Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

Development of metabolome extraction strategy for metabolite profiling of skin tissue

INTRODUCTION

Changes in skin phenotypic characteristics are based on skin tissue. The study of the metabolic changes in skin tissue can help understand the causes of skin diseases and identify effective therapeutic interventions.

OBJECTIVES

We aimed to establish and optimize a non-targeted skin metabolome extraction system for skin tissue metabolomics with high metabolite coverage, recovery, and reproducibility using gas chromatography/mass spectrometry.

METHODS

The metabolites in skin tissues were extracted using eleven different extraction systems, which were designed using reagents with different polarities based on sequential solid-liquid extraction employing a two-step strategy and analyzed using gas chromatograph/mass spectrometry. The extraction efficiency of diverse solvents was evaluated by coefficient of variation (CV), multivariate analysis, metabolites coverage, and relative peak area analysis.

RESULTS

We identified 119 metabolites and the metabolite profiles differed significantly between the eleven extraction systems. Metabolites with high abundances in the organic extraction systems, followed by aqueous extraction, were involved in the biosynthesis of unsaturated fatty acids, while metabolites with high abundances in the aqueous extraction systems, followed by organic extraction, were involved in amino sugar and nucleotide sugar metabolism, and glycerolipid metabolism. MeOH/chloroform-H2O and MeOH/H2O-chloroform were the extraction systems that yielded the highest number of metabolites, while MeOH/acetonitrile (ACN)-H2O and ACN/H2O-IPA exhibited superior metabolite recoveries.

CONCLUSION

Our results demonstrated that our research facilitates the selection of an appropriate metabolite extraction approach based on the experimental purpose for the metabolomics study of skin tissue.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

NMR-Based Mitochondria Metabolomic Profiling: A New Approach To Reveal Cancer-Associated Alterations

Studying metabolism may assist in understanding the relationship between normal and dysfunctional mitochondrial activity and various diseases, such as neurodegenerative, cardiovascular, autoimmune, psychiatric, and cancer. Nuclear magnetic resonance-based metabolomics represents a powerful method to characterize the chemical content of complex samples and has been successfully applied to studying a range of conditions. However, an optimized methodology is lacking for analyzing isolated organelles, such as mitochondria. In this study, we report the development of a protocol to metabolically profile mitochondria from healthy, tumoral, and metastatic tissues. Encouragingly, this approach provided quantitative information about up to 45 metabolites in one comprehensive and robust analysis. Our results revealed significant differences between whole-cell and mitochondrial metabolites, which supports a more refined approach to metabolic analysis. We applied our optimized methodology to investigate aggressive and metastatic breast cancer in mouse tissues, discovering that lung mitochondria exhibit an altered metabolic fingerprint. Specific amino acids, organic acids, and lipids showed significant increases in levels when compared with mitochondria from healthy tissues. Our optimized methodology could promote a better understanding of the molecular mechanisms underlying breast cancer aggressiveness and mitochondrial-related diseases and support the optimization of new advanced therapies.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

Evaluation of Lipid Extraction Protocols for Untargeted Analysis of Mouse Tissue Lipidome

Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid-liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl3 (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine.

Unravelling the Link between Psychological Distress and Liver Disease: Insights from an Anxiety-like Rat Model and Metabolomics Analysis

Psychological distress is associated with an increase in liver disease mortality. This association highlights the close relationship between psychological and physical health. The underlying mechanism of this association needs to be elucidated. In this study, a rat model of anxiety was developed via compound stress. Changes in the HPA axis and inflammatory factors in the brains of the rats were evaluated for behavioral tests and liver function, respectively. The liver metabolic profiles of the rats were characterized through liquid chromatography-mass spectrometry (LC-MS). Differential metabolites were screened based on the conditions of p < 0.05 and VIP > 1. A pathway enrichment analysis was performed on the metabolomics data using the Ingenuity Pathway Analysis (IPA). Immunofluorescence (IF), immunohistochemistry (IHC), and Western blotting assays were performed to examine the expression of the screened target epidermal growth factor receptor (EGFR) and to elucidate the pathway associated with the mechanism. The results showed the impairment of liver function among the rats in an anxiety-like state. Additionally, 61 differential metabolites in the control and anxiety groups were screened using metabolomics (p < 0.05, VIP > 1). The results of the IPA analysis showed that the key target was EGFR. We also found that an anxiety-like state in rats may cause liver injury through the EFGR/PI3K/AKT/NF-κB pathway, which can lead to the production of inflammatory factors in the liver. Our results revealed a mechanism by which anxiety-like behavior leads to liver damage in rats. The findings of this study provided new insights into the deleterious effects of psychological problems on physical health.

Ultrahigh-performance liquid chromatography-high-resolution mass spectrometry-based plasma metabolomics study of thymoma and thymic hyperplasia

RATIONALE

Thymoma is a rare malignant tumor but it is the most common primary tumor of the anterior mediastinum. The current imaging methods for thymoma screening suffer from false positive rate problems, and thymoma pathogenesis remains elusive. Study of thymoma metabolic characteristics could provide clues for improving the diagnosis and understanding the pathogenesis of thymoma.

METHODS

Metabolic profiling of plasma from thymoma and thymic hyperplasia patients was performed using ultrahigh-performance liquid chromatography combined with high-resolution mass spectrometry in both positive and negative ionization modes. After pre- and post-processing, the dataset was divided into three age groups and statistical analysis was performed to select differential metabolites of thymoma. For feature identification, experimental tandem mass spectra were matched to those of databases and available chemical standards, and also manually annotated with plausible chemical structures to ensure high identification confidence.

RESULTS

A total of 47 differential metabolites were identified in thymoma. Significantly higher levels of histidine, sphinganine 1-phosphate, lactic acid dimer, phenylacetylglutamine, LPC (18:3) and LPC (16:1), and significantly lower levels of phenylalanine, indole-3-propionic acid (IPA), hippuric acid and mesobilirubinogen were associated with thymoma. Tryptophan level in thymoma-associated myasthenia gravis (TAMG) was significantly lower than that of the MG(-) group. IPA and hippuric acid abundances exhibited increasing trends from indolent to aggressive thymoma.

CONCLUSIONS

Our study revealed aberrant aromatic amino acid metabolism and fatty acid oxidation might be associated with thymoma. The identified unique metabolic characteristics of thymoma may provide valuable information for study of the molecular mechanism of thymoma pathogenesis, and improvement of diagnosis and discovery of new therapeutic strategies for thymoma.

Early Differentiation Signatures in Human Induced Pluripotent Stem Cells Determined by Non-Targeted Metabolomics Analysis

Human induced pluripotent stem cells (hiPSCs) possess immense potential as a valuable source for the generation of a wide variety of human cells, yet monitoring the early cell differentiation towards a specific lineage remains challenging. In this study, we employed a non-targeted metabolomic analysis technique to analyze the extracellular metabolites present in samples as small as one microliter. The hiPSCs were subjected to differentiation by initiating culture under the basal medium E6 in combination with chemical inhibitors that have been previously reported to direct differentiation towards the ectodermal lineage such as Wnt/β-catenin and TGF-β kinase/activin receptor, alone or in combination with bFGF, and the inhibition of glycogen kinase 3 (GSK-3), which is commonly used for the diversion of hiPSCs towards mesodermal lineage. At 0 h and 48 h, 117 metabolites were identified, including biologically relevant metabolites such as lactic acid, pyruvic acid, and amino acids. By determining the expression of the pluripotency marker OCT3/4, we were able to correlate the differentiation status of cells with the shifted metabolites. The group of cells undergoing ectodermal differentiation showed a greater reduction in OCT3/4 expression. Moreover, metabolites such as pyruvic acid and kynurenine showed dramatic change under ectodermal differentiation conditions where pyruvic acid consumption increased 1-2-fold, while kynurenine secretion decreased 2-fold. Further metabolite analysis uncovered a group of metabolites specifically associated with ectodermal lineage, highlighting the potential of our findings to determine the characteristics of hiPSCs during cell differentiation, particularly under ectodermal lineage conditions.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Saliva Metabolomic Profile in Dental Medicine Research: A Narrative Review

Metabolomic research tends to increase in popularity over the years, leading to the identification of new biomarkers related to specific health disorders. Saliva is one of the most newly introduced and systematically developed biofluids in the human body that can serve as an informative substance in the metabolomic profiling armamentarium. This review aims to analyze the current knowledge regarding the human salivary metabolome, its alterations due to physiological, environmental and external factors, as well as the limitations and drawbacks presented in the most recent research conducted, focusing on pre—analytical and analytical workflows. Furthermore, the use of the saliva metabolomic profile as a promising biomarker for several oral pathologies, such as oral cancer and periodontitis will be investigated.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

Omics technologies in allergy and asthma research: An EAACI position paper

Allergic diseases and asthma are heterogenous chronic inflammatory conditions with several distinct complex endotypes. Both environmental and genetic factors can influence the development and progression of allergy. Complex pathogenetic pathways observed in allergic disorders present a challenge in patient management and successful targeted treatment strategies. The increasing availability of high-throughput omics technologies, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics allows studying biochemical systems and pathophysiological processes underlying allergic responses. Additionally, omics techniques present clinical applicability by functional identification and validation of biomarkers. Therefore, finding molecules or patterns characteristic for distinct immune-inflammatory endotypes, can subsequently influence its development, progression, and treatment. There is a great potential to further increase the effectiveness of single omics approaches by integrating them with other omics, and nonomics data. Systems biology aims to simultaneously and longitudinally understand multiple layers of a complex and multifactorial disease, such as allergy, or asthma by integrating several, separated data sets and generating a complete molecular profile of the condition. With the use of sophisticated biostatistics and machine learning techniques, these approaches provide in-depth insight into individual biological systems and will allow efficient and customized healthcare approaches, called precision medicine. In this EAACI Position Paper, the Task Force "Omics technologies in allergic research" broadly reviewed current advances and applicability of omics techniques in allergic diseases and asthma research, with a focus on methodology and data analysis, aiming to provide researchers (basic and clinical) with a desk reference in the field. The potential of omics strategies in understanding disease pathophysiology and key tools to reach unmet needs in allergy precision medicine, such as successful patients' stratification, accurate disease prognosis, and prediction of treatment efficacy and successful prevention measures are highlighted.

Transcriptome and Metabolome Analyses Reveal Differences in Terpenoid and Flavonoid Biosynthesis in Cryptomeria fortunei Needles Across Different Seasons

Cryptomeria fortunei (Chinese cedar) has outstanding medicinal value due to its abundant flavonoid and terpenoid contents. The metabolite contents of C. fortunei needles differ across different seasons. However, the biosynthetic mechanism of these differentially synthesized metabolites (DSMs) is poorly understood. To improve our understanding of this process, we performed integrated non-targeted metabolomic liquid chromatography and gas chromatography mass spectrometry (LC-MS and GC-MS), and transcriptomic analyses of summer and winter needles. In winter, the C. fortunei needle ultrastructure was damaged, and the chlorophyll content and F _v/F _m were significantly (p < 0.05) reduced. Based on GC-MS and LC-MS, we obtained 106 and 413 DSMs, respectively; based on transcriptome analysis, we obtained a total of 41.17 Gb of clean data and assembled 33,063 unigenes, including 14,057 differentially expressed unigenes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DSMs/DEGs were significantly (p < 0.05) enriched in many biosynthesis pathways, such as terpenoids, photosynthates, and flavonoids. Integrated transcriptomic and metabonomic analyses showed that seasonal changes have the greatest impact on photosynthesis pathways, followed by terpenoid and flavonoid biosynthesis pathways. In summer Chinese cedar (SCC) needles, DXS, DXR, and ispH in the 2-methyl-pentaerythritol 4-phosphate (MEP) pathway and GGPS were highly expressed and promoted the accumulation of terpenoids, especially diterpenoids. In winter Chinese cedar (WCC) needles, 9 genes (HCT, CHS, CHI, F3H, F3'H, F3'5'H, FLS, DFR, and LAR) involved in flavonoid biosynthesis were highly expressed and promoted flavonoid accumulation. This study broadens our understanding of the metabolic and transcriptomic changes in C. fortunei needles caused by seasonal changes and provides a reference regarding the adaptive mechanisms of C. fortunei and the extraction of its metabolites.

Dissecting the relationship between plasma and tissue metabolome in a cohort of women with obesity: Analysis of subcutaneous and visceral adipose, muscle, and liver

Untargeted metabolomics of blood samples has become widely applied to study metabolic alterations underpinning disease and to identify biomarkers. However, understanding the relevance of a blood metabolite marker can be challenging if it is unknown whether it reflects the concentration in relevant tissues. To explore this field, metabolomic and lipidomic profiles of plasma, four sites of adipose tissues (ATs) from peripheral or central depot, two sites of muscle tissue, and liver tissue from a group of nondiabetic women with obesity who were scheduled to undergo bariatric surgery (n = 21) or other upper GI surgery (n = 5), were measured by liquid chromatography coupled with mass spectrometry. Relationships between plasma and tissue profiles were examined using Pearson correlation analysis subject to Benjamini-Hochberg correction. Plasma metabolites and lipids showed the highest number of significantly positive correlations with their corresponding concentrations in liver tissue, including lipid species of ceramide, mono- and di-hexosylceramide, sphingomyelin, phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysophosphatidylethanolamine, dimethyl phosphatidylethanolamine, ether-linked PC, ether-linked PE, free fatty acid, cholesteryl ester, diacylglycerol and triacylglycerol, and polar metabolites linked to several metabolic functions and gut microbial metabolism. Plasma also showed significantly positive correlations with muscle for several phospholipid species and polar metabolites linked to metabolic functions and gut microbial metabolism, and with AT for several triacylglycerol species. In conclusion, plasma metabolomic and lipidomic profiles were reflective more of the liver profile than any of the muscle or AT sites examined in the present study. Our findings highlighted the importance of taking into consideration the metabolomic relationship of various tissues with plasma when postulating plasma metabolites marker to underlying mechanisms occurring in a specific tissue.

A Comparison of Solvent-Based Extraction Methods to Assess the Central Carbon Metabolites in Mouse Bone and Muscle

The identification of endogenous metabolites has great potential for understanding the underlying tissue processes occurring in either a homeostatic or a diseased state. The application of gas chromatography-mass spectrometry (GC-MS)-based metabolomics on musculoskeletal tissue samples has gained traction. However, limited comparison studies exist evaluating the sensitivity, reproducibility, and robustness of the various existing extraction protocols for musculoskeletal tissues. Here, we evaluated polar metabolite extraction from bone and muscle of mouse origin. The extraction methods compared were (1) modified Bligh–Dyer (mBD), (2) low chloroform (CHCl₃)-modified Bligh–Dyer (mBD-low), and (3) modified Matyash (mMat). In particular, the central carbon metabolites (CCM) appear to be relevant for musculoskeletal regeneration, given their role in energy metabolism. However, the sensitivity, reproducibility, and robustness of these methods for detecting targeted polar CCM remains unknown. Overall, the extraction of metabolites using the mBD, mBD-low, and mMat methods appears sufficiently robust and reproducible for bone, with the mBD method slightly bettering the mBD-low and mMat methods. Furthermore, mBD, mBD-low, and mMat were sufficiently sensitive in detecting polar metabolites extracted from mouse muscle; however, they lacked repeatability. This study highlights the need for a re-thinking, towards a tissue-specific optimization of methods for metabolite extractions, ensuring sufficient sensitivity, repeatability, and robustness.

Metabolite features and oxidative response in kidney of red crucian carp (Carassius auratus red var) after Aeromonas hydrophila challenge

Aeromonas hydrophila can threaten the survival of freshwater fish. In this study, A. hydrophila challenge could induce tissue damage, promote antioxidant imbalance as well as alter the transcript levels of oxidative stress indicators, apoptotic genes and metabolic enzyme genes in kidney of red crucian carp (RCC). Metabolomics analysis revealed that A. hydrophila challenge had a profound effect on amino acid metabolism and lipid metabolism. In addition, we further identified dipeptides, fatty acid derivatives, cortisol, choline and tetrahydrocortisone as crucial biomarkers in kidney of RCC subjected to A. hydrophila infection. These results highlighted the importance of metabolic strategy against bacterial infection.

Changes to gut amino acid transporters and microbiome associated with increased E/I ratio in Chd8+/- mouse model of ASD-like behavior

Autism spectrum disorder (ASD), a group of neurodevelopmental disorders characterized by social communication deficits and stereotyped behaviors, may be associated with changes to the gut microbiota. However, how gut commensal bacteria modulate brain function in ASD remains unclear. Here, we used chromodomain helicase DNA-binding protein 8 (CHD8) haploinsufficient mice as a model of ASD to elucidate the pathways through which the host and gut microbiota interact with each other. We found that increased levels of amino acid transporters in the intestines of the mouse model of ASD contribute to the high level of serum glutamine and the increased excitation/inhibition (E/I) ratio in the brain. In addition, elevated α-defensin levels in the haploinsufficient mice resulted in dysregulation of the gut microbiota characterized by a reduced abundance of Bacteroides. Furthermore, supplementation with Bacteroides uniformis improved the ASD-like behaviors and restored the E/I ratio in the brain by decreasing intestinal amino acid transport and the serum glutamine levels. Our study demonstrates associations between changes in the gut microbiota and amino acid transporters, and ASD-like behavioral and electrophysiology phenotypes, in a mouse model.

The gut microbiota has been shown to modulate the neural function via the microbiota-gut-brain axis. Here, the authors show that Bacteroides uniformis, a gut commensal bacterium, restores the ASD-like phenotypes by reducing intestinal amino acid transport in an ASD mouse model.

The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation

Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.

Metabolomics as a potential tool for the diagnosis of growth hormone deficiency (GHD): a review

Metabolomics uses several analytical tools to identify the chemical diversity of metabolites present in organisms. These metabolites are low molecular weight molecules (<1500 Da) classified as a final or intermediary product of metabolic processes. The application of this omics technology has become prominent in inferring physiological conditions through reporting on the phenotypic state; therefore, the introduction of metabolomics into clinical studies has been growing in recent years due to its efficiency in discriminating pathophysiological states. Regarding endocrine diseases, there is a great interest in verifying comprehensive and individualized physiological scenarios, in particular for growth hormone deficiency (GHD). The current GHD diagnostic tests are laborious and invasive and there is no exam with ideal reproducibility and sensitivity for diagnosis neither standard GH cut-off point. Therefore, this review was focussed on articles that applied metabolomics in the search for new biomarkers for GHD. The present work shows that the applications of metabolomics in GHD are still limited, since the little complementarily of analytical techniques, a low number of samples, GHD combined to other deficiencies, and idiopathic diagnosis shows a lack of progress. The results of the research are relevant and similar; however, their results do not provide an application for clinical practice due to the lack of multidisciplinary actions that would be needed to mediate the translation of the knowledge produced in the laboratory, if transferred to the medical setting.

Integrative Analysis of Metabolomics and Transcriptomics Data Identifies Prognostic Biomarkers Associated With Oral Squamous Cell Carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most malignant neoplasm in oral cancer. There is growing evidence that its progression involves altered metabolism. The current method of evaluating prognosis is very limited, and metabolomics may provide a new approach for quantitative evaluation. The aim of the study is to evaluate the use of metabolomics as prognostic markers for patients with OSCC.

Methods

An analytical platform, Ultra-Performance Liquid Chromatography-Quadrupole/Orbitrap High Resolution Mass Spectrometry (UHPLC-Q-Orbitrap HRMS), was used to acquire the serum fingerprinting profiles from a total of 103 patients of OSCC before and after the operation. In total, 103 OSCC patients were assigned to either a training set (n = 73) or a test set (n = 30). The potential biomarkers and the changes of serum metabolites were profiled and correlated with the clinicopathological parameters and survival of the patients by statistical analysis. To further verify our results, we linked them to gene expression using data from the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Results

In total, 14 differential metabolites and five disturbed pathways were identified between the preoperative group and postoperative group. Succinic acid change-low, hypoxanthine change-high tumor grade, and tumor stage indicated a trend towards improved recurrence-free survival (RFS), whether in a training set or a test set. In addition, succinic acid change-low, hypoxanthine change-high, and tumor grade provided the highest predictive accuracy of the patients with OSCC. KEGG enrichment analysis showed that the imbalance in the amino acid and purine metabolic pathway may affect the prognosis of OSCC.

Conclusions

The changes of metabolites before and after operation may be related to the prognosis of OSCC patients. UHPLC-Q-Orbitrap HRMS serum metabolomics analysis could be used to further stratify the prognosis of patients with OSCC. These results can better understand the mechanisms related to early recurrence and help develop more effective therapeutic targets.

Progress and Challenges in Quantifying Carbonyl-Metabolomic Phenomes with LC-MS/MS

Carbonyl-containing metabolites widely exist in biological samples and have important physiological functions. Thus, accurate and sensitive quantitative analysis of carbonyl-containing metabolites is crucial to provide insight into metabolic pathways as well as disease mechanisms. Although reversed phase liquid chromatography electrospray ionization mass spectrometry (RPLC-ESI-MS) is widely used due to the powerful separation capability of RPLC and high specificity and sensitivity of MS, but it is often challenging to directly analyze carbonyl-containing metabolites using RPLC-ESI-MS due to the poor ionization efficiency of neutral carbonyl groups in ESI. Modification of carbonyl-containing metabolites by a chemical derivatization strategy can overcome the obstacle of sensitivity; however, it is insufficient to achieve accurate quantification due to instrument drift and matrix effects. The emergence of stable isotope-coded derivatization (ICD) provides a good solution to the problems encountered above. Thus, LC-MS methods that utilize ICD have been applied in metabolomics including quantitative targeted analysis and untargeted profiling analysis. In addition, ICD makes multiplex or multichannel submetabolome analysis possible, which not only reduces instrument running time but also avoids the variation of MS response. In this review, representative derivatization reagents and typical applications in absolute quantification and submetabolome profiling are discussed to highlight the superiority of the ICD strategy for detection of carbonyl-containing metabolites.

The electromembrane extraction of pharmaceutical compounds from animal tissues

The reliable analysis of various compounds from tissue requires a tedious sample preparation. The sample pretreatment usually involves proper homogenization that facilitates extraction of target analytes, followed by an appropriate sample clean-up preventing matrix effects. Electromembrane extraction (EME) seems to have a significant potential to streamline the whole procedure. In this study, the applicability of EME for direct isolation of analytes from animal tissues was investigated for the first time. Extraction conditions were systematically optimized to isolate model analytes (daunorubicin and its metabolite daunorubicinol) from various tissues (myocardium, skeletal muscle and liver) coming from a pharmacokinetic study in rabbits. The relative recoveries of daunorubicin and its metabolite in all tissues, determined by the UHPLC-MS/MS method, were higher than 66 and 75%, respectively. Considerably low matrix effects (0 ± 8% with CV lower than 6%) and negligible content of phospholipids detected in EME extracts demonstrate the exceptional effectiveness of this microextraction approach in purification of tissue samples. The difference in the concentrations of the analytes determined after EME and reference liquid-liquid extraction of real tissue samples was lower than 12%, which further emphasized the trustworthiness of EME. Moreover, the considerable time reduction needed for sample treatment in case of EME must be emphasized. This study proved that EME is a simple, effective and reliable microextraction technique capable of direct extraction of the analytes from pulverized tissues without the need for an additional homogenization or purification step.

A Robust Method for Sample Preparation of Gastrointestinal Stromal Tumour for LC/MS Untargeted Metabolomics

Gastrointestinal stromal tumour has already been well explored at the genome level; however, little is known about metabolic processes occurring in the sarcoma. Sample preparation is a crucial step in untargeted metabolomics workflow, highly affecting the metabolome coverage and the quality of the results. In this study, four liquid-liquid extraction methods for the isolation of endogenous compounds from gastrointestinal stromal tumours were compared and evaluated. The protocols covered two-step or stepwise extraction with methyl-tert-butyl ether (MTBE) or dichloromethane. The extracts were subjected to LC-MS analysis by the application of reversed-phase and hydrophilic interaction liquid chromatography to enable the separation and detection of both polar and nonpolar analytes. The extraction methods were compared in terms of efficiency (total number of detected metabolites) and reproducibility. The method was based on the stepwise extraction with MTBE, methanol, and water proved to be the most reproducible, and thus, its robustness to fluctuations in experimental conditions was assessed employing Plackett–Burman design and hierarchical modelling. While most studied factors had no effect on the metabolite abundance, the highest coefficient value was observed for the volume of MTBE added during extraction. Herein, we demonstrate the application and the feasibility of the selected protocol for the analysis of gastrointestinal stromal tumour samples. The method selected could be considered as a reference for the best characterization of underlying molecular changes associated with complex tissue extracts of GIST.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Untargeted LC-MS Metabolomics for the Analysis of Micro-scaled Extracellular Metabolites from Hepatocytes

Metabolome analysis in micro physiological models is a challenge due to the low volume of the cell culture medium (CCM). Here, we report a LC-MS-based untargeted metabolomics protocol for the detection of hepatocyte extracellular metabolites from micro-scale samples of CCM. Using a single LC-MS method we have detected 57 metabolites of which 27 showed >2-fold shifts after 72-hour incubation. We demonstrate that micro-scale CCM samples can be used for modelling micro-physiological temporal dynamics in metabolite intensities.

Accurate Lipid Quantification of Tissue Homogenates Requires Suitable Sample Concentration, Solvent Composition, and Homogenization Procedure-A Case Study in Murine Liver

Lipidomics aim to quantify lipid species in all kinds of samples, including tissues. To subject a fixed amount of sample to various workflows, tissue homogenates were frequently prepared at defined concentrations in water or by addition of organic solvents. Here, we investigated this first step of tissue lipidomics by quantitative flow injection analysis coupled to Fourier-Transform mass spectrometry (FTMS). The influence of sample concentration, solvent composition, and homogenization procedure on the recovery of lipids was studied in murine liver. Liver homogenates were prepared either by grinding tissue in liquid nitrogen or by bead-based homogenization. Ground samples were dissolved at different concentrations in water, methanol, and water/methanol = 1/1 (v/v). Here, lipid recovery depends on solvent composition and sample concentration. The recovery of nonpolar lipid classes, including triglycerides and cholesteryl ester, was decreased in methanolic homogenates. In contrast, due to superior dispersion of precipitates, bead-based homogenization resulted in efficient lipid recovery independent of the solvent composition. However, lipid distribution within samples, i.e., lipid content of supernatant and pellet following centrifugation, was altered substantially by solvent composition. In conclusion, accurate lipid quantification of tissue homogenates requires evaluation of solvent composition, sample concentration, as well as the homogenization method to guarantee efficient lipid recovery. Due to a potential loss of lipids, removal of precipitates by centrifugation prior to lipid extraction should be avoided.

Discovery of Spoilage Markers for Chicken Eggs Using Liquid Chromatography-High Resolution Mass Spectrometry-Based Untargeted and Targeted Foodomics

The current approaches remain insufficient for measuring chicken egg spoilage or present analytical limitations. This study aimed to complement the existing analyses and identify novel markers using liquid chromatography-high resolution mass spectrometry-based foodomics strategies. In the discovery set, comparative untargeted metabolomics was utilized to identify marker candidates in microbially inoculated chicken eggs. Markers were annotated by spectral matching with authentic standards, experimental libraries, or in silico fragmentation. In the validation set, targeted metabolomics was employed to verify the markers in stored chicken eggs from five farms. Statistical differences at a p-value < 0.001 revealed increases in lactic and 3-hydroxybutyric acids and decreases in phosphocholine, LPE(O-18:1), LPC(16:0), and LPC(18:0) in stored eggs. Receiver operating characteristic curve analysis of the six combined markers yielded an AUC of 0.956 and a sensitivity and specificity of ∼90%. Four phospholipids were highlighted as a novel class of spoilage markers. Our findings may contribute to further industrial implementation, benefiting the quality assurance and food safety of poultry egg production.

Approaches in metabolomics for regulatory toxicology applications

Innovative methodological approaches are needed to conduct human health and environmental risk assessments on a growing number of marketed chemicals. Metabolomics is progressively proving its value as an efficient strategy to perform toxicological evaluations of new and existing substances, and it will likely become a key tool to accelerate chemical risk assessments. However, additional guidance with widely accepted and harmonized procedures is needed before metabolomics can be routinely incorporated in decision-making for regulatory purposes. The aim of this review is to provide an overview of metabolomic strategies that have been successfully employed in toxicity assessment as well as the most promising workflows in a regulatory context. First, we provide a general view of the different steps of regulatory toxicology-oriented metabolomics. Emphasis is put on three key elements: robustness of experimental design, choice of analytical platform, and use of adapted data treatment tools. Then, examples in which metabolomics supported regulatory toxicology outputs in different scenarios are reviewed, including chemical grouping, elucidation of mechanisms of toxicity, and determination of points of departure. The overall intention is to provide insights into why and how to plan and conduct metabolomic studies for regulatory toxicology purposes.

Estrogen Deprivation and Myocardial Infarction: Role of Aerobic Exercise Training, Inflammation and Metabolomics

In general, postmenopausal women present higher mortality, and worse prognosis after myocardial infarction (MI) compared to men, due to estrogen deficiency. After MI, cardiovascular alterations occur such as the autonomic imbalance and the pro-inflammatory cytokines increase. In this sense, therapies that aim to minimize deleterious effects caused by myocardial ischemia are important. Aerobic training has been proposed as a promising intervention in the prevention of cardiovascular diseases. On the other hand, some studies have attempted to identify potential biomarkers for cardiovascular diseases or specifically for MI. For this purpose, metabolomics has been used as a tool in the discovery of cardiovascular biomarkers. Therefore, the objective of this work is to discuss the changes involved in ovariectomy, myocardial infarction, and aerobic training, with emphasis on inflammation and metabolism.

The Role of Serum Metabolomics in Distinguishing Chronic Rhinosinusitis With Nasal Polyp Phenotypes

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by different clinical features and treatment responsiveness. This study aimed to compare the serum metabolomics profiles between eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (neCRSwNP) and healthy controls (HC) and explore objective biomarkers for distinguishing eCRSwNP before surgery. Methods: Serum samples were collected from 33 neCRSwNP patients, 37 eCRSwNP patients, and 29 HC. Serum metabolomics profiles were investigated by ultra-high-performance liquid chromatography-mass spectrometry. Results: The analysis results revealed that neCRSwNP, eCRSwNP, and HC exhibited distinctive metabolite signatures. In addition, eCRSwNP could be distinguished from neCRSwNP referring to their serum metabolic profiles, and the top ten different metabolites were citrulline, choline, linoleic acid, adenosine, glycocholic acid, L-serine, triethanolamine, 4-guanidinobutyric acid, methylmalonic acid, and L-methionine, which were related to several most important pathways including arginine and proline metabolism; glycine, serine, and threonine metabolism; linoleic acid metabolism; and purine metabolism. Among these distinctive metabolites, citrulline, linoleic acid, adenosine, and 4-guanidinobutyric acid showed good predictabilities, and the serum levels of citrulline, linoleic acid, and adenosine were significantly correlated with tissue eosinophil (T-EOS) percentage and T-EOS count. Conclusion: eCRSwNP patients exhibited discriminative serum metabolic signatures in comparison with neCRSwNP patients and HC. These results suggested that metabolomics profiles contributed to understanding the pathophysiological mechanisms of CRSwNP and distinguishing its phenotypes.

Assessment of solid phase microextraction as a sample preparation tool for untargeted analysis of brain tissue using liquid chromatography-mass spectrometry

This work presents an evaluation of solid-phase microextraction (SPME) SPME in combination with liquid chromatography-high resolution mass spectrometry (LC-HRMS) as an analytical approach for untargeted brain analysis. The study included a characterization of the metabolite coverage provided by C18, mixed-mode (MM, with benzene sulfonic acid and C18 functionalities), and hydrophilic lipophilic balanced (HLB) particles as sorbents in SPME coatings after extraction from cow brain homogenate at static conditions. The effects of desorption solvent, extraction time, and chromatographic modes on the metabolite features detected were investigated. Method precision and absolute matrix effects were also assessed. Among the main findings of this work, it was observed that all three tested coating chemistries were able to provide comparable brain tissue information. HLB provided higher responses for polar metabolites; however, as these fibers were prepared in-house, higher inter-fiber relative standard deviations were also observed. C18 and HLB coatings offered similar responses with respect to lipid-related features, whereas MM and C18 provided the best results in terms of method precision. Our results also showed that the use of methanol is essential for effective desorption of non-polar metabolites. Using a reversed-phase chromatographic method, an average of 800 and 1200 brain metabolite features detected in positive and negative modes, respectively, met inter-fibre RSD values below 30% (n=4) after removal of fibre and solvent artefacts from the associated datasets. For features detected using a lipidomics method, a total of 900 and 1800 features detected using C18 fibers in positive and negative mode, respectively, met the same criteria. In terms of absolute matrix effects, the majority of the model metabolites tested showed values between 80 and 120%, which are within the acceptable range. Overall, the findings of this work lay the foundation for further optimization of parameters for SPME-LC-HRMS methods suitable for in vivo and ex vivo brain (and other tissue) untargeted studies, and support the applicability of this approach for non-destructive tissue metabolomics.

Metabolic landscape of oral squamous cell carcinoma

BACKGROUND

Head and neck cancers are the seventh most common type of cancer worldwide, with almost half of the cases affecting the oral cavity. Oral squamous cell carcinoma (OSCC) is the most common form of oral cancer, showing poor prognosis and high mortality. OSCC molecular pathogenesis is complex, resulting from a wide range of events that involve the interplay between genetic mutations and altered levels of transcripts, proteins, and metabolites. Metabolomics is a recently developed sub-area of omics sciences focused on the comprehensive analysis of small molecules involved in several biological pathways by high throughput technologies.

AIM OF REVIEW

This review summarizes and evaluates studies focused on the metabolomics analysis of OSCC and oral premalignant disorders to better interpret the complex process of oral carcinogenesis. Additionally, the metabolic biomarkers signatures identified so far are also included. Moreover, we discuss the limitations of these studies and make suggestions for future investigations.

KEY SCIENTIFIC CONCEPTS

Although many questions about the metabolic features of OSCC have already been answered in metabolomic studies, further validation and optimization are still required to translate these findings into clinical applications.

Comparison of Metabolomic Profiles of Organs in Mice of Different Strains Based on SPME-LC-HRMS

Given that the extent to which genetics alters the metabolomic profile of tissues is still poorly understood, the current study aimed to characterize and investigate the metabolite profiles of brain, liver, kidney and skeletal muscle of two common mouse inbred strains (BALB/c, C57BL/6) and one outbred stock (CD1) for strain-specific differences. Male mice (n = 15) at the age of 12 weeks were used: BALB/c (n = 5), C57BL/6 (n = 5) and CD1 (n = 5). Solid phase microextraction (SPME) was applied for the extraction of analytes from the tissues. SPME fibers (approximately 0.2 mm in diameter) coated with a biocompatible sorbent (4 mm length of hydrophilic-lipophilic balanced particles) were inserted into each organ immediately after euthanasia. Samples were analyzed using liquid chromatography coupled to a Q-Exactive Focus Orbitrap mass spectrometer. Distinct interstrain differences in the metabolomic patterns of brain and liver tissue were revealed. The metabolome of kidney and muscle tissue in BALB/c mice differed greatly from C57BL/6 and CD1 strains. The main compounds differentiating all the targeted organs were alpha-amino acids, purine nucleotides and fatty acid esters. The results of the study indicate that the baseline metabolome of organs, as well as different metabolic pathways, vary widely among general-purpose models of laboratory mice commonly used in biomedical research.

Brain metabolomics study for the protective effects of Rhodiola crenulata extract on Alzheimer's disease by HPLC coupled with Fourier transform-ion cyclotron resonance mass spectrometry

In order to investigate the protective effects of Rhodiola crenulata extract on Alzheimer's disease, a brain metabolomics study in rats was conducted by high performance liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry. Rat model was constructed by bilateral hippocampal injection of amyloid-β peptide and immunohistochemistry was performed to evaluate the pharmacological effect of Rhodiola crenulata extract. Multivariate statistical analysis was used to discover potential biomarkers in rat brain and related metabolic pathways analysis was conducted to elucidate the action mechanism of Rhodiola crenulata extract. As a result, a total of 19 metabolites contributing to Alzheimer's disease progress were identified and nine of them were restored to the normal levels after drug administration. Pathway analysis revealed that the protective effects of Rhodiola crenulata extract are related to the regulation of glutathione metabolism and arachidonic acid metabolism in rat brain. In conclusion, this work demonstrates that the developed metabolomics method is useful to investigate the protective effects of Rhodiola crenulata extract against Alzheimer's disease. These outcomes may further provide reliable evidence to illuminate the intervention mechanism of other traditional Chinese medicines on Alzheimer's disease.

Non-Targeted Mass Isotopolome Analysis Using Stable Isotope Patterns to Identify Metabolic Changes

Gas chromatography coupled with mass spectrometry can provide an extensive overview of the metabolic state of a biological system. Analysis of raw mass spectrometry data requires powerful data processing software to generate interpretable results. Here we describe a data processing workflow to generate metabolite levels, mass isotopomer distribution, similarity and variability analysis of metabolites in a nontargeted manner, using stable isotope labeling. Using our data analysis software, no bioinformatic or programming background is needed to generate results from raw mass spectrometry data.

In Situ Study of Metabolic Response of Arabidopsis thaliana Leaves to Salt Stress by Neutral Desorption-Extractive Electrospray Ionization Mass Spectrometry

Salt stress is one of the most common factors limiting plant cultivation. In this study, metabolic responses to salt stress in Arabidopsis thaliana (A. thaliana) leaves were analyzed in situ by neutral desorption-extractive electrospray ionization mass spectrometry (ND-EESI-MS) without any sample pretreatment. Metabolic changes of A. thaliana leaves were observed in response to salt stress conditions, including the levels of serine, glutamic acid, arginine, cinnamic acid, ferulic acid, caffeic acid, protocatechuic acid, epicatechin, morin, myricetin, apigravin, and β-cotonefuran. The content of serine increased under 50, 100, and 200 mM NaCl salt stress, reaching the highest level at 200 mM NaCl, but decreased under the maximum concentration of 300 mM NaCl. A similar phenomenon was observed for arginine, glutamic acid, cinnamic acid, caffeic acid, ferulic acid, and epicatechin, respectively, involved in the metabolic pathway of shikimate-phenylpropanoid. Both principal component analysis (PCA) and partial least-squares discrimination analysis (PLS-DA) showed that the salt stress treatment groups of the higher concentrations (200 and 300 mM) could be well distinguished from those of the lower concentrations (50 and 100 mM) and the control. Marker metabolites, like m/z 261 (apigravin) and m/z 305 (β-cotonefuran), were assistantly selected from the fingerprints by variable importance for the projection (VIP). Our results indicated the potential of the ND-EESI-MS method for the rapid recognition of metabolic conditions in plant leaves under salt stress.

Metabolomics strategies to discover new biomarkers associated to severe allergic phenotypes

In the last decades have emerged new technological platforms that allow evaluation of genes, transcripts, proteins, or metabolites of a living being, so-called omics sciences. More importantly, new technics for their integration have provided access to a complete set of information of the current conditions and features of a specific biological sample in a precise moment. Thus, omic sciences are now considered an essential tool for patient stratification in base to their severity, to understand disease progression and to identify new biomarkers. Severe patients, that are out of control, provide an excellent model to understand disease evolution and to identify new intervention and biomarkers strategies. Here we discuss the use of metabolomics to understand severity in allergic diseases in a strategy that opens new insights as well as identify new biological systems relevant for allergy progression. Metabolomics strategies are based in parallel evaluation of different allergy severity models by mean of untargeted analysis that allows the identification of potential biomarkers. Overlapping of different biomarkers in multiple models, provides information of general as well as specific biological systems involved in each model. Later a selected panel of biomarkers will be used in a target method to explore the diagnosis potential to stratify allergic patients.

Assessment of gut microbiota fecal metabolites by chromatographic targeted approaches

Gut microbiota, the specific microbial community of the gastrointestinal tract, by means of the production of microbial metabolites provides the host with several functions affecting metabolic and immunological homeostasis. Insights into the intricate relationships between gut microbiota and the host require not only the understanding of its structure and function but also the measurement of effector molecules acting along the gut microbiota axis. This article reviews the literature on targeted chromatographic approaches in analysis of gut microbiota specific metabolites in feces as the most accessible biological matrix which can directly probe the connection between intestinal bacteria and the (patho)physiology of the holobiont. Together with a discussion on sample collection and preparation, the chromatographic methods targeted to determination of some classes of microbiota-derived metabolites (e.g., short-chain fatty acids, bile acids, low molecular masses amines and polyamines, vitamins, neurotransmitters and related compounds) are discussed and their main characteristics, summarized in Tables.

Comparison of liquid chromatography-mass spectrometry and direct infusion microchip electrospray ionization mass spectrometry in global metabolomics of cell samples

In this study, the feasibility of direct infusion electrospray ionization microchip mass spectrometry (chip-MS) was compared to the commonly used liquid chromatography-mass spectrometry (LC-MS) in non-targeted metabolomics analysis of human foreskin fibroblasts (HFF) and human induced pluripotent stem cells (hiPSC) reprogrammed from HFF. The total number of the detected features with chip-MS and LC-MS were 619 and 1959, respectively. Approximately 25% of detected features showed statistically significant changes between the cell lines with both analytical methods. The results show that chip-MS is a rapid and simple method that allows high sample throughput from small sample volumes and can detect the main metabolites and classify cells based on their metabolic profiles. However, the selectivity of chip-MS is limited compared to LC-MS and chip-MS may suffer from ion suppression.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

Tissue-Specific Sample Dilution: An Important Parameter to Optimise Prior to Untargeted LC-MS Metabolomics

When developing a sample preparation protocol for LC–MS untargeted metabolomics of a new sample matrix unfamiliar to the laboratory, selection of a suitable injection concentration is rarely described. Here we developed a simple workflow to address this issue prior to untargeted LC–MS metabolomics using pig adipose tissue and liver tissue. Bi-phasic extraction was performed to enable simultaneous optimisation of parameters for analysis of both lipids and polar extracts. A series of diluted pooled samples were analysed by LC–MS and used to evaluate signal linearity. Suitable injected concentrations were determined based on both the number of reproducible features and linear features. With our laboratory settings, the optimum concentrations of tissue mass to reconstitution solvent of liver and adipose tissue lipid fractions were found to be 125 mg/mL and 7.81 mg/mL respectively, producing 2811 (ESI+) and 4326 (ESI−) linear features from liver, 698 (ESI+) and 498 (ESI−) linear features from adipose tissue. For analysis of the polar fraction of both tissues, 250 mg/mL was suitable, producing 403 (ESI+) and 235 (ESI−) linear features from liver, 114 (ESI+) and 108 (ESI−) linear features from adipose tissue. Incorrect reconstitution volumes resulted in either severe overloading or poor linearity in our lipid data, while too dilute polar fractions resulted in a low number of reproducible features (<50) compared to hundreds of reproducible features from the optimum concentration used. Our study highlights on multiple matrices and multiple extract and chromatography types, the critical importance of determining a suitable injected concentration prior to untargeted LC–MS metabolomics, with the described workflow applicable to any matrix and LC–MS system.