytochemical abundance and richness had poor predictive power on pest regulation. This challenges the hypothesis of 'synergistic effects'. At the crop cover level, in line with the hypothesis of 'associational resistance', the phytochemical dissimilarity between neighbouring plants limited pest infestation. We discuss the intricate links between associational resistance and bottom-up pest control. Bridging different levels of organization in agroecosystems helps to dissect the multi-scale relationships between phytochemistry and insect herbivory.

High-throughput identification of gut microbiome-dependent metabolites

A significant hurdle that has limited progress in microbiome science has been identifying and studying the diverse set of metabolites produced by gut microbes. Gut microbial metabolism produces thousands of difficult-to-identify metabolites, which present a challenge to study their roles in host biology. In recent years, mass spectrometry-based metabolomics has become one of the core technologies for identifying small metabolites. However, metabolomics expertise, ranging from sample preparation to instrument use and data analysis, is often lacking in academic labs. Most targeted metabolomics methods provide high levels of sensitivity and quantification, while they are limited to a panel of predefined molecules that may not be informative to microbiome-focused studies. Here we have developed a gut microbe-focused and wide-spectrum metabolomic protocol using liquid chromatography-mass spectrometry and bioinformatic analysis. This protocol enables users to carry out experiments from sample collection to data analysis, only requiring access to a liquid chromatography-mass spectrometry instrument, which is often available at local core facilities. By applying this protocol to samples containing human gut microbial metabolites, spanning from culture supernatant to human biospecimens, our approach enables high-confidence identification of >800 metabolites that can serve as candidate mediators of microbe-host interactions. We expect this protocol will lower the barrier to tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling hypothesis-driven mechanistic studies and accelerating our understanding of the gut microbiome at the chemical level.

Advances of Ion Mobility Platform for Plant Metabolomics

Metabolomics aims to profile the extensive array of metabolites that exists in different types of matrices using modern analytical techniques. These techniques help to separate, identify, and quantify the plethora of chemical compounds at various analytical platforms. Hence, ion mobility spectrometry (IMS) has emerged as an advanced analytical approach, exclusively owing to the 3D separation of metabolites and their isomers. Furthermore, separated metabolites are identified based on their mass fragmentation pattern and CCS (collision cross-section) values. The IMS provides an advanced alternative dimension to separate the isomeric metabolites with enhanced throughput with lesser chemical noise. Thus, the present review highlights the types, factors affecting the resolution, and applications of IMMS (Ion mobility mass spectrometry) for isomeric separations, and ionic contaminants in the plant samples. Furthermore, an overview of IMS-based applications for the identification of plant metabolites (volatile and non-volatile) over the last few decades has been discussed, followed by future assumptions for creating IM-based databases. Such approaches could be significant to accelerate and improve our knowledge of the vast chemical diversity found in plants.

Unveiling new myrsinoic acids and AChE ligands from Myrsine guianensis (Aubl.) Kuntze

Molecular dereplication and drug-like discovery are important tools for exploring the chemical profile of metabolites in a complex mixture. In order to establish a workflow for discovering novel acetylcholinesterase (AChE) ligands, we performed the chemical study of Myrsine guianensis (Aubl.) Kuntze (Primulaceae). To carry out the bioprospection, nine extracts were obtained from different parts of the plant. Through the dereplication approaches, seventeen metabolites were annotated. In order to confirm the putative inferences, a HPLC preparative method was developed to isolate three known myrsinoic acids, A(1), B(2) and C(3). Along with, we are reporting the obtention of two new congeners, G(5) and H(6), which their structures were elucidated by NMR and HRMS data. Besides that, two extracts were submitted to affinity assays to accelerate the discovery of AChE ligands. Desorbates were analyzed through LC-HRMS for calculating the affinity ratio (AR). Thus, (1) presented AR = 4.59, therefore was considered a potential ligand.

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.

Changes in Black Truffle (Tuber melanosporum) Aroma during Storage under Different Conditions

The enticing aroma of truffles is a key factor for their culinary value. Although all truffle species tend to be pricy, the most intensely aromatic species are the most sought after. Research into the aroma of truffles encompasses various disciplines including chemistry, biology, and sensory science. This study focusses on the chemical composition of the aroma of black truffles (Tuber melanosporum) and the changes occurring under different storage conditions. For this, truffle samples were stored under different treatments, at different temperatures, and measured over a total storage time of 12 days. Measurements of the truffle aroma profiles were taken with SPME/GC-MS at regular intervals. To handle the ample data collected, a systematic approach utilizing multivariate data analysis techniques was taken. This approach led to a vast amount of data which we made publicly available for future exploration. Results reveal the complexity of aroma changes, with 695 compounds identified, highlighting the need for a comprehensive understanding. Principal component analyses offer initial insights into truffle composition, while individual compounds may serve as markers for age (formic acid, 1-methylpropyl ester), freshness (2-Methyl-1-propanal; 1-(methylthio)-propane), freezing (tetrahydrofuran), salt treatment (1-chloropentane), or heat exposure (4-hydroxy-3-methyl-2-butanone). This research suggests that heat treatment or salt contact significantly affects truffle aroma, while freezing and cutting have less pronounced effects in comparison. The enrichment of compounds showing significant changes during storage was investigated with a metabolomic pathway analysis. The involvement of some of the enriched compounds on the pyruvate/glycolysis and sulfur pathways was shown.

Structural annotation of unknown molecules in a miniaturized mass spectrometer based on a transformer enabled fragment tree method

Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution.

Exploring the specialized metabolome of the plant pathogen Streptomyces sp. 11-1-2

Streptomyces bacteria are notable for producing chemically diverse specialized metabolites that exhibit various bioactivities and mediate interactions with different organisms. Streptomyces sp. 11-1-2 is a plant pathogen that produces nigericin and geldanamycin, both of which display toxic effects against various plants. Here, the 'One Strain Many Compounds' approach was used to characterize the metabolic potential of Streptomyces sp. 11-1-2. Organic extracts were prepared from 11-1-2 cultures grown on six different agar media, and the extracts were tested in antimicrobial and plant bioassays and were subjected to untargeted metabolomics and molecular networking. Most extracts displayed strong bioactivity against Gram-positive bacteria and yeast, and they exhibited phytotoxic activity against potato tuber tissue and radish seedlings. Several known specialized metabolites, including musacin D, galbonolide B, guanidylfungin A, meridamycins and elaiophylin, were predicted to be present in the extracts along with closely related compounds with unknown structure and bioactivity. Targeted detection confirmed the presence of elaiophylin in the extracts, and bioassays using pure elaiophylin revealed that it enhances the phytotoxic effects of geldanamycin and nigericin on potato tuber tissue. Overall, this study reveals novel insights into the specialized metabolites that may mediate interactions between Streptomyces sp. 11-1-2 and other bacteria and eukaryotic organisms.

Workflow to facilitate the detection of new psychoactive substances and drugs of abuse in influent urban wastewater

The complexity around the dynamic markets for new psychoactive substances (NPS) forces researchers to develop and apply innovative analytical strategies to detect and identify them in influent urban wastewater. In this work a comprehensive suspect screening workflow following liquid chromatography - high resolution mass spectrometry analysis was established utilising the open-source InSpectra data processing platform and the HighResNPS library. In total, 278 urban influent wastewater samples from 47 sites in 16 countries were collected to investigate the presence of NPS and other drugs of abuse. A total of 50 compounds were detected in samples from at least one site. Most compounds found were prescription drugs such as gabapentin (detection frequency 79%), codeine (40%) and pregabalin (15%). However, cocaine was the most found illicit drug (83%), in all countries where samples were collected apart from the Republic of Korea and China. Eight NPS were also identified with this protocol: 3-methylmethcathinone 11%), eutylone (6%), etizolam (2%), 3-chloromethcathinone (4%), mitragynine (6%), phenibut (2%), 25I-NBOH (2%) and trimethoxyamphetamine (2%). The latter three have not previously been reported in municipal wastewater samples. The workflow employed allowed the prioritisation of features to be further investigated, reducing processing time and gaining in confidence in their identification.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.

The Metabolic and Lipidomic Fingerprint of Torin1 Exposure in Mouse Embryonic Fibroblasts Using Untargeted Metabolomics

Torin1, a selective kinase inhibitor targeting the mammalian target of rapamycin (mTOR), remains widely used in autophagy research due to its potent autophagy-inducing abilities, regardless of its unspecific properties. Recognizing the impact of mTOR inhibition on metabolism, our objective was to develop a reliable and thorough untargeted metabolomics workflow to study torin1-induced metabolic changes in mouse embryonic fibroblast (MEF) cells. Crucially, our quality assurance and quality control (QA/QC) protocols were designed to increase confidence in the reported findings by reducing the likelihood of false positives, including a validation experiment replicating all experimental steps from sample preparation to data analysis. This study investigated the metabolic fingerprint of torin1 exposure by using liquid chromatography-high resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics platforms. Our workflow identified 67 altered metabolites after torin1 exposure, combining univariate and multivariate statistics and the implementation of a validation experiment. In particular, intracellular ceramides, diglycerides, phosphatidylcholines, phosphatidylethanolamines, glutathione, and 5'-methylthioadenosine were downregulated. Lyso-phosphatidylcholines, lyso-phosphatidylethanolamines, glycerophosphocholine, triglycerides, inosine, and hypoxanthine were upregulated. Further biochemical pathway analyses provided deeper insights into the reported changes. Ultimately, our study provides a valuable workflow that can be implemented for future investigations into the effects of other compounds, including more specific autophagy modulators.

Proteomics revealed composition- and size-related regulators for hepatic impairments induced by silica nanoparticles

Along with the growing production and application of silica nanoparticles (SiNPs), increased human exposure and ensuing safety evaluation have progressively attracted concern. Accumulative data evidenced the hepatic injuries upon SiNPs inhalation. Still, the understanding of the hepatic outcomes resulting from SiNPs exposure, and underlying mechanisms are incompletely elucidated. Here, SiNPs of two sizes (60 nm and 300 nm) were applied to investigate their composition- and size-related impacts on livers of ApoE-/- mice via intratracheal instillation. Histopathological and biochemical analysis indicated SiNPs promoted inflammation, lipid deposition and fibrosis in the hepatic tissue, accompanied by increased ALT, AST, TC and TG. Oxidative stress was activated upon SiNPs stimuli, as evidenced by the increased hepatic ROS, MDA and declined GSH/GSSG. Of note, these alterations were more dramatic in SiNPs with a smaller size (SiNPs-60) but the same dosage. LC-MS/MS-based quantitative proteomics unveiled changes in mice liver protein profiles, and filtered out particle composition- or size-related molecules. Interestingly, altered lipid metabolism and oxidative damage served as two critical biological processes. In accordance with correlation analysis and liver disease-targeting prediction, a final of 10 differentially expressed proteins (DEPs) were selected as key potential targets attributable to composition- (4 molecules) and size-related (6 molecules) liver impairments upon SiNPs stimuli. Overall, our study provided strong laboratory evidence for a comprehensive understanding of the harmful biological effects of SiNPs, which was crucial for toxicological evaluation to ensure nanosafety.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Tandem Mass Spectrometry across Platforms

PubChem serves as a comprehensive repository, housing over 100 million unique chemical structures representing the breadth of our chemical knowledge across numerous fields including metabolism, pharmaceuticals, toxicology, cosmetics, agriculture, and many more. Rapid identification of these small molecules increasingly relies on electrospray ionization (ESI) paired with tandem mass spectrometry (MS/MS), particularly by comparison to genuine standard MS/MS data sets. Despite its widespread application, achieving consistency in MS/MS data across various analytical platforms remains an unaddressed concern. This study evaluated MS/MS data derived from one hundred molecular standards utilizing instruments from five manufacturers, inclusive of quadrupole time-of-flight (QTOF) and quadrupole orbitrap "exactive" (QE) mass spectrometers by Agilent (QTOF), Bruker (QTOF), SCIEX (QTOF), Waters (QTOF), and Thermo QE. We assessed fragment ion variations at multiple collisional energies (0, 10, 20, and 40 eV) using the cosine scoring algorithm for comparisons and the number of fragments observed. A parallel visual analysis of the MS/MS spectra across instruments was conducted, consistent with a standard procedure that is used to circumvent the still prevalent issue of mischaracterizations as shown for dimethyl sphingosine and C20 sphingosine. Our analysis revealed a notable consistency in MS/MS data and identifications, with fragment ions' m/z values exhibiting the highest concordance between instrument platforms at 20 eV, the other collisional energies (0, 10, and 40 eV) were significantly lower. While moving toward a standardized ESI MS/MS protocol is required for dependable molecular characterization, our results also underscore the continued importance of corroborating MS/MS data against standards to ensure accurate identifications. Our findings suggest that ESI MS/MS manufacturers, akin to the established norms for gas chromatography mass spectrometry instruments, should standardize the collision energy at 20 eV across different instrument platforms.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Inhibition of corrosion on API 5L X52 pipeline steel in acid media by Tradescantia spathacea

The concentration effect of Tradescantia spathacea (T. spathacea) as corrosion inhibitor of API 5L X52 steel in 0.5 M of H2SO4 was studied here through electrochemical and gravimetric techniques. To achieve it, samples of the material were prepared to be submitted to each of the tests. Results from electrochemical impedance spectroscopy (EIS) showed that there was an optimum concentration of the inhibitor in which is reached the maximum inhibition efficiency, displaying the best inhibition characteristics for this system with a maximum inhibition of 89% by using 400 ppm. However, the efficiency decreased until 40% when the temperature was increased to 60°C. Potentiodynamic polarization curves (PDP) revealed that some of the present compounds of T. spathacea may affect anodic and cathodic process, so it can be classified as a mix-type corrosion inhibitor for API 5L X52 in sulfuric acid. Also, this compound followed an adsorption mechanism; this can be described through a Frumkin isotherm with an adsorption standard free energy difference (ΔG°) of -56.59 kJmol-1. Metal surface was studied through scanning electron microscope, results revealed that by adding inhibitor, the metal surface is protected; also, they evidenced low damages compared with the surface with no inhibitor. Finally, Tradescantia spathacea inhibited the corrosion process with 82% efficiency.

Chemical-tag-based semi-annotated metabolomics facilitates gene identification and specialized metabolic pathway elucidation in wheat

The importance of metabolite modification and species-specific metabolic pathways has long been recognized. However, linking the chemical structure of metabolites to gene function in order to explore the genetic and biochemical basis of metabolism has not yet been reported in wheat (Triticum aestivum). Here, we profiled metabolic fragment enrichment in wheat leaves and consequently applied chemical-tag-based semi-annotated metabolomics in a genome-wide association study in accessions of wheat. The studies revealed that all 1,483 quantified metabolites have at least one known functional group whose modification is tailored in an enzyme-catalyzed manner and eventually allows efficient candidate gene mining. A Triticeae crop-specific flavonoid pathway and its underlying metabolic gene cluster were elucidated in further functional studies. Additionally, upon overexpressing the major effect gene of the cluster TraesCS2B01G460000 (TaOMT24), the pathway was reconstructed in rice (Oryza sativa), which lacks this pathway. The reported workflow represents an efficient and unbiased approach for gene mining using forward genetics in hexaploid wheat. The resultant candidate gene list contains vast molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets and will ultimately aid in achieving wheat crop improvement.

Mobilize a Proton to Transform the Collision-Induced Dissociation Spectral Pattern of a Cyclic Peptide

The collision-induced dissociation (CID) behaviors of protonated molecules of anabaenopeptins, a group of cyanobacterial cyclic peptides, were investigated in detail using liquid chromatography-tandem mass spectrometry. Although anabaenopeptin A and B share a macrocyclic peptide structure, they give strikingly different fragmentation patterns; the former gives a variety of product ions including cleavages in the cyclic peptide structure, which is useful for structural analysis; whereas the latter gives far fewer product ions and no fragmentation in the cyclic moiety. Energy-resolved CID experiments clarified the mechanism behind the striking difference attributable to the difference in exocyclic amino acid residues, Tyr or Arg. The guanidino group in Arg-containing analogue, anabaenopeptin B, should be by far the most preferred protonation site; the proton would be sequestered at the guanidino group in the protonated molecule, with the lack of proton mobility prohibiting opening of the charge-directed fragmentation channels in the cyclic moiety. Enzymatic hydrolysis of the guanidino group to give citrullinated-anabaenopeptin B restored proton mobility. The fragmentation pattern of the citrullinated peptide became almost identical to that of anabaenopeptin A. The observed fragmentation behaviors of these cyclic peptides were consistent with those of linear peptides, which have been well understood based on the mobile proton model.

Mass Spectral Library for DNA Adductomics

Endogenous electrophiles, ionizing and non-ionizing radiation, and hazardous chemicals present in the environment and diet can damage DNA by forming covalent adducts. DNA adducts can form in critical cancer driver genes and, if not repaired, may induce mutations during cell division, potentially leading to the onset of cancer. The detection and quantification of specific DNA adducts are some of the first steps in studying their role in carcinogenesis, the physiological conditions that lead to their production, and the risk assessment of exposure to specific genotoxic chemicals. Hundreds of different DNA adducts have been reported in the literature, and there is a critical need to establish a DNA adduct mass spectral database to facilitate the detection of previously observed DNA adducts and characterize newly discovered DNA adducts. We have collected synthetic DNA adduct standards from the research community, acquired MSn (n = 2, 3) fragmentation spectra using Orbitrap and Quadrupole-Time-of-Flight (Q-TOF) MS instrumentation, processed the spectral data and incorporated it into the MassBank of North America (MoNA) database, and created a DNA adduct portal Web site (https://sites.google.com/umn.edu/dnaadductportal) to serve as a central location for the DNA adduct mass spectra and metadata, including the spectral database downloadable in different formats. This spectral library should prove to be a valuable resource for the DNA adductomics community, accelerating research and improving our understanding of the role of DNA adducts in disease.

Weighting Low-Intensity MS/MS Ions and m/z Frequency for Spectral Library Annotation

Calculating spectral similarity is a fundamental step in MS/MS data analysis in untargeted metabolomics experiments, as it facilitates the identification of related spectra and the annotation of compounds. To improve matching accuracy when querying an experimental mass spectrum against a spectral library, previous approaches have proposed increasing peak intensities for high m/z ranges. These high m/z values tend to be smaller in magnitude, yet they offer more crucial information for identifying the chemical structure. Here, we evaluate the impact of using these weights for identifying structurally related compounds and mass spectral library searches. Additionally, we propose a weighting approach that (i) takes into account the frequency of the m/z values within a spectral library in order to assign higher importance to the most common peaks and (ii) increases the intensity of lower peaks, similar to previous approaches. To demonstrate our approach, we applied weighting preprocessing to modified cosine, entropy, and fidelity distance metrics and benchmarked it against previously reported weights. Our results demonstrate how weighting-based preprocessing can assist in annotating the structure of unknown spectra as well as identifying structurally similar compounds. Finally, we examined scenarios in which the utilization of weights resulted in diminished performance, pinpointing spectral features where the application of weights might be detrimental.

Mapping the metabolic responses to oxaliplatin-based chemotherapy with in vivo spatiotemporal metabolomics

Adjuvant chemotherapy improves the survival outlook for patients undergoing operations for lung metastases caused by colorectal cancer (CRC). However, a multidisciplinary approach that evaluates several factors related to patient and tumor characteristics is necessary for managing chemotherapy treatment in metastatic CRC patients with lung disease, as such factors dictate the timing and drug regimen, which may affect treatment response and prognosis. In this study, we explore the potential of spatial metabolomics for evaluating metabolic phenotypes and therapy outcomes during the local delivery of the anticancer drug, oxaliplatin, to the lung. 12 male Yorkshire pigs underwent a 3 h left lung in vivo lung perfusion (IVLP) with various doses of oxaliplatin (7.5, 10, 20, 40, and 80 mg/L), which were administered to the perfusion circuit reservoir as a bolus. Biocompatible solid-phase microextraction (SPME) microprobes were combined with global metabolite profiling to obtain spatiotemporal information about the activity of the drug, determine toxic doses that exceed therapeutic efficacy, and conduct a mechanistic exploration of associated lung injury. Mild and subclinical lung injury was observed at 40 mg/L of oxaliplatin, and significant compromise of the hemodynamic lung function was found at 80 mg/L. This result was associated with massive alterations in metabolic patterns of lung tissue and perfusate, resulting in a total of 139 discriminant compounds. Uncontrolled inflammatory response, abnormalities in energy metabolism, and mitochondrial dysfunction next to accelerated kynurenine and aldosterone production were recognized as distinct features of dysregulated metabolipidome. Spatial pharmacometabolomics may be a promising tool for identifying pathological responses to chemotherapy.

Comparison of high-resolution mass spectrometry acquisition methods for the simultaneous quantification and identification of per- and polyfluoroalkyl substances (PFAS)

Simultaneous identification and quantification of per- and polyfluoroalkyl substances (PFAS) were evaluated for three quadrupole time-of-flight mass spectrometry (QTOF) acquisition methods. The acquisition methods investigated were MS-Only, all ion fragmentation (All-Ions), and automated tandem mass spectrometry (Auto-MS/MS). Target analytes were the 25 PFAS of US EPA Method 533 and the acquisition methods were evaluated by analyte response, limit of quantification (LOQ), accuracy, precision, and target-suspect screening identification limit (IL). PFAS LOQs were consistent across acquisition methods, with individual PFAS LOQs within an order of magnitude. The mean and range for MS-Only, All-Ions, and Auto-MS/MS are 1.3 (0.34-5.1), 2.1 (0.49-5.1), and 1.5 (0.20-5.1) pg on column. For fast data processing and tentative identification with lower confidence, MS-Only is recommended; however, this can lead to false-positives. Where high-confidence identification, structural characterisation, and quantification are desired, Auto-MS/MS is recommended; however, cycle time should be considered where many compounds are anticipated to be present. For comprehensive screening workflows and sample archiving, All-Ions is recommended, facilitating both quantification and retrospective analysis. This study validated HRMS acquisition approaches for quantification (based upon precursor data) and exploration of identification workflows for a range of PFAS compounds.

Structure Based Machine Learning Prediction of Retention Times for LC Method Development of Pharmaceuticals

PURPOSE

Significant resources are spent on developing robust liquid chromatography (LC) methods with optimum conditions for all project in the pipeline. Although, data-driven computer assisted modelling has been implemented to shorten the method development timelines, these modelling approaches require project-specific screening data to model retention time (RT) as function of method parameters. Sometimes method re-development is required, leading to additional investments and redundant laboratory work. Cheminformatics techniques have been successfully used to predict the RT of metabolites & other component mixtures for similar use cases. Here we will show that these techniques can be used to model structurally diverse molecules and predictions of these models trained on multiple LC conditions can be used for downstream data-driven modelling.

METHODS

The Molecular Operating Environment (MOE) was used to calculate over 800 descriptors using the strucutres of the analytes. These descriptors were used to model the RT of the analytes under four chromatographic conditions. These models were then used to create data-driven models using LC-SIM.

RESULTS

A structural-based Random Forest (RF) model outperformed other techniques in cross-validation studies and predicted the RTs of a randomized test set with a median percentage error less than 4% for all LC conditions. RTs predicted by this structure-based model were used to fit a data-driven model that identifies optimum LC conditions without any additional experimental work.

CONCLUSIONS

These results show that small training sets yield pharmaceutically relevant models when used in a combination of structure-based and data-driven model.

In silico MS/MS prediction for peptidoglycan profiling uncovers novel anti-inflammatory peptidoglycan fragments of the gut microbiota

Peptidoglycan is an essential exoskeletal polymer across all bacteria. Gut microbiota-derived peptidoglycan fragments (PGNs) are increasingly recognized as key effector molecules that impact host biology. However, the current peptidoglycan analysis workflow relies on laborious manual identification from tandem mass spectrometry (MS/MS) data, impeding the discovery of novel bioactive PGNs in the gut microbiota. In this work, we built a computational tool PGN_MS2 that reliably simulates MS/MS spectra of PGNs and integrated it into the user-defined MS library of in silico PGN search space, facilitating automated PGN identification. Empowered by PGN_MS2, we comprehensively profiled gut bacterial peptidoglycan composition. Strikingly, the probiotic Bifidobacterium spp. manifests an abundant amount of the 1,6-anhydro-MurNAc moiety that is distinct from Gram-positive bacteria. In addition to biochemical characterization of three putative lytic transglycosylases (LTs) that are responsible for anhydro-PGN production in Bifidobacterium, we established that these 1,6-anhydro-PGNs exhibit potent anti-inflammatory activity in vitro, offering novel insights into Bifidobacterium-derived PGNs as molecular signals in gut microbiota-host crosstalk.

Rapid Quantification of Ammonium Nitrate and Urea Nitrate Using Liquid Chromatography-High-Resolution Orbitrap Mass Spectrometry

Resolution and sensitivity improvements in mass spectrometry technology have enabled renewed attempts at solving challenging analytical issues. One such issue involves the analysis of energetic ionic species. Energetic ionic species make up an important class of chemical materials, and a more robust and versatile analytical platform would provide tremendous value to the analytical community. Initial attempts at quantification of energetic ionic species employed high-resolution time-of-flight measurements with crown ether (CE) complexation and flow injection analysis (FIA). In this investigation, ammonium nitrate (AN) and urea nitrate (UN) in the presence of a crown ether complexation agent were explored by using high-resolution orbitrap mass spectrometry. Product ion scans of these signature complexes reveal positive identification of these energetic ionic species. Finally, quantification was demonstrated for both flow injection and liquid chromatography-mass spectrometry (LC-MS) analysis, suggesting the capability for routine and rapid analysis of these energetic ionic materials.