Liquid chromatography–high-resolution mass spectrometry-based cell metabolomics: Experimental design, recommendations, and applications

Ultra-High-Performance Liquid Chromatography-High-Definition Mass Spectrometry-Based Metabolomics to Reveal the Potential Anti-Arthritic Effects of Illicium verum in Cultured Fibroblast-like Synoviocytes Derived from Rheumatoid Arthritis

BACKGROUND

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. The fruits of Illicium verum, which is a medicinal and edible resource, have been shown to have anti-inflammatory properties.

METHODS

In this study, we investigated the effects of I. verum extracts (IVEs) on human RA fibroblasts-like synoviocytes (RA-FLS) by using a sensitive and selective ultra-high-performance liquid chromatography with high-definition mass spectrometry (UPLC-HDMS) method. We subsequently analyzed the metabolites produced after the incubation of cultured RA-FLS with IVEs.

RESULTS

IVEs inhibited the proliferation and suppressed the migration of RA-FLS, and reduced the levels of inflammatory factors including TNF-α and IL-6. Twenty differential metabolites responsible for the effects of IVEs were screened and annotated based on the UPLC-HDMS data by using a cell metabolomics approach.

DISCUSSION

Our findings suggest that treating RA-FLS with IVEs can regulate lipid and amino acid metabolism, indicating that this extract has the potential to modify the metabolic pathways that cause inflammation in RA.

CONCLUSIONS

This might lead to novel therapeutic strategies for managing patients with RA.

Rapid Screening of Biomarkers in KYSE-150 Cells Exposed to Polycyclic Aromatic Hydrocarbons via Inkjet Printing Single-Cell Mass Spectrometry

Single-cell analysis by mass spectrometry (MS) is emerging as a powerful tool that not only contributes to cellular heterogeneity but also offers an unprecedented opportunity to predict pathology onset and facilitates novel biomarker discovery. However, the development of single-cell MS analysis techniques with a focus on sample extraction, separation, and ionization methods for volume-limited samples and complexity of cellular samples are still a big challenge. In this study, we present a high-throughput approach to inkjet drop on demand printing single-cell MS for rapid screening of biomarkers of polycyclic aromatic hydrocarbon (PAH) exposure at the KYSE-150 cell, aiming to elucidate the pathogenesis of PAH-induced esophageal cancer. With an analytical bulk KYSE-150 cell throughput of up to 51 cells per minute, the method provides a new opportunity for simultaneous single-cell analysis of multiple biomarkers. We screened 930 characteristic ions from 3,683 detected peak signals and identified 91 distinctive molecules that exhibited significant differences under various concentrations of PAH exposure. These molecules have potential as clinical diagnostic biomarkers. Additionally, the current study identifies specific biomarkers that behave completely opposite in single-cell and multicell lipidomics as the concentration of PAH changes. These biomarkers potentially subdivide KYSE-150 cells into PAH-sensitive and PAH-insensitive types, providing a basis for revealing PAH toxicity and disease pathogenesis from the heterogeneity of cellular metabolism.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy

BACKGROUND

To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible.

RESULTS

We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples.

SIGNIFICANCE

The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Comparison of Three Widely Employed Extraction Methods for Metabolomic Analysis of Trypanosoma brucei

Trypanosoma brucei (Tb) is the causative agent of human African trypanosomiasis (HAT), also known as sleeping sickness, which can be fatal if left untreated. An understanding of the parasite's cellular metabolism is vital for the discovery of new antitrypanosomal drugs and for disease eradication. Metabolomics can be used to analyze numerous metabolic pathways described as essential to Tb. brucei but has some limitations linked to the metabolites' physicochemical properties and the extraction process. To develop an optimized method for extracting and analyzing Tb. brucei metabolites, we tested the three most commonly used extraction methods, analyzed the extracts by hydrophilic interaction liquid chromatography high-resolution mass spectrometry (HILIC LC-HRMS), and further evaluated the results using quantitative criteria including the number, intensity, reproducibility, and variability of features, as well as qualitative criteria such as the specific coverage of relevant metabolites. Here, we present the resulting protocols for untargeted metabolomic analysis of Tb. brucei using (HILIC LC-HRMS). © 2024 Wiley Periodicals LLC. Basic Protocol 1: Culture of Trypanosoma brucei brucei parasites Basic Protocol 2: Preparation of samples for metabolomic analysis of Trypanosoma brucei brucei Basic Protocol 3: LC-HRMS-based metabolomic data analysis of Trypanosoma brucei brucei.

Effects of Solvent Evaporation Methods and Short-Term Room Temperature Storage on High-Coverage Cellular Metabolome Analysis

Cellular metabolomics provides insights into the metabolic processes occurring within cells and can help researchers understand how these processes are regulated and how they relate to cellular function, health, and disease. In this technical note, we investigated the effects of solvent evaporation equipment and storage condition on high-coverage cellular metabolomics. We previously introduced a robust CIL LC-MS-based cellular metabolomics workflow that encompasses various steps, including cell harvest, metabolic quenching, cell lysis, metabolite extraction, differential chemical isotope labeling, and LC-MS analysis. This workflow has consistently served as the cornerstone of our collaborative research and service projects. As a core facility catering to users with diverse research needs and financial resources, we have encountered scenarios requiring short-term sample storage. For example, the need often arises to transport samples at room temperature from user sites to our core facility. Herein, we present a study in which we compared different solvent evaporation methods (specifically, the nitrogen blowdown evaporator, SpeedVac concentrator, and lyophilizer) and diverse storage conditions (including dried samples stored in a freezer, samples stored in a freezer with methanol, dried samples stored at room temperature, and samples stored at room temperature with methanol). Our findings indicate that the choice of solvent evaporation equipment did not significantly impact the cellular metabolome. However, we observed a noteworthy change in the metabolome after 7 days of storage when cells were stored with methanol, regardless of whether they were kept at -80 °C or room temperature, in contrast to cells that were dried and frozen. Importantly, we detected no significant alterations in cells that were dried and stored at room temperature. In conclusion, to ensure the production of high-quality CIL LC-MS metabolomics results, we strongly recommend that, in situations where low-temperature storage is not feasible, cell samples should be thoroughly dried before storage or shipment at room temperature.

Untargeted Metabolomic Analysis in Endolymphatic Sac Luminal Fluid from Patients with Meniere's Disease

Dysfunction of the endolymphatic sac (ES) is one of the etiologies of Meniere's disease (MD), the mechanism of which remains unclear. The aim of the present study was to explore the molecular pathological characteristics of ES during the development of MD. Metabolomic profiling of ES luminal fluid from patients with MD and patients with acoustic neuroma (AN) was performed. Diluted ES luminal fluid (ELF) samples were obtained from 10 patients who underwent endolymphatic duct blockage for the treatment of intractable MD and from 6 patients who underwent translabyrinthine surgery for AN. ELF analysis was performed using liquid chromatography-mass spectrometry before the raw data were normalized and subjected to subsequent statistical analysis by MetaboAnalyst. Using thresholds of P ≤ 0.05 and variable important in projection > 1, a total of 111 differential metabolites were screened in the ELF, including 52 metabolites in negative mode and 59 in positive mode. Furthermore, 15 differentially altered metabolites corresponding to 15 compound names were identified using a Student's t-test, including 7 significant increased metabolites and 8 significant decreased metabolites. Moreover, two differentially altered metabolites, hyaluronic acid (HA) and 4-hydroxynonenal (4-HNE), were validated to be upregulated in the epithelial lining of the ES, as well as in the subepithelial connective-tissue in patients with MD comparing with that in patients with AN. Among these differentially altered metabolites, an upregulated expression of HA detected in the ES lumen of the patients with MD was supposed to be associated with the increased endolymph in ES, while an increased level of 4-HNE found in the ELF of the patients with MD provided direct evidence to support that oxidative damage and inflammatory lesions underlie the mechanism of MD. Furthermore, citrate and ethylenediaminetetraacetic acid were detected to be decreased substantially in the ELF of the patients with MD, suggesting the elevated endolymphatic Ca2+ in the ears with chronic endolymphatic hydrops is likely to be associated with the reduction of these two chelators of Ca2+ in ES. The results in the present study indicate metabolomic analysis in the ELF of the patients with MD can potentially improve our understanding on the molecular pathophysiological mechanism in the ES during the development of MD.

A Proposed Methodology to Deal with the Impact of In Vitro Cellular Matrix on the Analytical Performances of a Targeted Metabolomic LC-HRMS Method

Performances of metabolomic methods have been widely studied on biological matrices such as serum, plasma, and urine; but much less on in vitro cell extracts. While the impact of cell culture and sample preparation on results are well-described, the specific effect of the in vitro cellular matrix on the analytical performance remains uncertain. The aim of the present work was to study the impact of this matrix on the analytical performance of an LC-HRMS metabolomic method. For this purpose, experiments were performed on total extracts from two cell lines (MDA-MB-231 and HepaRG) using different cell numbers. Matrix effects, carryover, linearity, and variability of the method were studied. Results showed that the performances of the method depend on the nature of the endogenous metabolite, the cell number, and the nature of the cell line. These three parameters should, therefore, be considered for the processing of experiments and the interpretation of results depending on whether the study focuses on a limited number of metabolites or aims to establish a metabolic signature.

Trypanosoma brucei: Metabolomics for analysis of cellular metabolism and drug discovery

BACKGROUND

Trypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite's cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.

AIM OF REVIEW

The present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Compared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei's metabolism.

Metabolic Footprinting of Microbial Systems Based on Comprehensive In Silico Predictions of MS/MS Relevant Data

Metabolic footprinting represents a holistic approach to gathering large-scale metabolomic information of a given biological system and is, therefore, a driving force for systems biology and bioprocess development. The ongoing development of automated cultivation platforms increases the need for a comprehensive and rapid profiling tool to cope with the cultivation throughput. In this study, we implemented a workflow to provide and select relevant metabolite information from a genome-scale model to automatically build an organism-specific comprehensive metabolome analysis method. Based on in-house literature and predicted metabolite information, the deduced metabolite set was distributed in stackable methods for a chromatography-free dilute and shoot flow-injection analysis multiple-reaction monitoring profiling approach. The workflow was used to create a method specific for Saccharomyces cerevisiae, covering 252 metabolites with 7 min/sample. The method was validated with a commercially available yeast metabolome standard, identifying up to 74.2% of the listed metabolites. As a first case study, three commercially available yeast extracts were screened with 118 metabolites passing quality control thresholds for statistical analysis, allowing to identify discriminating metabolites. The presented methodology provides metabolite screening in a time-optimised way by scaling analysis time to metabolite coverage and is open to other microbial systems simply starting from genome-scale model information.

Untargeted Metabolomics for the Diagnosis of Exocrine Pancreatic Insufficiency in Chronic Pancreatitis

Background and Objectives: The clinical manifestations and course of chronic pancreatitis (CP) are often nonspecific and variable, hampering diagnosis of the risk of exocrine pancreatic insufficiency (EPI). Development of new, reproducible, and non-invasive methods to diagnose EPI is therefore a major priority. The objective of this metabolomic study was to identify novel biomarkers associated with EPI. Materials and Methods: We analyzed 53 samples from patients with CP, 32 with and 21 without EPI, using an untargeted metabolomics workflow based on hydrophilic interaction chromatography coupled to high-resolution mass spectrometry. Principal component and partial least squares-discriminant analyses showed significant between-group differentiation, and univariate and multivariate analyses identified potential candidate metabolites that significantly differed between samples from CP patients with EPI and those without EPI. Results: Excellent results were obtained using a six-metabolic panel to diagnose the presence of EPI in CP patients (area under the ROC curve = 0.785). Conclusions: This study confirms the usefulness of metabolomics in this disease setting, allowing the identification of novel biomarkers to differentiate between the presence and absence of EPI in CP patients.

Exploiting targeted and untargeted approaches for the analysis of bacterial metabolites under altered growth conditions

In the host, pathogenic microorganisms have developed stress responses to cope with constantly changing environments. Stress responses are directly related to changes in several metabolomic pathways, which could hamper microorganisms' unequivocal identification. We evaluated the effect of various in vitro stress conditions (acidic, basic, oxidative, ethanolic, and saline conditions) on the metabolism of Staphylococcus aureus, Bacillus cereus, and Pseudomonas aeruginosa, which are common lung pathogens. The metabolite profiles of the bacteria were analyzed using liquid chromatography coupled to triple quadrupole and quadrupole time-of-flight mass spectrometry. The advantages of targeted and untargeted analysis combined with univariate and multivariate statistical analysis (principal component analysis, hierarchical cluster analysis, partial least square discriminant analysis, random forest) were combined to unequivocally identify bacterial species. In normal in vitro conditions, the targeted methodology, based on the analysis of primary metabolites, enabled the rapid and efficient discrimination of the three bacteria. In changing in vitro conditions and specifically in presence of the various stressors, the untargeted methodology proved to be more valuable for the global and accurate differentiation of the three bacteria, also considering the type of stress environment within each species. In addition, species-specific metabolites (i.e., fatty acids, polysaccharides, peptides, and nucleotide bases derivatives) were putatively identified. Good intra-day repeatability and inter-day repeatability (< 10% RSD and < 15% RSD, respectively) were obtained for the targeted and the untargeted methods. This untargeted approach highlights its importance in unusual (and less known) bacterial growth environments, being a powerful tool for infectious disease diagnosis, where the accurate classification of microorganisms is sought.

Exploratory Metabolomic Analysis Based on Reversed-Phase Liquid Chromatography-Mass Spectrometry to Study an In Vitro Model of Hypoxia-Induced Metabolic Alterations in HK-2 Cells

Oxygen deficiency in cells, tissues, and organs can not only prevent the proper development of biological functions but it can also lead to several diseases and disorders. In this sense, the kidney deserves special attention since hypoxia can be considered an important factor in the pathophysiology of both acute kidney injury and chronic kidney disease. To provide better knowledge to unveil the molecular mechanisms involved, new studies are necessary. In this sense, this work aims to study, for the first time, an in vitro model of hypoxia-induced metabolic alterations in human proximal tubular HK-2 cells because renal proximal tubules are particularly susceptible to hypoxia. Different groups of cells, cultivated under control and hypoxia conditions at 0.5, 5, 24, and 48 h, were investigated using untargeted metabolomic approaches based on reversed-phase liquid chromatography–mass spectrometry. Both intracellular and extracellular fluids were studied to obtain a large metabolite coverage. On the other hand, multivariate and univariate analyses were carried out to find the differences among the cell groups and to select the most relevant variables. The molecular features identified as affected metabolites were mainly amino acids and Amadori compounds. Insights about their biological relevance are also provided.

Cell metabolomics to study the function mechanism of Cyperus rotundus L. on triple-negative breast cancer cells

BACKGROUND

Triple-negative breast cancer (TNBC) is a kind of malignant tumor with higher recurrence and metastasis rate. According to historical records, the dry rhizomes Cyperus rotundus L. could be ground into powder and mixed with ginger juice and wine for external application for breast cancer. We studied the effect of the ethanol extract of Cyperus rotundus L. (EECR) on TNBC cells and found its' apoptosis-inducing effect with a dose-relationship. But the function mechanism of EECR on TNBC is still mysterious. Hence, the present research aimed to detect its function mechanism at the small molecule level through ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS/MS) metabolomics.

METHODS

The CCK-8 assay and the Annexin V-FITC/PI assay were applied to test the effect of EECR on MDA-MB-231 cells and MDA-MB 468 cells at various concentrations of 0, 200, 400, and 600 μg/ml. UPLC-Q-TOF-MS/MS based metabolomics was used between the control group and the EECR treatment groups. Multivariate statistical analysis was used to visualize the apoptosis-inducing action of EECR and filtrate significantly changed metabolites.

RESULTS

The apoptosis-inducing action was confirmed and forty-nine significantly changed metabolites (VIP > 1, p < 0.05, and FC > 1.2 or FC < 0.8) were identified after the interference of EECR. The level of significant differential metabolites between control group, middle dose group, and high dose group were compared and found that which supported the apoptosis-inducing action with dose-dependence.

CONCLUSION

By means of metabolism, we have detected the mechanism of EECR inducing apoptosis of TNBC cells at the level of small molecule metabolites and found that EECR impacted the energy metabolism of TNBC cells. In addition, we concluded that EECR induced apoptosis by breaking the balance between ATP-production and ATP-consumption: arresting the pathways of Carbohydrate metabolism such as Central carbon metabolism in cancer, aerobic glycolysis, and Amino sugar and nucleotide sugar metabolism, whereas accelerating the pathways of ATP-consumption including Amino Acids metabolism, Fatty acid metabolism, Riboflavin metabolism and Purine metabolism. Although further study is still needed, EECR has great potential in the clinical treatment of TNBC with fewer toxic and side effects.

Effects of methyl oleate and microparticle-enhanced cultivation on echinocandin B fermentation titer

Echinocandin B (ECB) is a key precursor of antifungal agent Anidulafungin, which has demonstrated clinical efficacy in patients with invasive candidiasis. In this study, the effects of microparticle-enhanced cultivation and methyl oleate on echinocandin B fermentation titer were investigated. The results showed that the titer was significantly influenced by the morphological type of mycelium, and mycelium pellet was beneficial to improve the titer of this secondary metabolism. First, different carbon sources were chosen for the fermentation, and methyl oleate achieved the highest echinocandin B titer of 2133 ± 50 mg/L, which was two times higher than that of the mannitol. The study further investigated the metabolic process of the fermentation, and the results showed that L-threonine concentration inside the cell could reach 275 mg/L at 168 h with methyl oleate, about 2.5 times higher than that of the mannitol. Therefore, L-threonine may be a key precursor of echinocandin B. In the end, a new method of adding microparticles for improving the mycelial morphology was used, and the addition of talcum powder (20 g/L, diameter of 45 µm) could make the maximum titer of echinocandin B reach 3148 ± 100 mg/L.

Effects of harvesting and extraction methods on metabolite recovery from adherently growing mammalian cells

With the wide application of cell metabolomics in many research areas, there is a need to develop an effective procedure for adherent mammalian cell metabolomics that allows for accurate determination of intracellular metabolite levels and easy comparison between multiple studies of a similar application. Here we aimed to compare the efficiencies of different cell harvesting methods and metabolite extraction methods in sample preparation procedures, and to provide a cell sample processing protocol which focuses on maximizing metabolite recovery ranging from polar to lipidic ones. A systematical evaluation of 4 cell harvesting methods and 4 extraction methods was conducted based on human hepatoma HepG2 cells. The impact of different methods on the recoveries of 11 different categories of metabolites was further investigated. The water disruption sample harvesting method provided superior performance to the other 3 harvesting methods, trypsinization, scraping in phosphate buffered saline, and direct solvent scraping, with respect to the recoveries of polar metabolites and lipids. Among the 4 extraction methods, the novel two-phase solvent system extraction method with both methyl tert-butyl ether (MTBE) and 75% 9 : 1 methanol : chloroform showed an absolute advantage with high extraction efficiency for global metabolomics. We showed a metabolite-specific impact of the harvesting method and extraction method on metabolite concentrations. The water disruption sample collection combined with novel two-phase solvent system extraction enabled simultaneous profiling of lipids and metabolites with mixed polarity for sample preparation. Our approach may open up new perspectives toward large-scale comprehensive metabolomic analyses of adherent mammalian cell samples.

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.

Untargeted liquid chromatography-mass spectrometry metabolomics to assess drug-induced cholestatic features in HepaRG® cells

Cholestasis is a liver disease associated with retention of bile in the liver, which leads to local hepatic inflammation and severe liver damage. In order to investigate the mode of action of drug-induced cholestasis, in vitro models have shown to be able to recapitulate important elements of this disease. In this study, we applied untargeted metabolomics to investigate the metabolic perturbances in HepaRG® cells exposed for 24 h and 72 h to bosentan, a cholestatic reference toxicant. Intracellular profiles were extracted and analysed with liquid chromatography and accurate-mass spectrometry. Metabolites of interest were selected using partial least-squares discriminant analysis and random forest classifier models. The observed metabolic patterns associated with cholestasis in vitro were complex. Acute (24 h) exposure revealed metabolites related to apoptosis, such as ceramide and triglyceride accumulation, in combination with phosphatidylethanolamine, choline and carnitine depletion. Metabolomic alterations during exposure to lower dosages and a prolonged exposure (72 h) included carnitine upregulation and changes in the polyamine metabolism. These metabolites were linked to changes in phospholipid metabolism, mitochondrial pathways and energy homeostasis. The metabolic changes confirmed the mitotoxic effects of bosentan and revealed the potential involvement of phospholipid metabolism as part of the mode of action of drug-induced cholestasis.

Dynamic 13C Labeling of Fast Turnover Metabolites for Analysis of Metabolic Fluxes and Metabolite Channeling

Dynamic or isotopically nonstationary ¹³C labeling experiments are a powerful tool not only for precise carbon flux quantification (e.g., metabolic flux analysis of photoautotrophic organisms) but also for the investigation of pathway bottlenecks, a cell's phenotype, and metabolite channeling. In general, isotopically nonstationary metabolic flux analysis requires three main components: (1) transient isotopic labeling experiments; (2) metabolite quenching and isotopomer analysis using LC-MS; (3) metabolic network construction and flux quantification. Labeling dynamics of key metabolites from ¹³C-pulse experiments allow flux estimation of key central pathways by solving ordinary differential equations to fit time-dependent isotopomer distribution data. Additionally, it is important to provide biomass requirements, carbon uptake rates, specific growth rates, and carbon excretion rates to properly and precisely balance the metabolic network. Labeling dynamics through cascade metabolites may also identify channeling phenomena in which metabolites are passed between enzymes without mixing with the bulk phase. In this chapter, we outline experimental protocols to probe metabolic pathways through dynamic labeling. We describe protocols for labeling experiments, metabolite quenching and extraction, LC-MS analysis, computational flux quantification, and metabolite channeling observations.

An untargeted metabolomic strategy based on liquid chromatography-mass spectrometry to study high glucose-induced changes in HK-2 cells

Diabetes mellitus is a major health concern nowadays. It is estimated that 40% of diabetics are affected by diabetic nephropathy, one of the complications derived from high glucose blood levels which can lead to chronic loss of kidney function. It is now clear that the renal proximal tubule plays a critical role in the progression of diabetic nephropathy but research focused on studying the molecular mechanisms involved is still needed. The aim of this work was to develop a liquid chromatography-mass spectrometry platform to carry out, for the first time, the untargeted metabolomic analysis of high glucose-induced changes in cultured human proximal tubular HK-2 cells. In order to find the metabolites which were affected by high glucose and to expand the metabolite coverage, intra- and extracellular fluid from HK-2 cells exposed to high glucose (25 mM), normal glucose (5.5 mM) or osmotic control (5.5 mM glucose +19.5 mM mannitol) were analyzed by two complementary chromatographic modes: hydrophilic interaction and reversed-phase liquid chromatography. Non-supervised principal components analysis showed a good separation among the three groups of samples. Statistically significant variables were chosen for further metabolite identification. Different metabolic pathways were affected mainly those derived from amino acidic, polyol, and nitrogenous bases metabolism.

Metabolomics profiling of steatosis progression in HepaRG® cells using sodium valproate

Non-alcoholic Fatty Liver Disease (NAFLD) is a frequently encountered Drug-Induced Liver Injury (DILI). Although this stage of the disease is reversible, it can lead to irreversible damage provoked by non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. Therefore, the assessment of NAFLD is a paramount objective in toxicological screenings of new drug candidates. In this study, a metabolomic fingerprint of NAFLD induced in HepaRG^® cells at four dosing schemes by a reference toxicant, sodium valproate (NaVPA), was obtained using liquid-liquid extraction followed by liquid chromatography and accurate mass-mass spectrometry (LC-AM/MS). The combination of a strict design of experiment with a robust detection method, applied on sodium valproate, validated the possibilities of untargeted metabolomics in hepatic toxicological research. Distinctive patterns between exposed and control cells were consistently observed, multivariate analyses selected up to 200 features of interest, revealing hallmark NAFLD-biomarkers, such as diacylglycerol and triglyceride accumulation and carnitine deficiency. Initial toxic responses show increased levels of S-adenosylmethionine and mono-acetylspermidine in combination with only a moderate increase in triglycerides. New specific markers of toxicity have been observed, such as spermidines, creatine, and acetylcholine. The described design of experiment provides a valuable metabolomics platform for mechanistic research of toxicological hazards and identified new markers for steatotic progression.

Best-Matched Internal Standard Normalization in Liquid Chromatography-Mass Spectrometry Metabolomics Applied to Environmental Samples

The goal of metabolomics is to measure the entire range of small organic molecules in biological samples. In liquid chromatography-mass spectrometry-based metabolomics, formidable analytical challenges remain in removing the nonbiological factors that affect chromatographic peak areas. These factors include sample matrix-induced ion suppression, chromatographic quality, and analytical drift. The combination of these factors is referred to as obscuring variation. Some metabolomics samples can exhibit intense obscuring variation due to matrix-induced ion suppression, rendering large amounts of data unreliable and difficult to interpret. Existing normalization techniques have limited applicability to these sample types. Here we present a data normalization method to minimize the effects of obscuring variation. We normalize peak areas using a batch-specific normalization process, which matches measured metabolites with isotope-labeled internal standards that behave similarly during the analysis. This method, called best-matched internal standard (B-MIS) normalization, can be applied to targeted or untargeted metabolomics data sets and yields relative concentrations. We evaluate and demonstrate the utility of B-MIS normalization using marine environmental samples and laboratory grown cultures of phytoplankton. In untargeted analyses, B-MIS normalization allowed for inclusion of mass features in downstream analyses that would have been considered unreliable without normalization due to obscuring variation. B-MIS normalization for targeted or untargeted metabolomics is freely available at https://github.com/IngallsLabUW/B-MIS-normalization .

Influence of direct and sequential extraction methodology on metabolic profiling

A systematic comparison was made of the detected metabolite profiles for two plant materials (black beans and soybeans) and a dietary supplement (black cohosh) extracted using sequential (hexane, ethyl acetate, and 50% aqueous methanol) and direct extraction with three solvent systems (80% aqueous methanol, methanol/chloroform/water (2.5:1:1, v/v/v) and water). Extracts were analyzed by LC-MS (without derivatization) and GC-FID (with BSTFA/TMCS derivatizations). For sequential extraction, HPLC-UV and BSTFA/TMCS-derivatized GC-FID detection were more responsive to the polar molecules with a rough distribution of 10%, 10%, and 80% of the total signals in hexane, ethyl acetate, and 50% aqueous methanol, respectively. With HPLC-MS detection, the distribution of signals was more balanced, roughly 40%, 30%, and 30% for the same extracts (hexane, ethyl acetate, and 50% aqueous methanol). For direct extraction, HPLC-UV and BSTFA/TMCS-derivatized 4GC-FID provided signals between 60% and 150% of the total sequential extracted signals. The overlap of signals for the 3 sequential extracts ranged from 1% to 3%. The overlap of the signals for direct extraction with the total for sequential extraction ranged from 15% to 98%. With HPLC-MS detection, signals varied from 30% to 40% of the total signals for sequential extraction. Multivariate analysis showed that the components for the sequential and direct extracts were statistically different. However, each extract, sequential or direct, allowed discrimination between the 3 plant materials.

Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches

Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2 .

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Global profiling of xenobiotics in human matrices in an untargeted mode is gaining attention for studying the environmental chemical space of the human exposome. Defined as the study of a comprehensive inclusion of environmental influences and associated biological responses, human exposome science is currently evolving out of the metabolomics science. In analogy to the latter, the development and applications of high resolution mass spectrometry (HRMS) has shown potential and promise to greatly expand our ability to capture the broad spectrum of environmental chemicals in exposome studies. HRMS can perform both untargeted and targeted analysis because of its capability of full- and/or tandem-mass spectrum acquisition at high mass accuracy with good sensitivity. The collected data from target, suspect and non-target screening can be used not only for the identification of environmental chemical contaminants in human matrices prospectively but also retrospectively. This review covers recent trends and advances in this field. We focus on advances and applications of HRMS in human biomonitoring studies, and data acquisition and mining. The acquired insights provide stepping stones to improve understanding of the human exposome by applying HRMS, and the challenges and prospects for future research.

Wildtype motoneurons, ALS-Linked SOD1 mutation and glutamate profoundly modify astrocyte metabolism and lactate shuttling

The selective degeneration of motoneuron that typifies amyotrophic lateral sclerosis (ALS) implicates non-cell-autonomous effects of astrocytes. However, mechanisms underlying astrocyte-mediated neurotoxicity remain largely unknown. According to the determinant role of astrocyte metabolism in supporting neuronal function, we propose to explore the metabolic status of astrocytes exposed to ALS-associated conditions. We found a significant metabolic dysregulation including purine, pyrimidine, lysine, and glycerophospholipid metabolism pathways in astrocytes expressing an ALS-causing mutated superoxide dismutase-1 (SOD1) when co-cultured with motoneurons. SOD1 astrocytes exposed to glutamate revealed a significant modification of the astrocyte metabolic fingerprint. More importantly, we observed that SOD1 mutation and glutamate impact the cellular shuttling of lactate between astrocytes and motoneurons with a decreased in extra- and intra-cellular lactate levels in astrocytes. Based on the emergent strategy of metabolomics, this work provides novel insight for understanding metabolic dysfunction of astrocytes in ALS conditions and opens the perspective of therapeutics targets through focusing on these metabolic pathways. GLIA 2017 GLIA 2017;65:592-605.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.

Untargeted metabolomics reveals a mild impact of remote ischemic conditioning on the plasma metabolome and α-hydroxybutyrate as a possible cardioprotective factor and biomarker of tissue ischemia

INTRODUCTION

Remote ischemic conditioning (RIC) is a maneuver by which short non-lethal ischemic events are applied on distant organs or limbs to reduce ischemia and reperfusion injuries caused by e.g. myocardial infarct. Although intensively investigated, the specific mechanism of this protective phenomenon remains incompletely understood and in particular, knowledge on the role of small metabolites is scarce.

OBJECTIVES

In this study, we aimed to study perturbations in the plasma metabolome following RIC and gain insight into metabolic changes by the intervention as well as to identify potential novel cardio-protective metabolites.

METHODS

Blood plasma samples from ten healthy males were collected prior to and after RIC and tested for bioactivity in a HL-1 based cellular model of ischemia-reperfusion damage. Following this, the plasma was analyzed using untargeted LC-qTOF-MS and regulated metabolites were identified using univariate and multivariate statistical analysis. Results were finally verified in a second plasma study from the same group of volunteers and by testing a metabolite ester in the HL-1 cell model.

RESULTS

The analysis revealed a moderate impact on the plasma metabolome following RIC. One metabolite, α-hydroxybutyrate (AHB) however, stood out as highly significantly upregulated after RIC. AHB might be a novel and more sensitive plasma-biomarker of transient tissue ischemia than lactate. Importantly, it was also found that a cell permeable AHB precursor protects cardiomyocytes from ischemia-reperfusion damage.

CONCLUSION

Untargeted metabolomics analysis of plasma following RIC has led to insight into metabolism during RIC and revealed a possible novel metabolite of relevance to ischemic-reperfusion damage.

Untargeted LC-HRMS-Based Metabolomics for Searching New Biomarkers of Pancreatic Ductal Adenocarcinoma: A Pilot Study

Pancreatic ductal adenocarcinoma is one of the most lethal tumors since it is usually detected at an advanced stage in which surgery and/or current chemotherapy have limited efficacy. The lack of sensitive and specific markers for diagnosis leads to a dismal prognosis. The purpose of this study is to identify metabolites in serum of pancreatic ductal adenocarcinoma patients that could be used as diagnostic biomarkers of this pathology. We used liquid chromatography-high-resolution mass spectrometry for a nontargeted metabolomics approach with serum samples from 28 individuals, including 16 patients with pancreatic ductal adenocarcinoma and 12 healthy controls. Multivariate statistical analysis, which included principal component analysis and partial least squares, revealed clear separation between the patient and control groups analyzed by liquid chromatography-high-resolution mass spectrometry using a nontargeted metabolomics approach. The metabolic analysis showed significantly lower levels of phospholipids in the serum from patients with pancreatic ductal adenocarcinoma compared with serum from controls. Our results suggest that the liquid chromatography-high-resolution mass spectrometry-based metabolomics approach provides a potent and promising tool for the diagnosis of pancreatic ductal adenocarcinoma patients using the specific metabolites identified as novel biomarkers that could be used for an earlier detection and treatment of these patients.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.