Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology

Unlocking the future potential of SWATH-MS: Advancing non-target screening workflow for the qualitative and quantitative analysis of emerging contaminants

SWATH-MS offers a robust data-independent acquisition method for complex proteomics and metabolomics. This study presents a detailed non-target screening workflow utilizing SWATH-MS to detect and analyze emerging contaminants (ECs) in aquatic environments. Our workflow, covering peak picking, alignment, prioritization, structure identification, and quantification, effectively identified all qualifying peaks from 298 standard compounds with different concentrations, discarding any that did not meet the criteria. In extracts of real water samples spiked at 100 and 10 ng/mL, our workflow prioritized 2083 and 1328 features, respectively. Following structure identification, these features were assigned confidence levels ranging from 1 to 5. Of these, 215 and 92 spiked standards achieved level 1. The remaining standards were not recognized as level 1 due to low intensities or poor peak shapes that failed to meet certain criteria. Additionally, using fragment ion peak areas for quantification significantly improved the linearity of standard curves, enhancing R2 values for ∼63 % of the standards. Incorporating fragment ion data improved quantification accuracy, increasing compounds within the 80 %-120 % range from 78 % to 90 % at 100 ng/mL and within the 50 %-150 % range from 36 % to 69 % at 10 ng/mL. These findings underscore SWATH-MS's potential to enhance monitoring of ECs and ecological risk assessments, providing critical insights for environmental management.

Mass spectrometry-based metabolomic as a powerful tool to unravel the component and mechanism in TCM

Mass spectrometry (MS)-based metabolomics has emerged as a transformative tool to unraveling components and their mechanisms in traditional Chinese medicine (TCM). The integration of advanced analytical platforms, such as LC-MS and GC-MS, coupled with metabolomics, has propelled the qualitative and quantitative characterization of TCM's complex components. This review comprehensively examines the applications of MS-based metabolomics in elucidating TCM efficacy, spanning chemical composition analysis, molecular target identification, mechanism-of-action studies, and syndrome differentiation. Recent innovations in functional metabolomics, spatial metabolomics, single-cell metabolomics, and metabolic flux analysis have further expanded TCM research horizons. Artificial intelligence (AI) and bioinformatics integration offer promising avenues for overcoming analytical bottlenecks, enhancing database standardization, and driving interdisciplinary breakthroughs. However, challenges remain, including the need for improved data processing standardization, database expansion, and understanding of metabolite-gene-protein interactions. By addressing these gaps, metabolomics can bridge traditional practices and modern biomedical research, fostering global acceptance of TCM. This review highlights the synergy of advanced MS techniques, computational tools, and TCM's holistic philosophy, presenting a forward-looking perspective on its clinical translation and internationalization.

Establishment of a novel large-scale targeted metabolomics method based on NFSWI-DDA mode utilizing HRMS and TQ-MS

Metabolites identification is the major bottleneck in untargeted LC-MS metabolomics, primarily due to the limited availability of MS2 information for most detected metabolites in data dependent acquisition (DDA) mode. To solve this problem, we have integrated the iterative, interval, and segmented window acquisition concepts to develop an innovative non-fixed segmented window interval data dependency acquisition (NFSWI-DDA) mode, which achieves comparable MS2 coverage to data independent acquisition (DIA) mode. This acquisition strategy harnesses the strengths of both DDA and DIA, which could provide extensive coverage and excellent reproducibility of MS2 spectra. Furthermore, utilizing the NFSWI-DDA data, we successfully acquired and identified a large-scale of multiple reaction monitoring (MRM) ion pairs, and transitioned them from high-resolution mass spectrometry (HRMS) to triple quadrupole mass spectrometry (TQ-MS). At last, a large-scale targeted metabolomics method was established practically. This method enables targeted analysis of 475 endogenous metabolites encompassing amino acids, nucleotides, bile acids, fatty acids, and carnitines, which could cover 9 major metabolic pathways as well as 65 secondary metabolic pathways. The established targeted method allows for semi-quantitative assessment of 475 metabolites while enabling quantitative analysis of 327 specific metabolites in biological samples. The method demonstrates immense potential in the detection of various biological samples, offering robust technical support and generating extensive data to advance applications in precision medicine and life sciences.

Recent Advances in Nontargeted Screening of Chemical Hazards in Foodstuffs

The emergence of several chemical substances continues to enrich and facilitate the development of food science, but their irrational use also poses a threat to food safety and human health. Nontargeted screening (NTS) has become an important tool for rapid traceability and efficient identification of chemical hazards in food matrices. NTS in food analysis is highly integrated with sample pretreatment, instrumental analysis platforms, data acquisition and analysis, and toxicology. This article is a systemic review of current sample preparation, analytical platforms, and toxicity-guided NTS techniques and provides the latest advancements in workflows and innovative applications of the NTS process based on mass spectrometric techniques. High-throughput toxicity screening platforms play an important role in NTS of unknown chemical hazards of complex food matrices. Advanced machine learning and artificial intelligence are increasingly accessible fields that may effectively process large-scale screening data and advance food NTS research.

High-Resolution Tandem Mass Spectrometry for Metabolic Profiling of Ocotea diospyrifolia (Meisn.) Mez Leaves

Ocotea is an important genus of Lauraceae plant family that comprises over 400 species, many of which pose challenges in taxonomic differentiation due to their complex botanical characteristics. Chemosystematics, and more recently, chemophenetics, have emerged as valuable tools to address these challenges based on their natural products (NPs) composition. O. diospyrifolia (Meisn.) Mez is a poorly studied species with known pharmacological potential. Here, we applied ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS) allied to a curated in-house database with all previous isolated NPs from the Ocotea genus (OcoteaDB), gas phase fragmentations reactions, and biosynthesis. The strategy resulted in compounds annotated in confidence levels 2 (n=27), 3 (n=231), and 4 (n=21) according to the Metabolomics Standards Initiative (MSI). Additional annotations based on fragmentation proposals (n=16) were also included. The study revealed that O. diospyrifolia is a great alkaloid producer, even though different lignoids, which also comes from the shikimate pathway, were annotated. Additionally, the flavonoid profile predominantly consists of flavonol glycosides, complementing prior reports. This study provides the first comprehensive chemical profile of O. diospyrifolia leaves, which corroborates the chemotaxonomy of the species, and also contributes to further characterization studies, as the UHPLC-HRMS data is publicly available.

Microbiota-Gut-Brain Axis: Mass-Spectrometry-Based Metabolomics in the Study of Microbiome Mediators-Stress Relationship

The microbiota-gut-brain axis is a complex bidirectional communication system that involves multiple interactions between intestinal functions and the emotional and cognitive centers of the brain. These interactions are mediated by molecules (metabolites) produced in both areas, which are considered mediators. To shed light on this complex mechanism, which is still largely unknown, a reliable characterization of the mediators is essential. Here, we review the most studied metabolites in the microbiota-gut-brain axis, the metabolic pathways in which they are involved, and their functions. This review focuses mainly on the use of mass spectrometry for their determination, reporting on the latest analytical methods, their limitations, and future perspectives. The analytical strategy for the qualitative-quantitative characterization of mediators must be reliable in order to elucidate the molecular mechanisms underlying the influence of the above-mentioned axis on stress resilience or vulnerability.

Retention time-independent strategy for screening pesticide residues in herbs based on a fingerprint database and all ion fragmentation acquisition with LC-QTOF MS

A retention time (RT)-independent strategy for nontargeted screening of pesticide residues in herbs was exploited using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF MS). The core of this strategy is a fingerprint database coupled with a data-independent acquisition (DIA) scan mode of all ion fragmentation (AIF). In the fingerprint database, a total of 150 pesticides with quasimolecular ions and fragment ions at five-level collision energies were collected as qualified ions for screening. During the data acquisition, the AIF scan was performed via real unbiased full-spectrum MS/MS acquisition. Six herb matrices spiked with 30 banned pesticides were used to evaluate the applicability of the strategy in real samples. The use of the narrow ion mass extraction window (10 mDa) and the narrow RT window (0.1 min) enabled the effective extraction of spectra from noisy backgrounds and the discovery of suspected pesticides via similarity matching of filtered qualified ions. On average, more than 11/30 of pesticides at 1 ng mL-1 and more than 23/30 of pesticides at 10 ng mL-1 or lower could be screened out in each matrix using at least two qualified ions. In addition, the AIF mode exhibited superior anti-interference capability compared to data-dependent acquisition (DDA) and sequential window acquisition of all theoretical mass spectra (SWATH), as determined by comparing the limits of screening (LOSs) of 30 banned pesticides spiked into Isatidis Folium. Finally, the developed strategy was applied to screen pesticide residues in extracts of Ganoderma and Foeniculi Fructus. Phorate-sulfone and phorate-sulfoxide were found in Ganoderma, as well as terbufos-sulfone and terbufos-sulfoxide were found in Foeniculi Fructus. In conclusion, the developed RT-independent strategy based on a fingerprint database and AIF acquisition with LC-QTOF MS seems to be one of the most efficient tools for the analysis of nontargeted pesticide residues in complicated matrices.

Integration of UPLC-MS/MS-based metabolomics and desorption electrospray ionization-mass spectrometry imaging reveals that Shouhui Tongbian Capsule alleviates slow transit constipation by regulating bile acid metabolism

Slow transit constipation (STC) is a common intestinal disorder. Some studies reported that Shouhui Tongbian Capsule (SHTB) can effectively mitigate STC symptoms. A detailed understanding of the changes in the endogenous metabolite profile of rats is crucial for a more accurate comprehension of the molecular pathological characteristics of SHTB in treating STC. In the present study, a method integrating metabolomics based on Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and Desorption electrospray ionization (DESI)-mass spectrometry imaging (MSI) was proposed to investigate serum, feces and colon tissue metabolic alterations of STC rats induced by diphenoxylate and the effect of SHTB treatment on metabolism. Then, Enzyme-linked immunosorbent assay (ELISA) and Western blot (WB) analysis for verifying the potential mechanism of SHTB in treating STC. As a result, we first indicated that SHTB significantly improved intestinal peristalsis and low fecal water content in STC rats. Furthermore, after treatment with SHTB, the thickness of muscle layers was increased, demonstrated SHTB's effectiveness in reducing intestinal injury in STC rats. Besides, bile acid (BA) metabolomics based on UPLC-MS/MS revealed significant increase in serum levels of Cholic acid (CA), Deoxycholic acid (DCA), Chenodeoxycholic acid (CDCA), Ursodeoxycholic acid (UDCA), and Glycolithocholic acid (GLCA), whereas the contents of CA and DCA in feces were significantly decreased in STC rats. Nonetheless, they returned to the control levels after the SHTB administration. ELISA results showed that SHTB significantly hindered the excessive reabsorption of BAs by inhibiting apical sodium-dependent bile acid transporter (ASBT), organic solute transporter alpha (OSTα) and organic solute transporter beta (OSTβ) in the ileum tissue of STC rats. Furthermore, the DESI-MSI analysis revealed that SHTB remarkably enhanced DCA in the colon tissue of STC rats. The WB results indicated that SHTB reinstated Takeda G-protein-coupled receptor 5 (TGR5) expression, a receptor for BAs and a key regulator of colonic motility. Consequently, DCA exerted its effects on TGR5, leading to the promotion of colonic motility. This study provided more comprehensive and detailed information about the BA metabolomics in the serum, feces and colon of STC rats. These findings highlighted the promising potential of metabolomics based on UPLC-MS/MS and DESI-MSI method for application in the study of STC diseases.

Advances in intelligent mass spectrometry data processing technology for in vivo analysis of natural medicines

Natural medicines (NMs) are crucial for treating human diseases. Efficiently characterizing their bioactive components in vivo has been a key focus and challenge in NM research. High-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) systems offer high sensitivity, resolution, and precision for conducting in vivo analysis of NMs. However, due to the complexity of NMs, conventional data acquisition, mining, and processing techniques often fail to meet the practical needs of in vivo NM analysis. Over the past two decades, intelligent spectral data-processing techniques based on various principles and algorithms have been developed and applied for in vivo NM analysis. Consequently, improvements have been achieved in the overall analytical performance by relying on these techniques without the need to change the instrument hardware. These improvements include enhanced instrument analysis sensitivity, expanded compound analysis coverage, intelligent identification, and characterization of nontargeted in vivo compounds, providing powerful technical means for studying the in vivo metabolism of NMs and screening for pharmacologically active components. This review summarizes the research progress on in vivo analysis strategies for NMs using intelligent MS data processing techniques reported over the past two decades. It discusses differences in compound structures, variations among biological samples, and the application of artificial intelligence (AI) neural network algorithms. Additionally, the review offers insights into the potential of in vivo tracking of NMs, including the screening of bioactive components and the identification of pharmacokinetic markers. The aim is to provide a reference for the integration and development of new technologies and strategies for future in vivo analysis of NMs.

UPLC-Triple-TOF-MS-based serum metabonomic revealed the alleviating effect of QingYan Formula on perimenopausal syndrome rats

The study aimed to investigate the relieving effect of QingYan Formula (QYF) in treating perimenopausal syndrome. A combination of metabonomic analysis and in vitro pharmacodynamic experiments was employed to achieve this objective.Over a period of 12 weeks, ovariectomized (OVX) rats were orally administered QYF's 70 % ethanol extract or estradiol valerate (EV). The results demonstrate that QYF restored the estrous cycle of ovariectomized rats and exhibited significant estrogenic activity, as indicated by reversal of uterine and vagina atrophy, improvement of serum estradiol level and decrease of serum luteinizing hormone(LH) level. Additionally, QYF administration effectively reduced high bone turnover and repaired trabecular microstructure damage. Metabonomic analysis of the OVX rats treated with QYF revealed the identification of 55 different metabolites in the serum, out of which 35 may be potential biomarkers. QYF could regulate the disturbed metabolic pathways including the Biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, bile secretion, and steroid hormone biosynthesis. PI3KCA, SRC, and MAPK3 are potential therapeutic targets for QYF therapeutic effects. These findings support the efficacy of QYF in alleviating perimenopausal syndrome and regulating lipid metabolic disorders in OVX rats.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Bone Proteomics Method Optimization for Forensic Investigations

The application of proteomic analysis to forensic skeletal remains has gained significant interest in improving biological and chronological estimations in medico-legal investigations. To enhance the applicability of these analyses to forensic casework, it is crucial to maximize throughput and proteome recovery while minimizing interoperator variability and laboratory-induced post-translational protein modifications (PTMs). This work compared different workflows for extracting, purifying, and analyzing bone proteins using liquid chromatography with tandem mass spectrometry (LC-MS)/MS including an in-StageTip protocol previously optimized for forensic applications and two protocols using novel suspension-trap technology (S-Trap) and different lysis solutions. This study also compared data-dependent acquisition (DDA) with data-independent acquisition (DIA). By testing all of the workflows on 30 human cortical tibiae samples, S-Trap workflows resulted in increased proteome recovery with both lysis solutions tested and in decreased levels of induced deamidations, and the DIA mode resulted in greater sensitivity and window of identification for the identification of lower-abundance proteins, especially when open-source software was utilized for data processing in both modes. The newly developed S-Trap protocol is, therefore, suitable for forensic bone proteomic workflows and, particularly when paired with DIA mode, can offer improved proteomic outcomes and increased reproducibility, showcasing its potential in forensic proteomics and contributing to achieving standardization in bone proteomic analyses for forensic applications.

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.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Targeted arginine metabolomics combined with metagenomics revealed the potential mechanism of Pueraria lobata extract in treating myocardial infarction

The extraction methods for traditional Chinese medicine (TCM) may have varying therapeutic effects on diseases. Currently, Pueraria lobata (PL) is mostly extracted with ethanol, but decoction, as a TCM extraction method, is not widely adopted. In this study, we present a strategy that integrates targeted metabolomics, 16 s rDNA sequencing technology and metagenomics for exploring the potential mechanism of the water extract of PL (PLE) in treating myocardial infarction (MI). Using advanced analytical techniques like ultra-high performance liquid chromatography with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), we comprehensively characterized PLE's chemical composition. Further, we tested its efficacy in a rat model of MI induced by ligation of the left anterior descending branch of the coronary artery (LAD). We assessed cardiac enzyme levels and conducted echocardiograms. UPLC-MS/MS was used to compare amino acid differences in serum. Furthermore, we investigated fecal samples using 16S rDNA sequencing and metagenomic sequencing to study intestinal flora diversity and function. This study demonstrated PLE's effectiveness in reducing cardiac injury in LAD-ligated rats. Amino acid metabolomics revealed significant improvements in serum levels of arginine, citrulline, proline, ornithine, creatine, creatinine, and sarcosine in MI rats, which are key compounds in the arginine metabolism pathway. Enzyme-linked immunosorbent assay (ELISA) results showed that PLE significantly improved arginase (Arg), nitric oxide synthase (NOS), and creatine kinase (CK) contents in the liver tissue of MI rats. 16 s rDNA and metagenome sequencing revealed that PLE significantly improved intestinal flora imbalance in MI rats, particularly in taxa such as Tuzzerella, Desulfovibrio, Fournierella, Oscillibater, Harryflintia, and Holdemania. PLE also improved the arginine metabolic pathway in the intestinal microorganisms of MI rats. The findings indicate that PLE effectively modulates MI-induced arginine levels and restores intestinal flora balance. This study, the first to explore the mechanism of action of PLE in MI treatment considering amino acid metabolism and intestinal flora, expands our understanding of the potential of PL in MI treatment. It offers fresh insights into the mechanisms of PL, guiding further research and development of PL-based medicines.

LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case

We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

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.

Using Data-Dependent and -Independent Hybrid Acquisitions for Fast Liquid Chromatography-Based Untargeted Lipidomics

Untargeted lipidomics using liquid chromatography (LC) coupled with tandem mass spectrometry (MS) is essential for large cohort studies. Using a fast LC gradient of less than 10 min for the rapid screening of lipids decreases the annotation rate, because of the lower coverage of the MS/MS spectra caused by the narrow peak width. A systematic procedure is proposed in this study to achieve a high annotation rate in fast LC-based untargeted lipidomics by integrating data-dependent acquisition (DDA) and sequential window acquisition of all-theoretical mass spectrometry data-independent acquisition (SWATH-DIA) techniques using the updated MS-DIAL program. This strategy uses variable SWATH-DIA methods for quality control (QC) samples, which are a mixture of biological samples that were analyzed multiple times to correct the MS signal drift. In contrast, biological samples are analyzed using DDA to facilitate the structural elucidation of lipids using the pure spectrum to the maximum extent. The workflow is demonstrated using an 8.6 min LC gradient, where the QC samples are analyzed using five different SWATH-DIA methods. The use of both DDA and SWATH-DIA achieves a 1.7-fold annotation coverage from publicly available benchmark data obtained using a fast LC-DDA-MS technique and offers 95.3% lipid coverage, as compared to the benchmark data set from a 25 min LC gradient. This study demonstrates that harmonized improvements in analytical conditions and informatics tools provide a comprehensive lipidome in fast LC-based untargeted lipidomics, not only for large-scale studies but also for small-scale experiments, contributing to both clinical applications and basic biology.

Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.

HGF secreted by hUC-MSCs mitigates neuronal apoptosis to repair the injured spinal cord via phosphorylation of Akt/FoxO3a pathway

Spinal cord injury (SCI) can cause severe and permanent neurological damage, and neuronal apoptosis could inhibit functional recovery of damaged spinal cord greatly. Human umbilical cord mesenchymal stem cells (hUC-MSCs) have great potential to repair SCI because of a series of advantages, including inhibition of neuronal apoptosis and multiple differentiation. The former may play an important role. However, the detailed regulatory mechanism associated with the inhibition of neuronal apoptosis after hUC-MSCs administration has not been elucidated. In this study, proteomics analysis of precious human cerebrospinal fluid (CSF) samples collected from SCI subjects receiving hUC-MSCs delivery indicated that hepatocyte growth factor (HGF) is largely involved in SCI repair. Furthermore, overexpression of HGF derived from hUC-MSCs could decrease reactive oxygen species to prevent neuron apoptosis to the maximum, and thus lead to significant recovery of spinal cord dysfunction. Moreover, HGF could promote phosphorylation of Akt/FoxO3a pathway to decrease reactive oxygen species to reduce neuron apoptosis. For the first time, our research revealed that HGF secreted by hUC-MSCs inhibits neuron apoptosis by phosphorylation of Akt/FoxO3a to repair SCI. This study provides important clues associated with drug selection for the effective treatment of SCI in humans.

Data independent acquisition for gas chromatographic MS/MS analysis of volatile compounds

This study presents a novel tandem mass spectrometry (MS/MS) approach utilizing a data independent acquisition (DIA) concept specifically designed with gas chromatography-electron ionization-triple quadrupole mass spectrometry (GC-EI-QqQMS). This allows compound identification based on comparison between all the experimental MS/MS product ion spectra and the simulated library data of >1,000 MS/MS transitions of 71 compounds. The simulation data were generated by using the Competitive Fragmentation Modeling (CFM-ID) 3.0 program. The approach for calculation of the DIA MS/MS library match scores was then established and applied for identification of a range of terpenoids and oxygenated compounds in perfume. The identity of each peak was confirmed using 4-241 MS/MS transitions. The established data collection and analysis methods are expected to be useful for increased confidence in volatile compound analysis.

Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques

High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

Dilute and shoot approach for toxicology testing

Toxicology testing is performed in clinical settings, forensic settings, and for controlling doping. Drug screening is a toxicology test to determine if drugs are present in biological samples. The most common specimen type for drug testing is urine, as drugs and/or their metabolites are often more concentrated in the urine, extending the detection window of drugs. The dilute-and-shoot method is a simple procedure used in toxicology testing, where a sample is diluted before being directly injected into the liquid chromatography-mass spectrometry (LC-MS) system. This method is easy, quick, and cost-saving, and can be used for protein-poor liquid specimens such as urine. Thus, it is reasonable and attractive for busy toxicology laboratories to combine the dilute-and-shoot method with high-resolution hyphenated-MS for urine drug screening. This method has several disadvantages, including a suboptimal detection capability for certain analytes, as well as interference from co-eluting matrix components called matrix effects, in which co-eluting matrix molecules alter the ionization efficiency of the analyte molecules at the ionization source in LC-MS, altering (mostly reducing) the analyte detection capability. The matrix effect testing is essential for the validation of LC-MS-based assays. A reasonable approach to addressing these undesirable effects would be to minimize these components. The most straightforward approach is to reduce the amounts of matrix components by using a higher dilution of the specimen and a lower volume for specimen injection. Optimization of the chromatographic separation is another reasonable approach for reducing co-eluting matrix components with the analyte.

Q-RAI data-independent acquisition for lipidomic quantitative profiling

Untargeted lipidomics has been increasingly adopted for hypothesis generation in a biological context or discovery of disease biomarkers. Most of the current liquid chromatography mass spectrometry (LC-MS) based untargeted methodologies utilize a data dependent acquisition (DDA) approach in pooled samples for identification and MS-only acquisition for semi-quantification in individual samples. In this study, we present for the first time an untargeted lipidomic workflow that makes use of the newly implemented Quadrupole Resolved All-Ions (Q-RAI) acquisition function on the Agilent 6546 quadrupole time-of-flight (Q-TOF) mass spectrometer to acquire MS2 spectra in data independent acquisition (DIA) mode. This is followed by data processing and analysis on MetaboKit, a software enabling DDA-based spectral library construction and extraction of MS1 and MS2 peak areas, for reproducible identification and quantification of lipids in DIA analysis. This workflow was tested on lipid extracts from human plasma and showed quantification at MS1 and MS2 levels comparable to multiple reaction monitoring (MRM) targeted analysis of the same samples. Analysis of serum from Ceramide Synthase 2 (CerS2) null mice using the Q-RAI DIA workflow identified 88 lipid species significantly different between CerS2 null and wild type mice, including well-characterized changes previously associated with this phenotype. Our results show the Q-RAI DIA as a reliable option to perform simultaneous identification and reproducible relative quantification of lipids in exploratory biological studies.

Bioprospecting-based untargeted metabolomics identifies alkaloids as potential anti-inflammatory bioactive markers of Ocotea species (Lauraceae)

BACKGROUND

Species within the Ocotea genus (Lauraceae), have demonstrated an interesting profile of bioactivities. Renowned for their diverse morphology and intricate specialized metabolite composition, Ocotea species have re-emerged as compelling candidates for bioprospecting in drug discovery research. However, it is a genus insufficiently studied, particularly regarding anti-inflammatory activity.

PURPOSE

To investigate the anti-inflammatory activity of Ocotea spp. extracts and determine the major markers in this genus.

METHODS

Extracts of 60 different Ocotea spp. were analysed by an ex vivo anti-inflammatory assay in human whole blood. The experiment estimates the prostaglandin E2 levels, which is one of the main mediators of the inflammatory cascade, responsible for the classical symptoms of fever, pain, and other common effects of the inflammatory process. Untargeted metabolomics analysis through liquid chromatography coupled with high-resolution mass spectrometry was performed, along with statistical analysis, to investigate which Ocotea metabolites are correlated with their anti-inflammatory activity.

RESULTS

The anti-inflammatory screening indicated that 49 out of 60 Ocotea spp. extracts exhibited significant inhibition of PGE2 release compared to the vehicle (p < 0.05). Furthermore, 10 of these extracts showed statistical similarity to the reference drugs. The bioactive markers were accurately identified using multivariate statistics combined with a fold change (> 1.5) and adjusted false discovery rate analysis as unknown compounds and alkaloids, with a majority of aporphine and benzylisoquinolines. These alkaloids were annotated with an increased level of confidence since MSE spectra were compared with comprehensive databases.

CONCLUSION

This study represents the first bioprospecting report revealing the anti-inflammatory potential of several Ocotea spp. The determination of their anti-inflammatory markers could contribute to drug discovery and the chemical knowledge of the Ocotea genus.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Integrated strategy facilitates rapid in-depth chemome characterization of traditional Chinese medicine prescriptions: Shengbai oral liquid as a case

Chemome characterization is the prerequisite for either therapeutic mechanism clarification or quality control of traditional Chinese medicine prescriptions (TCMPs). Liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) currently serves as the most popular analytical tool; however, chemome characterization is still challenged by MS/MS spectral acquisition and post-acquisition data processing. Here, an integrated strategy was proposed for in-depth chemome clarification of Shengbai oral liquid (SBOL). Gas phase ion fractionation with staggered mass ranges was demonstrated to be the superior acquisition method regarding MS2 spectrum coverage in this study, and narrower mass range further advanced coverage. To facilitate information extraction, all ingredient materials were measured in parallel to form an in-house library, where each MS1 -MS2 item generated a square mass-to-charge ratio (m/z) frame to capture the tagged identity and each chemical family produced a pentagon frame for mass defect features to accomplish chemical analogs-targeted quasi-molecular ion extraction. Square m/z frame imprinting captured 355 identities, while mass defect frames extracted 275 compounds. Attributing to comprehensive MS2 spectrum acquisition and efficient data processing, 355 components were captured and tentatively identified, resulting in a clarified chemical composition for SBOL. Therefore, the proposed strategy should be meaningful for the chemome characterization of TCMPs.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

A comparative metabolomics study on dried and processed Rehmannia Radix

The efficacy of Rehmannia Radix changes after processing. However, the precise effect of processing on the properties of Rehmannia Radix is an intricate topic, as this effect cannot be explained by traditional methods. The purpose of this study was to investigate how processing methods influence the properties of Rehmannia Radix, as well as the changes in body function after administering dried Rehmannia Radix (RR) and processed Rehmannia Radix (PR) using a metabolomics approach. In addition, principal component analysis and orthogonal partial least-squares discriminant analysis models were generated using SIMCA-P 14.0 to evaluate the properties of RR and PR. Potential biomarkers were identified, and associated metabolic networks were established to clarify differences in the properties and efficacies of RR and PR. The results showed that RR and PR have cold and hot properties, respectively. RR can exert a hypolipidaemic effect by regulating nicotinate and nicotinamide metabolism. PR exerts a tonic effect and regulates the body's reproductive function through the regulation of alanine, aspartate and glutamate metabolism, arachidonic acid, pentose and glucuronate metabolism, respectively. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics is a promising approach to determine the cold/hot properties of traditional Chinese medicine formulations.

MetaboMSDIA: A tool for implementing data-independent acquisition in metabolomic-based mass spectrometry analysis

Data-dependent acquisition (DDA) is the most widely used mode in untargeted metabolomic analysis despite its limited tandem mass spectrometry (MS2) detection coverage. We present MetaboMSDIA for complete processing of data-independent acquisition (DIA) files by the extraction of multiplexed MS2 spectra and further identification of metabolites in open libraries. In the analysis of polar extracts from lemon and olive fruits, DIA allows one to obtain multiplexed MS2 spectra for 100% of precursor ions compared to 64% of precursor ions from average MS2 acquisition in DDA. MetaboMSDIA is compatible with MS2 repositories and homemade libraries prepared by analysis of standards. An additional option is based on filtering molecular entities by searching for selective fragmentation patterns according to selective neutral losses or product ions to target the annotation of families of metabolites. Combining both options, the applicability of MetaboMSDIA was tested by annotating 50 and 35 metabolites in polar extracts from lemon and olive fruit, respectively. MetaboMSDIA is particularly proposed to increase the acquisition coverage in untargeted metabolomics and to improve spectral quality, which are two critical pillars for the tentative annotation of metabolites. The R script used in MetaboMSDIA workflow is available at github repository (https://github.com/MonicaCalSan/MetaboMSDIA).

An integrated deep learning framework for the interpretation of untargeted metabolomics data

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Analysis of Phospholipids in Digestion Using Hybrid IDA and SWATH Acquisition: An Example for Krill Oil

The composition and digestion of phospholipid-rich foods have important effects on the health of the body. Herein, a model-assisted liquid chromatography coupling mass spectrometry (LC-MS) method was established to analyze the phosphatidylcholine (PC) and lyso-phosphatidylcholine (LPC) species in krill oil before and after digestion. According to the confirmed PC and LPC species in the IDA (information dependent acquisition) results, three categories of mathematical models were set up, involving the retention time (RT), carbon number and unsaturation degree of the fatty acyl chain. All of the regression coefficient values (R²) were greater than 0.90, showing satisfactory fitting results. On this basis, using the computationally created precursor ion mass of PC and LPC species, 12 extra PC species and 4 LPC species were found in the SWATH (sequential windowed acquisition of all theoretical fragment ions) results. The PC and LPC compositions in the final digestive products had obvious differences among the different krill oils with different phospholipid content. Furthermore, more than half of the LPC species in the final digestive products were newly generated, indicating that LPC was one of basic constituents in the digestive products of krill oil. In conclusion, model-assisted hybrid IDA and SWATH acquisition has excellent detection performance, contributing to deep studies of the formations and functions of phospholipids.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

Ensuring Fact-Based Metabolite Identification in Liquid Chromatography-Mass Spectrometry-Based Metabolomics

Metabolite identification represents a major bottleneck in contemporary metabolomics research and a step where critical errors may occur and pass unnoticed. This is especially the case for studies employing liquid chromatography-mass spectrometry technology, where there is increased concern on the validity of the proposed identities. In the present perspective article, we describe the issue and categorize the errors into two types: identities that show poor biological plausibility and identities that do not comply with chromatographic data and thus to physicochemical properties (usually hydrophobicity/hydrophilicity) of the proposed molecule. We discuss the problem, present characteristic examples, and propose measures to improve the situation.

Non-aqueous capillary electrophoresis-time of flight mass spectrometry method to determine emerging mycotoxins

Enniatins (ENN) and beauvericin (BEA) are emerging mycotoxins that have been traditionally determined by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). However, to the best of our knowledge, no analytical methods based on capillary electrophoresis (CE)-MS/MS have been reported so far. Due to their non-polar nature, in this work, a non-aqueous CE (NACE) method coupled to quadrupole time-of-flight-MS is proposed for the first time to identify and quantify these mycotoxins. Determination was achieved in 4 min under optimum conditions: 40 mM ammonium acetate in 80:20 (v/v) acetonitrile-methanol (buffer), 30 kV (voltage), 80 cm (capillary length), 20 °C (capillary temperature) and 50 mbar × 30 s (injection). Higher selectivity can be achieved when compared with LC due to the formation of exclusive CE adducts such as [M + CH₃CH₂NH₃]⁺. "All Ions" acquisition mode was selected as it allows the quantification of the usual ENNs, as well as the identity confirmation of less common ENNs. The method was validated for wheat samples, obtaining limits of quantification from 4.0 to 8.3 μg/kg depending on the emerging mycotoxin, recovery values higher than 87.4%, and intra- and inter-day precision values (RSDs) lower than 15.1% in all cases. Finally, 29 wheat samples were analyzed, finding 26 samples with concentrations of enniatin B higher than the limit of quantification (7.5-1480 μg/kg), 20 for enniatin B1 (5.2-550 μg/kg), 7 for enniatin A (10-55 μg/kg), 4 for enniatin A1 (12.6-77 μg/kg) and 5 for BEA (9.2-16.4 μg/kg). Moreover, two other ENNs were tentatively identified.

Comparative proteomics analyses of whey proteins from breastmilk collected from two ethnic groups in northeast China

The current study aims to investigate differences in whey protein of breastmilk of volunteered mother collected from two ethnic groups (Korean and Han) in China using data-independent acquisition (DIA) based proteomics technique. The total detected 624 proteins were principally allocated to cellular process of biological process (BP), cell and cell part of cell component (CC) and binding of molecular function (MF) according to Gene Ontology (GO) annotation; and carbohydrate metabolism of Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Among the 54 differently expressed proteins, 8 were related with immunity. Enrichment data showed that intracellular of GO functions and viral myocarditis of KEGG pathways were most significantly enriched (p < 0.05). Protein-protein interaction (PPI) network suggested that 40S ribosomal protein S27a and 60S ribosomal protein L10a which interacted most with other proteins ranked the top two hub proteins by MCC (Maximal Clique Centrality) method. This study may have guiding role for development of infant formula powder for specific infants of Han or Korean groups according to responding breastmilk composition.

A novel strategy integrating gas phase fractionation with staggered mass range and LC-MS/MS molecular network for comprehensive metabolites profiling of Gui Ling Ji in rats

Metabolite detection from complex biological samples faces challenges due to interference from endogenous substrates and the inherent limitation of multiple subsequent tandem scanning rates of instruments. Here, a new integrated approach based on gas-phase fractionation with a staggered mass range (sGPF) and a liquid chromatography-tandem mass spectrometry (LC-MS/MS) molecular network was developed to accelerate the data processing of the targeted and untargeted constituents absorbed in rats after oral administration of the traditional Chinese medicine (TCM) prescription Gui Ling Ji (GLJ). Compared with three conventional acquisition methods, sGPF at 3, 5, and 7 mass fractions could enhance MS/MS coverage with an increased MS/MS triggering rate of 29.4-206.2% over data-dependent acquisition (DDA), fast DDA and gas-phase fractionation. A mass range fraction setting of five optimized the performance. Based on the similar diagnostic fragment ions and characteristic neutral loss behaviors in the DDA-MS/MS spectrum, an initial molecular network of GLJ was created with the help of the global natural products social molecular networking (GNPS) platform. Furthermore, to remove the endogenous interference nodes, Cytoscape software was adopted to produce a clean and concise molecular network of prototype compounds and their corresponding metabolites. Using this strategy, a total of 210 compounds, including 59 prototype constituents and 151 metabolites, was unambiguously or tentatively identified in GLJ. This first systematic metabolic study of GLJ in vivo elucidated the potential pharmacodynamic basis of GLJ in clinical treatment. More importantly, this work can serve as a practical example and establish a guide for rapidly identifying TCM metabolites in biological matrices.

Enhanced Identification of Ginsenosides Simultaneously from Seven Panax Herbal Extracts by Data-Dependent Acquisition Including a Preferred Precursor Ions List Derived from an In-House Programmed Virtual Library

Data-dependent acquisition (DDA) is widely utilized for metabolite identification in natural product research and food science, which, however, can suffer from low coverage. A potential solution to improve DDA coverage is to include the precursor ions list (PIL). Here, we aimed to construct a PIL-containing DDA strategy based on an in-house library of ginsenosides (VLG) and identify ginsenosides simultaneously from seven Panax herbal extracts. VLG, combined with mass defect filtering, could efficiently screen the ginsenoside precursors and elaborate the separate PIL involved in DDA for each ginseng extract. Consequently, we could characterize 500 ginsenosides, including 176 ones with unknown masses. Using the Panax ginseng extract, the superiority of this strategy was embodied in targeting more known ginsenoside masses and newly acquiring the MS² spectra of 13 components. Conclusively, knowledge-based large-scale molecular prediction and PIL-DDA can represent a powerful targeted/untargeted strategy beneficial to novel natural compound discovery.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

Integrating Enhanced Profiling and Chemometrics to Unveil the Potential Markers for Differentiating among the Leaves of Panax ginseng, P. quinquefolius, and P. notoginseng by Ultra-High Performance Liquid Chromatography/Ion Mobility-Quadrupole Time-of-Flight Mass Spectrometry

The leaves of Panax species (e.g., Panax ginseng-PGL, P. quinquefolius-PQL, and P. notoginseng-PNL) can serve as a source for healthcare products. Comprehensive characterization and unveiling of the metabolomic difference among PGL, PQL, and PNL are critical to ensure their correct use. For this purpose, enhanced profiling and chemometrics were integrated to probe into the ginsenoside markers for PGL/PQL/PNL by ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS). A hybrid scan approach (HDMSE-HDDDA) was established achieving the dimension-enhanced metabolic profiling, with 342 saponins identified or tentatively characterized from PGL/PQL/PNL. Multivariate statistical analysis (33 batches of leaf samples) could unveil 42 marker saponins, and the characteristic ginsenosides diagnostic for differentiating among PGL/PQL/PNL were primarily established. Compared with the single DDA or DIA, the HDMSE-HDDDA hybrid scan approach could balance between the metabolome coverage and spectral reliability, leading to high-definition MS spectra and the additional collision-cross section (CCS) useful to differentiate isomers.

Metabonomic analysis of abnormal sphingolipid metabolism in rheumatoid arthritis synovial fibroblasts in hypoxia microenvironment and intervention of geniposide

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by a joint hypoxia microenvironment. Our previous untargeted metabolomics study found that sphingolipid (SPL) metabolism was abnormal in the joint synovial fluid samples from adjuvant arthritis (AA) rats. Geniposide (GE), an iridoid glycoside component of the dried fruit of Gardenia jasminoides Ellis, is commonly used for RA treatment in many Asian countries. At present, the mechanism of GE in the treatment of RA, especially in the joint hypoxia microenvironment, is not entirely clear from the perspective of SPL metabolism. The purpose of this research was to explore the potential mechanism of abnormal SPL metabolism in RA joint hypoxia microenvironment and the intervention effect of GE, through the untargeted metabolic analysis based on the ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Arthritis index, foot swelling and histopathology were used to assess whether the AA rat model was successfully established. The SPLs extracts collected from AA rats’ synovial tissue, serum and rheumatoid arthritis synovial fibroblasts (RASFs, MH7A cells, hypoxia/normoxia culture) were analyzed by metabolomics and lipdomics approach based on UPLC-Q-TOF/MS, to identify potential biomarkers associated with disorders of GE regulated RA sphingolipid metabolism. As a result, 11 sphingolipid metabolites related to RA were screened and identified. Except for galactosylceramide (d18:1/20:0), GE could recover the change levels of the above 10 sphingolipid biomarkers in varying degrees. Western blotting results showed that the changes in ceramide (Cer) level regulated by GE were related to the down-regulation of acid-sphingomyelinase (A-SMase) expression in synovial tissue of AA rats. To sum up, this research examined the mechanism of GE in the treatment of RA from the perspective of SPL metabolism and provided a new strategy for the screening of biomarkers for clinical diagnosis of RA.

Combining data acquisition modes in liquid-chromatography-tandem mass spectrometry for comprehensive determination of acylcarnitines in human serum

Acylcarnitines (ACs) are metabolites involved in fatty acid β-oxidation and organic acid metabolism. Metabolic disorders associated to these two processes can be evaluated by determining the complete profile of ACs. In this research, we present an overall strategy for identification, confirmation, and quantitative determination of acylcarnitines in human serum. By this strategy we identified the presence of 47 ACs from C2 to C24 with detection of the unsaturation degree by application of a data-independent acquisition (DIA) liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Complementary, quantitative determination of ACs is based on a high-throughput and fully automated method consisting of solid-phase extraction on-line coupled to LC-MS/MS in data-dependent acquisition (DDA) to improve analytical features avoiding the errors associated to sample processing. Quantitation limits were at pg mL^-1 level, the intra-day and between-day variability were below 15-20%, respectively; and the accuracy, expressed as bias, was always within ± 25%. The proposed method was tested with 40 human volunteers to determine the relative concentration of ACs in serum and identify predominant forms. Significant differences were detected by comparing the ACs profile of obese versus non-obese individuals.

Identifying the cardioprotective mechanism of Danyu Tongmai Granules against myocardial infarction by targeted metabolomics combined with network pharmacology

BACKGROUND

Danyu Tongmai Granules (DY), the commercial Chinese medicine, was well-accepted cardiovascular protective actions in clinic. However, the mechanisms underlying the beneficial effects of DY on cardiovascular disease still need largely to be clarified.

PURPOSE

Therefore, this study was designed to explore potential mechanisms of DY in myocardial infarction (MI) by integrated strategy of metabolomics and network pharmacology.

METHODS

Cardiomyocytes were subjected to H2O2 induced myocardial injury and rats were induced MI via isoproterenol (ISO) injection. The entire metabolic alterations in serum and heart tissues of experimental rats were profiled by UPLC-MS/MS. Based on the identified differential metabolites, the pathway analysis results were obtained and further validated using the network pharmacology approach.

RESULTS

We found that DY exerted significant cardioprotective effects in vitro and in vivo, and ameliorated inflammatory cell infiltration and cardiomyocyte apoptosis induced by ISO. The metabolomics data suggested that DY mainly affected the amino acid metabolism (i.e., valine, leucine and isoleucine biosynthesis, arginine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, glycine, serine, as well as the alanine metabolism, aspartate and glutamate metabolism, etc.). Simultaneously, DY participated in the regulation of the biosynthesis of bile acids and biosynthesis of unsaturated fatty acids. Notably, DY significantly reduced the biosynthesis of valine, leucine and isoleucine to regulating the metabolism of branched chain amino acids (BCAAs) in infarcted myocardium, thus blocking the inflammation via inhibiting the expression of NLRP3 inflammasome in ISO-induced rats. The anti-inflammatory system of DY was further validated with the results of network pharmacology.

CONCLUSION

Our study, for the first time, confirmed that DY inhibited inflammation and further exerted significant anti-myocardial infarction effect. Additionally, our work further demonstrated that the myocardial protective effect of DY was contributed to the inhibition of the NLRP3 inflammasome activation by regulating BCAAs in infarcted myocardium using the comprehensive metabolomics, molecular biology and network analysis. Overall, our study gained new insights into the role of the relationship between the metabolic regulation of BCAAs and the NLRP3 inflammasome against MI.