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

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.

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.

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.

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.

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.

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.

Fish Skin Grafts Affect Adenosine and Methionine Metabolism during Burn Wound Healing

Burn wound healing is a complex process orchestrated through successive biochemical events that span from weeks to months depending on the depth of the wound. Here, we report an untargeted metabolomics discovery approach to capture metabolic changes during the healing of deep partial-thickness (DPT) and full-thickness (FT) burn wounds in a porcine burn wound model. The metabolic changes during healing could be described with six and seven distinct metabolic trajectories for DPT and FT wounds, respectively. Arginine and histidine metabolism were the most affected metabolic pathways during healing, irrespective of burn depth. Metabolic proxies for oxidative stress were different in the wound types, reaching maximum levels at day 14 in DPT burns but at day 7 in FT burns. We examined how acellular fish skin graft (AFSG) influences the wound metabolome compared to other standard-or-care burn wound treatments. We identified changes in metabolites within the methionine salvage pathway, specifically in DPT burn wounds that is novel to the understanding of the wound healing process. Furthermore, we found that AFSGs boost glutamate and adenosine in wounds that is of relevance given the importance of purinergic signaling in regulating oxidative stress and wound healing. Collectively, these results serve to define biomarkers of burn wound healing. These results conclusively contribute to the understanding of the multifactorial mechanism of the action of AFSG that has traditionally been attributed to its structural properties and omega-3 fatty acid content.

Abrupt Photoperiod Changes Differentially Modulate Hepatic Antioxidant Response in Healthy and Obese Rats: Effects of Grape Seed Proanthocyanidin Extract (GSPE)

Disruptions of the light/dark cycle and unhealthy diets can promote misalignment of biological rhythms and metabolic alterations, ultimately leading to an oxidative stress condition. Grape seed proanthocyanidin extract (GSPE), which possesses antioxidant properties, has demonstrated its beneficial effects in metabolic-associated diseases and its potential role in modulating circadian disruptions. Therefore, this study aimed to assess the impact of GSPE administration on the liver oxidant system of healthy and diet-induced obese rats undergoing a sudden photoperiod shift. To this end, forty-eight photoperiod-sensitive Fischer 344/IcoCrl rats were fed either a standard (STD) or a cafeteria diet (CAF) for 6 weeks. A week before euthanizing, rats were abruptly transferred from a standard photoperiod of 12 h of light/day (L12) to either a short (6 h light/day, L6) or a long photoperiod (18 h light/day, L18) while receiving a daily oral dose of vehicle (VH) or GSPE (25 mg/kg). Alterations in body weight gain, serum and liver biochemical parameters, antioxidant gene and protein expression, and antioxidant metabolites were observed. Interestingly, GSPE partially ameliorated these effects by reducing the oxidative stress status in L6 through an increase in GPx1 expression and in hepatic antioxidant metabolites and in L18 by increasing the NRF2/KEAP1/ARE pathway, thereby showing potential in the treatment of circadian-related disorders by increasing the hepatic antioxidant response in a photoperiod-dependent manner.

Metabolomics-a powerful tool in livestock research

Advancements in the Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) along with recent developments in omics sciences have resulted in a better understanding of molecular mechanisms and pathways associated with the physio-pathological state of the animal. Metabolomics is a post-genomics tool that deals with small molecular metabolites in a given set of time which provides clear information about the status of an organism. Recently many researchers mainly focus their research on metabolomics studies due to its valuable information in the various fields of livestock management and precision dairying. The main aim of the present review is to provide an insight into the current research output from different sources and application of metabolomics in various areas of livestock including nutri-metabolomics, disease diagnosis advancements, reproductive disorders, pharmaco-metabolomics, genomics studies, and dairy production studies. The present review would be helpful in understanding the metabolomics methodologies and use of livestock metabolomics in various areas in a brief way.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Application of metabolomics in diagnostics and differentiation of meningitis: A narrative review with a critical approach to the literature

Due to its high mortality rate associated with various life-threatening sequelae, meningitis poses a vital problem in contemporary medicine. Numerous algorithms, many of which were derived with the aid of artificial intelligence, were brought up in a strive for perfection in predicting the status of sepsis-related survival or exacerbation. This review aims to provide key insights on the contextual utilization of metabolomics. The aim of this the metabolomic approach set of methods can be used to investigate both bacterial and host metabolite sets from both the host and its microbes in several types of specimens - even in one's breath, mainly with use of two methods - Mass Spectrometry (MS) and Nuclear Magnetic Resonance (NMR). Metabolomics, and has been used to elucidate the mechanisms underlying disease development and metabolic identification changes in a wide range of metabolite contents, leading to improved methods of diagnosis, treatment, and prognosis of meningitis. Mass spectrometry (MS) and Nuclear Magnetic Resonance (NMR) are the main analytical platforms used in metabolomics. Its high sensitivity accounts for the usefulness of metabolomics in studies into meningitis, its sequelae, and concomitant comorbidities. Metabolomics approaches are a double-edged sword, due to not only their flexibility, but also - high complexity, as even minor changes in the multi-step methods can have a massive impact on the results. Information on the differential diagnosis of meningitis act as a background in presenting the merits and drawbacks of the use of metabolomics in context of meningeal infections.

Water, Water Everywhere, but Every Drop Unique: Challenges in the Science to Understand the Role of Contaminants of Emerging Concern in the Management of Drinking Water Supplies

The protection and management of water resources continues to be challenged by multiple and ongoing factors such as shifts in demographic, social, economic, and public health requirements. Physical limitations placed on access to potable supplies include natural and human-caused factors such as aquifer depletion, aging infrastructure, saltwater intrusion, floods, and drought. These factors, although varying in magnitude, spatial extent, and timing, can exacerbate the potential for contaminants of concern (CECs) to be present in sources of drinking water, infrastructure, premise plumbing and associated tap water. This monograph examines how current and emerging scientific efforts and technologies increase our understanding of the range of CECs and drinking water issues facing current and future populations. It is not intended to be read in one sitting, but is instead a starting point for scientists wanting to learn more about the issues surrounding CECs. This text discusses the topical evolution CECs over time (Section 1), improvements in measuring chemical and microbial CECs, through both analysis of concentration and toxicity (Section 2) and modeling CEC exposure and fate (Section 3), forms of treatment effective at removing chemical and microbial CECs (Section 4), and potential for human health impacts from exposure to CECs (Section 5). The paper concludes with how changes to water quantity, both scarcity and surpluses, could affect water quality (Section 6). Taken together, these sections document the past 25 years of CEC research and the regulatory response to these contaminants, the current work to identify and monitor CECs and mitigate exposure, and the challenges facing the future.

Metabolomics and Machine Learning Identify Metabolic Differences and Potential Biomarkers for Frequent Versus Infrequent Gout Flares

OBJECTIVE

The objective of this study was to discover differential metabolites and pathways underlying infrequent gout flares (InGF) and frequent gout flares (FrGF) using metabolomics and to establish a predictive model by machine learning (ML) algorithms.

METHODS

Serum samples from a discovery cohort of 163 patients with InGF and 239 patients with FrGF were analyzed by mass spectrometry-based untargeted metabolomics to profile differential metabolites and explore dysregulated metabolic pathways using pathway enrichment analysis and network propagation-based algorithms. ML algorithms were performed to establish a predictive model based on selected metabolites, which was further optimized by a quantitative targeted metabolomics method and validated in an independent validation cohort with 97 participants with InGF and 139 participants with FrGF.

RESULTS

A total of 439 differential metabolites between InGF and FrGF groups were identified. Top dysregulated pathways included carbohydrates, amino acids, bile acids, and nucleotide metabolism. Subnetworks with maximum disturbances in the global metabolic networks featured cross-talk between purine metabolism and caffeine metabolism, as well as interactions among pathways involving primary bile acid biosynthesis, taurine and hypotaurine metabolism, alanine, aspartate, and glutamate metabolism, suggesting epigenetic modifications and gut microbiome in metabolic alterations underlying InGF and FrGF. Potential metabolite biomarkers were identified using ML-based multivariable selection and further validated by targeted metabolomics. Area under receiver operating characteristics curve for differentiating InGF and FrGF achieved 0.88 and 0.67 for the discovery and validation cohorts, respectively.

CONCLUSION

Systematic metabolic alterations underlie InGF and FrGF, and distinct profiles are associated with differences in gout flare frequencies. Predictive modeling based on selected metabolites from metabolomics can differentiate InGF and FrGF.

On-capillary alkylation micro-reactor: a facile strategy for proteo-metabolome profiling in the same single cells

Single-cell multi-omics analysis can provide comprehensive insights to study cell-to-cell heterogeneity in normal and disease physiology. However, due to the lack of amplification technique, the measurement of proteome and metabolome in the same cell is challenging. Herein, a novel on-capillary alkylation micro-reactor (OCAM) was developed to achieve proteo-metabolome profiling in the same single cells, by which proteins were first covalently bound to an iodoacetic acid functionalized open-tubular capillary micro-reactor via sulfhydryl alkylation reaction, and metabolites were rapidly eluted, followed by on-column digestion of captured proteins. Compared with existing methods for low-input proteome sample preparation, OCAM exhibited improved efficiency, anti-interference ability and recovery, enabling the identification of an average of 1509 protein groups in single HeLa cells. This strategy was applied to single-cell proteo-metabolome analysis of mouse oocytes at different stages, 3457 protein groups and 171 metabolites were identified in single oocytes, which is the deepest coverage of proteome and metabolome from single mouse oocytes to date, achieving complementary characterization of metabolic patterns during oocyte maturation.

Deep graph convolutional network for small-molecule retention time prediction

The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-based molecule identification. Recent research shows that graph neural networks (GNNs) outperform traditional machine learning algorithms in RT prediction. However, all of these models use relatively shallow GNNs. This study for the first time investigates how depth affects GNNs' performance on RT prediction. The results demonstrate that a notable improvement can be achieved by pushing the depth of GNNs to 16 layers by the adoption of residual connection. Additionally, we also find that graph convolutional network (GCN) model benefits from the edge information. The developed deep graph convolutional network, DeepGCN-RT, significantly outperforms the previous state-of-the-art method and achieves the lowest mean absolute percentage error (MAPE) of 3.3% and the lowest mean absolute error (MAE) of 26.55 s on the SMRT test set. We also finetune DeepGCN-RT on seven datasets with various chromatographic conditions. The mean MAE of the seven datasets largely decreases 30% compared to previous state-of-the-art method. On the RIKEN-PlaSMA dataset, we also test the effectiveness of DeepGCN-RT in assisting molecular structure identification. By 30% lessening the number of potential structures, DeepGCN-RT is able to improve top-1 accuracy by about 11%.

Reducing Mass Confusion over the Microbiome

As genetic tools continue to emerge and mature, more information is revealed about the identity and diversity of microbial community members. Genetic tools can also be used to make predictions about the chemistry that bacteria and fungi produce to function and communicate with one another and the host. Ongoing efforts to identify these products and link genetic information to microbiome chemistry rely on analytical tools. This tutorial highlights recent advancements in microbiome studies driven by techniques in mass spectrometry.

Database-assisted, globally optimized targeted secondary electrospray ionization high resolution mass spectrometry (dGOT-SESI-HRMS) and spectral stitching enhanced volatilomics analysis of bacterial metabolites

Secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) is an innovative analytical technique for the rapid and non-invasive analysis of volatile organic compounds (VOCs). However, compound annotation and ion suppression in the SESI source has hindered feature detection, stability and reproducibility of SESI-HRMS in untargeted volatilomics. To address this, we have developed and optimized a novel pseudo-targeted approach, database-assisted globally optimized targeted (dGOT)-SESI-HRMS using the microbial-VOC (mVOC) database, and spectral stitching methods to enhance metabolite detection in headspace of anaerobic bacterial cultures. Headspace volatiles from representative bacteria strains were assessed using full scan with data dependent acquisition (DDA), conventional globally optimized targeted (GOT) method, and spectral stitching supported dGOT experiments based on a MS peaks list derived from mVOC. Our results indicate that spectral stitching supported dGOT-SESI-HRMS can proportionally fragment peaks with respect to different analysis windows, with a total of 109 VOCs fragmented from 306 targeted compounds. Of the collected spectra, 88 features were confirmed as culture derived volatiles with respect to media blanks. Annotation was also achieved with a total of 25 unique volatiles referenced to standard databases allowing for biological interpretation. Principal component analysis (PCA) summarizing the headspace volatile demonstrated improved separation of clusters when data was acquired using the dGOT method. Collectively, our dGOT-SESI-HRMS method afforded robust capability of capturing unique VOC profiles from different bacterial strains and culture conditions when compared to conventional GOT and DDA modes, suggesting the newly developed approach can serve as a more reliable analytical method for the sensitive monitoring of gut microbial metabolism.

Sweet treats before sleep disrupt the clock system and increase metabolic risk markers in healthy rats

AIM

Biological rhythms are endogenously generated natural cycles that act as pacemakers of different physiological mechanisms and homeostasis in the organism, and whose disruption increases metabolic risk. The circadian rhythm is not only reset by light but it is also regulated by behavioral cues such as timing of food intake. This study investigates whether the chronic consumption of a sweet treat before sleeping can disrupt diurnal rhythmicity and metabolism in healthy rats.

METHODS

For this, 32 Fischer rats were administered daily a low dose of sugar (160 mg/kg, equivalent to 2.5 g in humans) as a sweet treat at 8:00 a.m. or 8:00 p.m. (ZT0 and ZT12, respectively) for 4 weeks. To elucidate diurnal rhythmicity of clock gene expression and metabolic parameters, animals were sacrificed at different times, including 1, 7, 13, and 19 h after the last sugar dose (ZT1, ZT7, ZT13, and ZT19).

RESULTS

Increased body weight gain and higher cardiometabolic risk were observed when sweet treat was administered at the beginning of the resting period. Moreover, central clock and food intake signaling genes varied depending on snack time. Specifically, the hypothalamic expression of Nampt, Bmal1, Rev-erbα, and Cart showed prominent changes in their diurnal expression pattern, highlighting that sweet treat before bedtime disrupts hypothalamic control of energy homeostasis.

CONCLUSIONS

These results show that central clock genes and metabolic effects following a low dose of sugar are strongly time-dependent, causing higher circadian metabolic disruption when it is consumed at the beginning of the resting period, that is, with the late-night snack.

Exploratory optimisation of a LC-HRMS based analytical method for untargeted metabolomic screening of Cannabis Sativa L. through Data Mining

BACKGROUND

Recent increase in public acceptance of cannabis as a natural medical alternative for certain neurological pathologies has led to its approval in different regions of the world. However, due to its previous illegal background, little research has been conducted around its biochemical insights. Therefore, in the current framework, metabolomics may be a suitable approach for deepening the knowledge around this plant species. Nevertheless, experimental methods in metabolomics must be carefully handled, as slight modifications can lead to metabolomic coverage loss. Hence, the main objective of this work was to optimise an analytical method for appropriate untargeted metabolomic screening of cannabis.

RESULTS

We present an empirically optimised experimental procedure through which the broadest metabolomic coverage was obtained, in which extraction solvents for metabolite isolation, chromatographic columns for LC-qOrbitrap analysis and plant-representative biological tissues were compared. By exploratory means, it was determined that the solvent combination composed of CHCl3:H2O:CH3OH (2:1:1, v/v) provided the highest number of features from diverse chemical classes, as it was a two-phase extractant. In addition, a reverse phase 2.6 μm C18 100 Å (150 × 3 mm) chromatographic column was determined as the appropriate choice for adequate separation and further detection of the diverse metabolite classes. Apart from that, overall chromatographic peak quality provided by each column was observed and the need for batch correction methods through quality control (QC) samples was confirmed. At last, leaf and flower tissues resulted to provide complementary metabolic information of the plant, to the detriment of stem tissue, which resulted to be negligible.

SIGNIFICANCE

It was concluded that the optimised experimental procedure could significantly ease the path for future research works related to cannabis metabolomics by LC-HRMS means, as the work was based on previous plant metabolomics literature. Furthermore, it is crucial to highlight that an optimal analytical method can vary depending on the main objective of the research, as changes in the experimental factors can lead to different outcomes, regardless of whether the results are better or worse.

Variations in metabolite profiles of serum coronas produced around PEGylated liposomal drugs by surface property

Understanding biomolecular coronas that spontaneously occur around nanocarriers (NCs) in biological fluids is critical to nanomedicine as the coronas influence the behaviors of NCs in biological systems. In contrast to extensive investigations of protein coronas over the past decades, understanding of the coronas of biomolecules beyond proteins, e.g., metabolites, has been rather limited despite such biochemicals being ubiquitously involved in the coronas, which may influence the bio-nano interactions and thus exert certain biological impacts. In this study, serum biomolecular coronas, in particular the coronas of metabolites including lipids, around PEGylated doxorubicin-loaded liposomes with different surface property were investigated. The surface properties of liposomal drugs varied in terms of surface charge and PEGylation density by employing different ionic lipids such as DOTAP and DOPS and different concentrations of PEGylation lipids in liposome formulation. Using the liposomal drugs, the influence of the surface property on the serum metabolite profiles in the coronas was traced for target molecules of 220 lipids and 88 hydrophilic metabolites. From the results, it was found that metabolites rather than proteins mainly constitute the serum coronas on the liposomal drugs. Most of the serum metabolites were found to be retained in the coronas but with altered abundances. Depending on their class, lipids exhibited a different dependence on the surface property. However, overall, lipids appeared to favor corona formation on more negatively charged and PEGylated surfaces. Hydrophilic metabolites also exhibited a similar propensity for corona formation. This study on the surface dependence of metabolite corona formation provides a fundamental contribution toward attaining a comprehensive understanding of biomolecular coronas, which will be critical to the development of efficient nanomedicine.

Metabolomics and lipidomics strategies in modern drug discovery and development

Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.

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 extractions) 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, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of 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.