Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics

The impact of sampling time point on the lipidome composition

Lipidomic profiling has been reported as an effective approach for characterizing and differentiating brain tumors. However, since lipids can undergo non-specific enzymatic and nonenzymatic reactions due to tissue disruption, it is critical to consider the preanalytical phase of the diagnostic process (e.g., optimizing the sampling time and sampling conditions). Thus, this study assesses the ways in which the time point of sampling impacts the lipidome composition of brain tumors. Two histologically distinct brain tumors-namely, meningiomas and gliomas-were sampled using solid-phase microextraction (SPME) fibers at two time points: on-site directly after removal, and after 12 months of storage at -30 °C. The samples were analyzed via HILIC chromatography coupled with HRMS, which enabled the detection of a wide range of features, including phospholipids and sphingolipids, as well as changes in the profiles of these compounds. The samples obtained from the stored tissues tended to have elevated levels of analytes with lower m/z values. In addition, the samples obtained from the fresh and stored tissues were easily distinguished based on their lipidome compositions, regardless of the histological tumor type. Notably, while storage did not affect the possibility of differentiating meningiomas and gliomas, the biological interpretation of the obtained results were prone to bias.

Analytical challenges and opportunities in the study of endocrine disrupting chemicals within an exposomics framework

Exposomics aims to measure human exposures throughout the lifespan and the changes they produce in the human body. Exposome-scale studies have significant potential to understand the interplay of environmental factors with complex multifactorial diseases widespread in our society and whose origin remain unclear. In this framework, the study of the chemical exposome aims to cover all chemical exposures and their effects in human health but, today, this goal still seems unfeasible or at least very challenging, which makes the exposome for now only a concept. Furthermore, the study of the chemical exposome faces several methodological challenges such as moving from specific targeted methodologies towards high-throughput multitargeted and non-targeted approaches, guaranteeing the availability and quality of biological samples to obtain quality analytical data, standardization of applied analytical methodologies, as well as the statistical assignment of increasingly complex datasets, or the identification of (un)known analytes. This review discusses the various steps involved in applying the exposome concept from an analytical perspective. It provides an overview of the wide variety of existing analytical methods and instruments, highlighting their complementarity to develop combined analytical strategies to advance towards the chemical exposome characterization. In addition, this review focuses on endocrine disrupting chemicals (EDCs) to show how studying even a minor part of the chemical exposome represents a great challenge. Analytical strategies applied in an exposomics context have shown great potential to elucidate the role of EDCs in health outcomes. However, translating innovative methods into etiological research and chemical risk assessment will require a multidisciplinary effort. Unlike other review articles focused on exposomics, this review offers a holistic view from the perspective of analytical chemistry and discuss the entire analytical workflow to finally obtain valuable results.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Oxylipins in Breast Implant-Associated Systemic Symptoms

BACKGROUND

A subset of females with breast implants have reported a myriad of nonspecific systemic symptoms collectively termed systemic symptoms associated with breast implants (SSBI). SSBI symptoms are similar to manifestations associated with autoimmune and connective tissue disorders. Breast tissue is rich in adipose cells, comprised of lipids. Insertion of an implant creates an oxidative environment leading to lipid oxidation. Oxylipins can influence immune responses and inflammatory processes.

OBJECTIVES

In this study we explored the abundance of a spectrum of oxylipins in the periprosthetic tissue surrounding the breast implant. Because oxylipins are immunogenic, we sought to determine if they were associated with the SSBI patients. We have also attempted to determine if the common manifestations exhibited by such patients have any association with oxylipin abundance.

METHODS

The study included 120 patients divided into 3 cohorts. We analyzed 46 patients with breast implants exhibiting manifestations associated with SSBI; 29 patients with breast implants not exhibiting manifestations associated with SSBI (control cohort I, non-SSBI); and 45 patients without implants (control cohort II, no-implant tissue). Lipid extraction and oxylipin quantification were performed with liquid chromatography mass spectrometry (LC-MS/MS). LC-MS/MS targeted analysis of the breast adipose tissue was performed.

RESULTS

Of the 15 oxylipins analyzed, 5 exhibited increased abundance in the SSBI cohort when compared to the non-SSBI and no-implant cohorts.

CONCLUSIONS

The study documents the association of the oxylipins with each manifestation reported by the patient. This study provides an objective assessment of the subjective questionnaire, highlighting which symptoms may be more relevant than the others.

LEVEL OF EVIDENCE: 4

Sphingolipid metabolites as potential circulating biomarkers for sarcopenia in men

BACKGROUND

Sarcopenia is an age-related progressive loss of muscle mass and function. Sarcopenia is a multifactorial disorder, including metabolic disturbance; therefore, metabolites may be used as circulating biomarkers for sarcopenia. We aimed to investigate potential biomarkers of sarcopenia using metabolomics.

METHODS

After non-targeted metabolome profiling of plasma from mice of an aging mouse model of sarcopenia, sphingolipid metabolites and muscle cells from the animal model were evaluated using targeted metabolome profiling. The associations between sphingolipid metabolites identified from mouse and cell studies and sarcopenia status were assessed in men in an age-matched discovery (72 cases and 72 controls) and validation (36 cases and 128 controls) cohort; women with sarcopenia (36 cases and 36 controls) were also included as a discovery cohort.

RESULTS

Both non-targeted and targeted metabolome profiling in the experimental studies showed an association between sphingolipid metabolites, including ceramides (CERs) and sphingomyelins (SMs), and sarcopenia. Plasma SM (16:0), CER (24:1), and SM (24:1) levels in men with sarcopenia were significantly higher in the discovery cohort than in the controls (all P < 0.05). There were no significant differences in plasma sphingolipid levels for women with or without sarcopenia. In men in the discovery cohort, an area under the receiver-operating characteristic curve (AUROC) of SM (16:0) for low muscle strength and low muscle mass was 0.600 (95% confidence interval [CI]: 0.501-0.699) and 0.647 (95% CI: 0.557-0.737). The AUROC (95% CI) of CER (24:1) and SM (24:1) for low muscle mass in men was 0.669 (95% CI: 0.581-0.757) and 0.670 (95% CI: 0.582-0.759), respectively. Using a regression equation combining CER (24:1) and SM (16:0) levels, a sphingolipid (SphL) score was calculated; an AUROC of the SphL score for sarcopenia was 0.712 (95% CI: 0.626-0.798). The addition of the SphL score to HGS significantly improved the AUC from 0.646 (95% CI: 0.575-0.717; HGS only) to 0.751 (95% CI: 0.671-0.831, P = 0.002; HGS + SphL) in the discovery cohort. The predictive ability of the SphL score for sarcopenia was confirmed in the validation cohort (AUROC = 0.695, 95% CI: 0.591-0.799).

CONCLUSIONS

SM (16:0), reflecting low muscle strength, and CER (24:1) and SM (16:0), reflecting low muscle mass, are potential circulating biomarkers for sarcopenia in men. Further research on sphingolipid metabolites is required to confirm these results and provide additional insights into the metabolomic changes relevant to the pathogenesis and diagnosis of sarcopenia.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 μl of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid and a decrease in eicosapentaenoic acid content across all lipid classes.

Exploring lipid signaling in plant physiology: From cellular membranes to environmental adaptation

Lipids have evolved as versatile signaling molecules that regulate a variety of physiological processes in plants. Convincing evidence highlights their critical role as mediators in a wide range of plant processes required for survival, growth, development, and responses to environmental conditions such as water availability, temperature changes, salt, pests, and diseases. Understanding lipid signaling as a critical process has helped us expand our understanding of plant biology by explaining how plants sense and respond to environmental cues. Lipid signaling pathways constitute a complex network of lipids, enzymes, and receptors that coordinate important cellular responses and stressing plant biology's changing and adaptable traits. Plant lipid signaling involves a wide range of lipid classes, including phospholipids, sphingolipids, oxylipins, and sterols, each of which contributes differently to cellular communication and control. These lipids function not only as structural components, but also as bioactive molecules that transfer signals. The mechanisms entail the production of lipid mediators and their detection by particular receptors, which frequently trigger downstream cascades that affect gene expression, cellular functions, and overall plant growth. This review looks into lipid signaling in plant physiology, giving an in-depth look and emphasizing its critical function as a master regulator of vital activities.

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.

Peripheral Lipid Signatures, Metabolic Dysfunction, and Pathophysiology in Schizophrenia Spectrum Disorders

Metabolic dysfunction is commonly observed in schizophrenia spectrum disorders (SSDs). The causes of metabolic comorbidity in SSDs are complex and include intrinsic or biological factors linked to the disorder, which are compounded by antipsychotic (AP) medications. The exact mechanisms underlying SSD pathophysiology and AP-induced metabolic dysfunction are unknown, but dysregulated lipid metabolism may play a role. Lipidomics, which detects lipid metabolites in a biological sample, represents an analytical tool to examine lipid metabolism. This systematic review aims to determine peripheral lipid signatures that are dysregulated among individuals with SSDs (1) with minimal exposure to APs and (2) during AP treatment. To accomplish this goal, we searched MEDLINE, Embase, and PsychINFO databases in February 2024 to identify all full-text articles written in English where the authors conducted lipidomics in SSDs. Lipid signatures reported to significantly differ in SSDs compared to controls or in relation to AP treatment and the direction of dysregulation were extracted as outcomes. We identified 46 studies that met our inclusion criteria. Most of the lipid metabolites that significantly differed in minimally AP-treated patients vs. controls comprised glycerophospholipids, which were mostly downregulated. In the AP-treated group vs. controls, the significantly different metabolites were primarily fatty acyls, which were dysregulated in conflicting directions between studies. In the pre-to-post AP-treated patients, the most impacted metabolites were glycerophospholipids and fatty acyls, which were found to be primarily upregulated and conflicting, respectively. These lipid metabolites may contribute to SSD pathophysiology and metabolic dysfunction through various mechanisms, including the modulation of inflammation, cellular membrane permeability, and metabolic signaling pathways.

Application of Pan-Omics Technologies in Research on Important Economic Traits for Ruminants

The economic significance of ruminants in agriculture underscores the need for advanced research methodologies to enhance their traits. This review aims to elucidate the transformative role of pan-omics technologies in ruminant research, focusing on their application in uncovering the genetic mechanisms underlying complex traits such as growth, reproduction, production performance, and rumen function. Pan-omics analysis not only helps in identifying key genes and their regulatory networks associated with important economic traits but also reveals the impact of environmental factors on trait expression. By integrating genomics, epigenomics, transcriptomics, metabolomics, and microbiomics, pan-omics enables a comprehensive analysis of the interplay between genetics and environmental factors, offering a holistic understanding of trait expression. We explore specific examples of economic traits where these technologies have been pivotal, highlighting key genes and regulatory networks identified through pan-omics approaches. Additionally, we trace the historical evolution of each omics field, detailing their progression from foundational discoveries to high-throughput platforms. This review provides a critical synthesis of recent advancements, offering new insights and practical recommendations for the application of pan-omics in the ruminant industry. The broader implications for modern animal husbandry are discussed, emphasizing the potential for these technologies to drive sustainable improvements in ruminant production systems.

In Vivo and Real-Time Metabolic Profiling of Plant-Microbe Interactions in Leaves, Stems, and Roots of Bacterially Inoculated Chardonnay Plantlets using SpiderMass

There is growing interest in limiting the use of fungicides and implementing innovative, environmentally friendly strategies, such as the use of beneficial bacteria-triggered immunity, to protect grapevines from natural pathogens. Therefore, we need rapid and innovative ways to translate the knowledge of the molecular mechanisms underlying the activation of grapevine defenses against pathogens to induced resistance. Here, we have implemented an in vivo minimally invasive approach to study the interaction between plants and beneficial bacteria based on metabolic signatures. Paraburkholderia phytofirmans strain PsJN and PsJN-grapevine were used as bacterial and plant-bacterium interaction models, respectively. Using an innovative tool, SpiderMass, based on water-assisted laser desorption ionization with an IR microsampling probe, we simultaneously detect metabolic and lipidomic species. A metabolomic spectrum was thus generated, which was used to build a library and identify the most variable and discriminative peaks between the two conditions. We then showed that caftaric acid (m/z 311.04), caftaric acid dimer (m/z 623.09), derived caftaric acid (m/z 653.15), and quercetin-O-glucuronide tended to accumulate in grapevine leaves after root bacterization with PsJN. In addition, together with these phenolic messengers, we identified lipid biomarkers such as palmitic acid, linoleic acid, and α-linoleic acid as important messengers of enhanced defense mechanisms in Chardonnay plantlets. Taken together, SpiderMass is the next-generation methodology for studying plant-microorganism metabolic interactions with the prospect of in vivo real-time analysis in viticulture.

Infestation of Rice Striped Stem Borer (Chilo suppressalis) Larvae Induces Emission of Volatile Organic Compounds in Rice and Repels Female Adult Oviposition

Plants regulate the biosynthesis and emission of metabolic compounds to manage herbivorous stresses. In this study, as a destructive pest, the pre-infestation of rice striped stem borer (SSB, Chilo suppressalis) larvae on rice (Oryza sativa) reduced the subsequent SSB female adult oviposition preference. Widely targeted volatilomics and transcriptome sequencing were used to identify released volatile metabolic profiles and differentially expressed genes in SSB-infested and uninfested rice plants. SSB infestation significantly altered the accumulation of 71 volatile organic compounds (VOCs), including 13 terpenoids. A total of 7897 significantly differentially expressed genes were identified, and genes involved in the terpenoid and phenylpropanoid metabolic pathways were highly enriched. Correlation analysis revealed that DEGs in terpenoid metabolism-related pathways were likely involved in the regulation of VOC biosynthesis in SSB-infested rice plants. Furthermore, two terpenoids, (-)-carvone and cedrol, were selected to analyse the behaviour of SSB and predators. Y-tube olfactometer tests demonstrated that both (-)-carvone and cedrol could repel SSB adults at higher concentrations; (-)-carvone could simultaneously attract the natural enemies of SSB, Cotesia chilonis and Trichogramma japonicum, and cedrol could only attract T. japonicum at lower concentrations. These findings provide a better understanding of the response of rice plants to SSB and contribute to the development of new strategies to control herbivorous pests.

Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial

BACKGROUND

Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration.

AIM OF REVIEW

In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Bridging Targeted (Zeno MRM-HR) and Untargeted (SWATH) LC-HRMS in a Single Run for Sensitive Exposomics

Traditionally, chemical exposure has been assessed by low-resolution mass spectrometry via targeted approaches due to the typically extremely low concentration of such compounds in biological samples. Nevertheless, untargeted approaches are now becoming a promising tool for a broader investigation of the exposome, covering additional compounds, their biotransformation products, and possible metabolic alterations (metabolomics). However, despite broad compound coverage, untargeted metabolomics still underperforms in ultratrace biomonitoring analysis. To overcome these analytical limitations, we present the development of the first combined targeted/untargeted LC-MS method, merging MRM-HR and SWATH experiments in one analytical run, making use of Zeno technology for improved sensitivity. Multiple reaction monitoring transitions were optimized for 135 highly diverse toxicants including mycotoxins, plasticizers, PFAS, personal care products ingredients, and industrial side products as well as potentially beneficial xenobiotics such as phytohormones. As a proof of concept, standard reference materials of human plasma (SRM 1950) and serum (SRM 1958) were analyzed with both Zeno MRM-HR + SWATH and SWATH-only methodologies. Results demonstrated a significant increase in sensitivity represented by the detection of lower concentration levels in spiked SRM materials (mean value: 2.2 and 3 times lower concentrations for SRMs 1950 and 1958, respectively). Overall, the detection frequency was increased by 68% (19 to 32 positive detections) in the MRM-HR + SWATH mode compared to the SWATH-only. This work presents a promising avenue for addressing the outstanding key challenge in the small-molecule omics field: finding a balance between high sensitivity and broad chemical coverage. It was demonstrated for exposomic applications but might be transferred to lipidomics and metabolomics workflows.

Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

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.

Microbial metabolomics' latest SICRIT: Soft ionization by Chemical Reaction in-Transfer mass spectrometry

Microbial metabolomics studies are a common approach to identifying microbial strains that have a capacity to produce new chemistries both in vitro and in situ. A limitation to applying microbial metabolomics to the discovery of new chemical entities is the rediscovery of known compounds, or "known unknowns." One contributing factor to this rediscovery is the majority of laboratories use one ionization source-electrospray ionization (ESI)-to conduct metabolomics studies. Although ESI is an efficient, widely adopted ionization method, its widespread use may contribute to the re-identification of known metabolites. Here, we present the use of a dielectric barrier discharge ionization (DBDI) for microbial metabolomics applications through the use of soft ionization chemical reaction in-transfer (SICRIT). Additionally, we compared SICRIT to ESI using two different Vibrio species-Vibrio fischeri, a symbiotic marine bacterium, and Vibrio cholerae, a pathogenic bacterium. Overall, we found that the SICRIT source ionizes a different set of metabolites than ESI, and it has the ability to ionize lipids more efficiently than ESI in positive mode. This work highlights the value of using more than one ionization source for the detection of metabolites.

Omics analysis unveils changes in the metabolome and lipidome of Caenorhabditis elegans upon polydopamine exposure

Polydopamine (PDA) is an insoluble biopolymer with a dark brown-black color that forms through the autoxidation of dopamine. Because of its outstanding biocompatibility and durability, PDA holds enormous promise for various applications, both in the biomedical and non-medical domains. To ensure human safety, protect health, and minimize environmental impacts, the assessment of PDA toxicity is important. In this study, metabolomics and lipidomics assessed the impact of acute PDA exposure on Caenorhabditis elegans (C. elegans). The findings revealed a pronounced perturbation in the metabolome and lipidome of C. elegans at the L4 stage following 24 hours of exposure to 100 µg/mL PDA. The changes in lipid composition varied based on lipid classes. Increased lipid classes included lysophosphatidylethanolamine, triacylglycerides, and fatty acids, while decreased species involved in several sub-classes of glycerophospholipids and sphingolipids. Besides, we detected 37 significantly affected metabolites in the positive and 8 in the negative ion modes due to exposure to PDA in C. elegans. The metabolites most impacted by PDA exposure were associated with purine metabolism, biosynthesis of valine, leucine, and isoleucine; aminoacyl-tRNA biosynthesis; and cysteine and methionine metabolism, along with pantothenate and CoA biosynthesis; the citrate cycle (TCA cycle); and beta-alanine metabolism. In conclusion, PDA exposure may intricately influence the metabolome and lipidome of C. elegans. The combined application of metabolomics and lipidomics offers additional insights into the metabolic perturbations involved in PDA-induced biological effects and presents potential biomarkers for the assessment of PDA safety.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges

Newborn screening programs have seen significant evolution since their initial implementation more than 60 years ago, with the primary goal of detecting treatable conditions within the earliest possible timeframe to ensure the optimal treatment and outcomes for the newborn. New technologies have driven the expansion of screening programs to cover additional conditions. In the current era, the breadth of screened conditions could be further expanded by integrating omic technologies such as untargeted metabolomics and genomics. Genomic screening could offer opportunities for lifelong care beyond the newborn period. For genomic newborn screening to be effective and ready for routine adoption, it must overcome barriers such as implementation cost, public acceptability, and scalability. Metabolomics approaches, on the other hand, can offer insight into disease phenotypes and could be used to identify known and novel biomarkers of disease. Given recent advances in metabolomic technologies, alongside advances in genomics including whole-genome sequencing, the combination of complementary multi-omic approaches may provide an exciting opportunity to leverage the best of both approaches and overcome their respective limitations. These techniques are described, along with the current outlook on multi-omic-based NBS research.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Integrated network pharmacology, molecular docking, and lipidomics to reveal the regulatory effect of Qingxuan Zhike granules on lipid metabolism in lipopolysaccharide-induced acute lung injury

Qingxuan Zhike granules (QXZKG), a traditional Chinese patent medication, has shown therapeutic potential against acute lung injury (ALI). However, the precise mechanism underlying its lung-protective effects requires further investigation. In this study, integrated network pharmacology, molecular docking, and lipidomics were used to elucidate QXZKG's regulatory effect on lipid metabolism in lipopolysaccharide-induced ALI. Animal experiments were conducted to substantiate the efficacy of QXZKG in reducing pro-inflammatory cytokines and mitigating pulmonary pathology. Network pharmacology analysis identified 145 active compounds that directly targeted 119 primary targets of QXZKG against ALI. Gene Ontology function analysis emphasized the roles of lipid metabolism and mitogen-activated protein kinase (MAPK) cascade as crucial biological processes. The MAPK1 protein exhibited promising affinities for naringenin, luteolin, and kaempferol. Lipidomic analysis revealed that 12 lipids showed significant restoration following QXZKG treatment (p < 0.05, FC >1.2 or <0.83). Specifically, DG 38:4, DG 40:7, PC O-40:8, TG 18:1_18:3_22:6, PI 18:2_20:4, FA 16:3, FA 20:3, FA 20:4, FA 22:5, and FA 24:5 were downregulated, while Cer 18:0;2O/24:0 and SM 36:1;2O/34:5 were upregulated in the QXZKG versus model groups. This study enhances our understanding of the active compounds and targets of QXZKG, as well as the potential of lipid metabolism in the treatment of ALI.

Dietary chlorogenic acid alleviates high-fat diet-induced steatotic liver disease by regulating metabolites and gut microbiota

The high-fat diet would lead to excessive fat storage in the liver to form metabolic dysfunction-associated steatotic liver disease (MASLD), and the trend is burgeoning. The aim of the study is to investigate the effects of chlorogenic acid (CGA) on metabolites and gut microorganisms in MASLD mice induced by a high-fat diet. In comparison to the HF group, the TC (total cholesterol), TG (total triglycerides), LDL-C (low-density lipoprotein cholesterol), AST (aspartate aminotransferase) and ALT (alanine transaminase) levels were reduced after CGA supplement. CGA led to an increase in l-phenylalanine, l-tryptophan levels, and promoted fatty acid degradation. CGA increased the abundance of the Muribaculaceae, Bacteroides and Parabacteroides. Changes in these microbes were significantly associated with the liver metabolites level and lipid profile level. These data suggest important roles for CGA regulating the gut microbiota, liver and caecum content metabolites, and TG-, TC- and LDL-C lowering function.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples

An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.

Serum and urine lipidomic profiles identify biomarkers diagnostic for seropositive and seronegative rheumatoid arthritis

Objective

Seronegative rheumatoid arthritis (RA) is defined as RA without circulating autoantibodies such as rheumatoid factor and anti-citrullinated protein antibodies; thus, early diagnosis of seronegative RA can be challenging. Here, we aimed to identify diagnostic biomarkers for seronegative RA by performing lipidomic analyses of sera and urine samples from patients with RA.

Methods

We performed untargeted lipidomic analysis of sera and urine samples from 111 RA patients, 45 osteoarthritis (OA) patients, and 25 healthy controls (HC). These samples were divided into a discovery cohort (n = 97) and a validation cohort (n = 84). Serum samples from 20 patients with systemic lupus erythematosus (SLE) were also used for validation.

Results

The serum lipidome profile of RA was distinguishable from that of OA and HC. We identified a panel of ten serum lipids and three urine lipids in the discovery cohort that showed the most significant differences. These were deemed potential lipid biomarker candidates for RA. The serum lipid panel was tested using a validation cohort; the results revealed an accuracy of 79%, a sensitivity of 71%, and a specificity of 86%. Both seropositive and seronegative RA patients were differentiated from patients with OA, SLE, and HC. Three urinary lipids showing differential expression between RA from HC were identified with an accuracy of 84%, but they failed to differentiate RA from OA. There were five lipid pathways that differed between seronegative and seropositive RA.

Conclusion

Here, we identified a panel of ten serum lipids as potential biomarkers that can differentiate RA from OA and SLE, regardless of seropositivity. In addition, three urinary lipids had diagnostic utility for differentiating RA from HC.

Sources of biases in the in vitro testing of nanomaterials: the role of the biomolecular corona

The biological fate of nanomaterials (NMs) is driven by specific interactions through which biomolecules, naturally adhering onto their surface, engage with cell membrane receptors and intracellular organelles. The molecular composition of this layer, called the biomolecular corona (BMC), depends on both the physical-chemical features of the NMs and the biological media in which the NMs are dispersed and cells grow. In this work, we demonstrate that the widespread use of 10% fetal bovine serum in an in vitro assay cannot recapitulate the complexity of in vivo systemic administration, with NMs being transported by the blood. For this purpose, we undertook a comparative journey involving proteomics, lipidomics, high throughput multiparametric in vitro screening, and single molecular feature analysis to investigate the molecular details behind this in vivo/in vitro bias. Our work indirectly highlights the need to introduce novel, more physiological-like media closer in composition to human plasma to produce realistic in vitro screening data for NMs. We also aim to set the basis to reduce this in vitro-in vivo mismatch, which currently limits the formulation of NMs for clinical settings.

Addressing the untargeted lipidomics challenge in urine samples: Comparative study of extraction methods by UHPLC-ESI-QTOF-MS

Urine analysis has remained a fundamental and widely used method in clinical diagnostics for over a century. With its minimal invasive nature and comprehensive range of analytes, urine has established itself as a clinical diagnostic tool for various disorders, including renal, urological, metabolic, and endocrine diseases. Furthermore, urine's unique attributes make it an attractive matrix for biomarker discovery, as well as in assessing the metabolic and physiological states of patients and healthy individuals alike. However, limitations in our knowledge of average values and sources of urinary lipids decrease the wider clinical application of urinary lipidomics. In this context, untargeted lipidomics analysis relies heavily on the extraction and analysis of lipids in biological samples. Nevertheless, this type of analysis presents challenges in lipid identification due to the diverse nature of lipids. Therefore, proper sample treatment before analysis is crucial to obtain robust and reproducible lipidomic profiles. To address this gap, we conducted a comparative study of a urine pool sample collected from twenty healthy volunteers using four different lipid extraction methods: one biphasic and three monophasic protocols. The extracted lipids were then analyzed using UHPLC-MS and MS/MS, and the semi-quantification of all the accurately annotated lipid species was performed for each extraction method.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

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.

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.

Selected Ion Monitoring for Orbitrap-Based Metabolomics

Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases

BACKGROUND

Lipids play key roles in numerous biological processes, including energy storage, cell membrane structure, signaling, immune responses, and homeostasis, making lipidomics a vital branch of metabolomics that analyzes and characterizes a wide range of lipid classes. Addressing the complex etiology, age-related risk, progression, inflammation, and research overlap in conditions like Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and Cancer poses significant challenges in the quest for effective therapeutic targets, improved diagnostic markers, and advanced treatments. Mass spectrometry is an indispensable tool in clinical lipidomics, delivering quantitative and structural lipid data, and its integration with technologies like Liquid Chromatography (LC), Magnetic Resonance Imaging (MRI), and few emerging Matrix-Assisted Laser Desorption Ionization- Imaging Mass Spectrometry (MALDI-IMS) along with its incorporation into Tissue Microarray (TMA) represents current advances. These innovations enhance lipidomics assessment, bolster accuracy, and offer insights into lipid subcellular localization, dynamics, and functional roles in disease contexts.

AIM OF THE REVIEW

The review article summarizes recent advancements in lipidomic methodologies from 2019 to 2023 for diagnosing major neurodegenerative diseases, Alzheimer's and Parkinson's, serious non-communicable cardiovascular diseases and cancer, emphasizing the role of lipid level variations, and highlighting the potential of lipidomics data integration with genomics and proteomics to improve disease understanding and innovative prognostic, diagnostic and therapeutic strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Clinical lipidomic studies are a promising approach to track and analyze lipid profiles, revealing their crucial roles in various diseases. This lipid-focused research provides insights into disease mechanisms, biomarker identification, and potential therapeutic targets, advancing our understanding and management of conditions such as Alzheimer's Disease, Parkinson's Disease, Cardiovascular Diseases, and specific cancers.

Determining topical product bioequivalence with stimulated Raman scattering microscopy

Generic drugs are essential for affordable medicine and improving accessibility to treatments. Bioequivalence (BE) is typically demonstrated by assessing a generic product's pharmacokinetics (PK) relative to a reference-listed drug (RLD). Accurately estimating cutaneous PK (cPK) at or near the site of action can be challenging for locally acting topical products. Certain cPK approaches are available for assessing local bioavailability (BA) in the skin. Stimulated Raman scattering (SRS) microscopy has unique capabilities enabling continuous, high spatial and temporal resolution and quantitative imaging of drugs within the skin. In this paper, we developed an approach based on SRS and a polymer-based standard reference for the evaluation of topical product BA and BE in human skin ex vivo. BE assessment of tazarotene-containing formulations was achieved using cPK parameters obtained within different skin microstructures. The establishment of BE between the RLD and an approved generic product was successfully demonstrated. Interestingly, within the constraints of the current study design the results suggest similar BA between the tested gel formulation and the reference cream formulation, despite the differences in the formulation/dosage form. Another formulation containing polyethylene glycol as the vehicle was demonstrated to be not bioequivalent to the RLD. Compared to using the SRS approach without a standard reference, the developed approach enabled more consistent and reproducible results, which is crucial in BE assessment. The abundant information from the developed approach can help to systematically identify key areas of study design that will enable a better comparison of topical products and support an assessment of BE.

Gut microbiota and metabolic biomarkers in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), a replacement of the nomenclature employed for NAFLD, is the most prevalent chronic liver disease worldwide. Despite its high global prevalence, NAFLD is often under-recognized due to the absence of reliable noninvasive biomarkers for diagnosis and staging. Growing evidence suggests that the gut microbiome plays a significant role in the occurrence and progression of NAFLD by causing immune dysregulation and metabolic alterations due to gut dysbiosis. The rapid advancement of sequencing tools and metabolomics has enabled the identification of alterations in microbiome signatures and gut microbiota-derived metabolite profiles in numerous clinical studies related to NAFLD. Overall, these studies have shown a decrease in α-diversity and changes in gut microbiota abundance, characterized by increased levels of Escherichia and Prevotella, and decreased levels of Akkermansia muciniphila and Faecalibacterium in patients with NAFLD. Furthermore, bile acids, short-chain fatty acids, trimethylamine N-oxide, and tryptophan metabolites are believed to be closely associated with the onset and progression of NAFLD. In this review, we provide novel insights into the vital role of gut microbiome in the pathogenesis of NAFLD. Specifically, we summarize the major classes of gut microbiota and metabolic biomarkers in NAFLD, thereby highlighting the links between specific bacterial species and certain gut microbiota-derived metabolites in patients with NAFLD.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Multi-omics and gut microbiome: Unveiling the pathogenic mechanisms of early-life pesticide exposure

The extensive application of pesticides in agricultural production has raised significant concerns about its impact on human health. Different pesticides, including fungicides, insecticides, and herbicides, cause environmental pollution and health problems for non-target organisms. Infants and young children are so vulnerable to the harmful effects of pesticide exposure that early-life exposure to pesticides deserves focused attention. Recent research lays emphasis on understanding the mechanism between negative health impacts and early-life exposure to various pesticides. Studies have explored the impacts of exposure to these pesticides on model organisms (zebrafish, rats, and mice), as well as the mechanism of negative health effects, based on advanced methodologies like gut microbiota and multi-omics. These methodologies help comprehend the pathogenic mechanisms associated with early-life pesticide exposure. In addition to presenting health problems stemming from early-life exposure to pesticides and their pathogenic mechanisms, this review proposes expectations for future research. These proposals include focusing on identifying biomarkers that indicate early-life pesticide exposure, investigating transgenerational effects, and seeking effective treatments for diseases arising from such exposure. This review emphasizes how to understand the pathogenic mechanisms of early-life pesticide exposure through gut microbiota and multi-omics, as well as the adverse health effects of such exposure.

Comparing univariate filtration preceding and succeeding PLS-DA analysis on the differential variables/metabolites identified from untargeted LC-MS metabolomics data

BACKGROUND

PLS-DA of high-dimensional metabolomics data is frequently employed to capture the most pertinent features to sample classification. But the presence of numerous insignificant input features could distort the PLS-DA model, blow up and scramble the selected differential features. Usually, univariate filtration is subsequently complemented to refine the selected features, but often giving unstable results. Whereas by precluding insignificant features through univariate data prefiltration assessed by FDR adjusted p-value, PLS-DA can generate more stable and reliable differential features. We explored and compared these two data analysis procedures to gain insights into the underlying mechanisms responsible for the disparate results.

RESULTS

The effect of univariate data filtration preceding and succeeding PLS-DA analysis on the identified discriminative features/metabolites was investigated using LC-MS data acquired on the samples of human serum and C. elegans extracts, with and without metabolite standards spiked to simulate the treated and control groups of biological samples. It was shown that the univariate data prefiltration before PLS-DA usually gave less but more stable and likely more reliable and meaningful differential features, while PLS-DA applied directly to the original data could be affected by the presence of insignificant features and orthogonal noise. Large number of insignificant variables and orthogonal noise could distort the generated PLS-DA model and affect the p(corr) value, and artificially inflate the calculated VIP values of relevant features due to the increased total number of input features for model construction, thus leading to more false positives selected by the conventional VIP threshold of 1.0.

SIGNIFICANCE AND NOVELTY

Univariate data filtration preceding PLS-DA was important for the identification of reliable differential features if using a conventional threshold of VIP of 1.0. Presence of insignificant features could distort the PLS-DA model and inflate VIP values. Appropriate VIP threshold is associated with the numbers of input features and the model components. For PLS-DA without univariate prefiltration, threshold of VIP larger than 1.0 is recommended for the selection of discriminative features to reduce the false positives.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

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.

LiLA: lipid lung-based ATLAS built through a comprehensive workflow designed for an accurate lipid annotation

Accurate lipid annotation is crucial for understanding the role of lipids in health and disease and identifying therapeutic targets. However, annotating the wide variety of lipid species in biological samples remains challenging in untargeted lipidomic studies. In this work, we present a lipid annotation workflow based on LC-MS and MS/MS strategies, the combination of four bioinformatic tools, and a decision tree to support the accurate annotation and semi-quantification of the lipid species present in lung tissue from control mice. The proposed workflow allowed us to generate a lipid lung-based ATLAS (LiLA), which was then employed to unveil the lipidomic signatures of the Mycobacterium tuberculosis infection at two different time points for a deeper understanding of the disease progression. This workflow, combined with manual inspection strategies of MS/MS data, can enhance the annotation process for lipidomic studies and guide the generation of sample-specific lipidome maps. LiLA serves as a freely available data resource that can be employed in future studies to address lipidomic alterations in mice lung tissue.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Detailed Protocol for Solid-Phase Extraction for Lipidomic Analysis

Developing robust analytical techniques is a vital phase to facilitate understanding the roles and impacts of various omic profilings in cellular functions. The comprehensive analysis of various biological molecules within a biological system requires a precise sample preparation technique. Solid-Phase Extraction (SPE) has proven to be an indispensable method in lipidomic analysis, providing an uncomplicated and user-friendly technique for extraction and purification of lipid components from complex biological matrices. Of all the factors influencing the reliability and success of SPE, column or adsorbent materials, flow rate, and storage conditions are paramount in terms of their significance. In this chapter, we will discuss in detail the SPE steps for lipidomic analysis in biofluid samples (serum and plasma) and muscle tissues.

Proteomics for Microalgae Extracts by High-Resolution Mass Spectrometry

Two protocols of protein extraction from Arthrospira platensis (spirulina) microalgae to study their proteome by mass spectrometry (MS) are here presented. The first is based on an aqueous buffer solution of Tris-HCl and the second on cold acetone. The identification of proteins was carried out by a bottom-up approach, which involves enzymatic digestion of extracted proteins followed by either matrix-assisted laser desorption ionization with time-of-flight (MALDI-TOF) MS or liquid chromatography (LC) coupled with electrospray ionization (ESI) and Fourier-transform tandem MS. While MALDI-TOF MS allowed for a fast peptide mass fingerprinting (PMF) check yet identifying less than 20 proteins in the extracted samples, the data-dependent acquisitions (DDA) mode of reversed-phase (RP) LC-ESI tandem FTMS/MS separations allowed us to recognize more than one hundred proteins by searching into dedicated spectral libraries. The application of MALDI-TOF MS analysis was found, however, of great support for preliminary investigations of cyanobacteria samples before proceeding with the RPLC-ESI-MS/MS DDA investigation, which definitively allows for a qualitative proteome analysis also of minor spirulina proteins in processed foodstuffs. Although the protein content in spirulina can be influenced by cultivation and environmental conditions, e.g., light intensity, climate, and water/air quality, here the qualitative chemical profiles of the examined samples were characterized by similar composition in high-quality proteins as phycocyanins and phycoerythrins.

Identification of ethyl vanillin in strawberry (Fragaria × ananassa) using a targeted metabolomics strategy: From artificial to natural

Improving flavor can be an important goal of strawberry through breeding that is enhanced through the accurate identification and quantification of flavor compounds. Herein, a targeted metabolomics strategy was developed using liquid-liquid extraction, an in-house standard database, and GC-MS/MS analysis. The database consisted of key food odorants (KFOs), artificial flavor compounds (AFCs) and volatiles. A total of 131 flavor compounds were accurately identified in Medallion® 'FL 16.30-128' strawberry. Importantly, ethyl vanillin was identified for the first time in natural food. Multiple techniques, including GC-MS, GC-MS/MS and UPLC-MS/MS were applied to ensure the identification. The ethyl vanillin in the Medallion® samples were determined in a range of concentrations from 0.070 ± 0.0006 µg/kg to 0.1372 ± 0.0014 µg/kg by using stable isotope dilution analysis. The identification of ethyl vanillin in strawberry implys the future commercial use a natural flavor compound and the potential to identify genes and proteins associated with its biosynthesis.