Next-generation capillary electrophoresis–mass spectrometry approaches in metabolomics

Chemo-Selective Single-Cell Metabolomics Reveals the Spatiotemporal Behavior of Exogenous Pollutants During Xenopus Laevis Embryogenesis

In-depth profiling of embryogenesis-associated endogenous and exogenous metabolic changes can reveal potential bio-effects resulting from human-made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio-transformation behavior of dynamic metabolites at single-cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero-sample-loss micro-biopsy-based mass spectrometric platform is presented for quantitative, chemo-selective, high-coverage, and minimal-destructive profiling of development-associated cis-diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis-diol metabolic signatures among oocytes, anterior and posterior part of tailbud-stage embryos; 2) halogenated cis-diols heavily accumulate at the posterior part of tailbud-stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio-transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

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.

Implication of metabolism in the polarization of tumor-associated-macrophages: the mass spectrometry-based point of view

Tumor-associated macrophages (TAMs) represent one of the main tumor-infiltrating immune cell types and are generally categorized into either of two functionally contrasting subtypes, namely classical activated M1 macrophages and alternatively activated M2 macrophages. TAMs showed different activation states that can be represent by the two extremes of the complex profile of macrophages biology, the M1-like phenotype (pro-inflammatory activity) and the M2-like phenotype (anti-inflammatory activity). Based on the tumor type, and grades, TAMs can acquire different functions and properties; usually, the M1-like phenotype is typical of early tumor stages and is associated to an anti-tumor activity, while M2-like phenotype has a pro-inflammatory activity and is related to a poor patients' prognosis. The classification of macrophages into M1/M2 groups based on well-defined stimuli does not model the infinitely more complex tissue milieu where macrophages (potentially of different origin) would be exposed to multiple signals in different sequential order. This review aims to summarize the recent mass spectrometry-based (MS-based) metabolomics findings about the modifications of metabolism in TAMs polarization in different tumors. The published data shows that MS-based metabolomics is a promising tool to help better understanding TAMs metabolic phenotypes, although it is still poorly applied for TAMs metabolism. The knowledge of key metabolic alterations in TAMs is an essential step for discovering TAMs polarization novel biomarkers and developing novel therapeutic approaches targeting TAM metabolism to repolarize TAMs towards their anti-tumor phenotype.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Machine learning-empowered cis-diol metabolic fingerprinting enables precise diagnosis of primary liver cancer

Cis-diol metabolic reprogramming evolves during primary liver cancer (PLC) initiation and progression. However, owing to the low concentrations and highly structural heterogeneity of cis-diols in vivo, severe interference from complex biofluids and limited profiling coverage of existing methods, in-depth profiling of cis-diol metabolites and linking their specific changes with PLC remain challenging. Besides, due to the low specificity of widely used protein biomarkers, accurate classification of PLC from hepatitis still represents an unmet need in clinical diagnostics. Herein, to high-coverage profile cis-diols and explore the translational potential of them as biomarkers, a machine learning-empowered boronate affinity extraction-solvent evaporation assisted enrichment-mass spectrometry (MLE-BESE-MS) was developed. A single analytical platform integrated with multiple complementary functions, including pH-controlled boronate affinity extraction, solvent evaporation-assisted enrichment and nanoelectrospray ionization-based cis-diol identification, was constructed, which significantly improved the metabolite coverage. Meanwhile, by virtue of machine learning (principal components analysis, orthogonal partial least-squares discrimination analysis and random forest), collected cis-diols were statistically screened to extract efficient features for precise PLC diagnosis, and the results outperform the routinely used protein biomarker-based methods both in sensitivity (87.5% vs. less than 70%) and specificity (85.7% vs. ca. 80%). This machine learning-empowered integrated MS platform advanced the targeted metabolic analysis for early cancer diagnosis, rendering great promise for clinical translation.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Research on Detection of Sterol Doping in Sports by Electrochemical Sensors: A Review

The use of doping by athletes to improve performance is prohibited. Therefore, doping testing is an important step to ensure fairness in sports. Doping is gradually metabolized in the body and is therefore difficult to detect immediately by a common method. At the same time, the emergence of new doping agents poses a challenge for highly sensitive detection. Electrochemical sensors are a fast, highly sensitive, and inexpensive analytical detection technology. It provides qualitative and quantitative determination of analytes by altering the electrochemical signal of the analyte or probe at the electrode. In this min-review, we summarized the different electrochemical sensing strategies for sterol doping detection. Some of the representative papers were interpreted in detail. In addition, we compare different sensing strategies.

Atrazine Exposure Induces Hepatic Metabolism Disorder in Male Adult Zebrafish

Atrazine (ATZ) is a herbicide used in agricultural production and has been detected in surface water due to its widespread use worldwide. This may pose a threat to the health of aquatic animals. To explore the ATZ−induced hepatic metabolism disorder, male zebrafish were exposed to 300 and 1000 μg/L ATZ for 21 days, respectively. The results revealed that ATZ exposure significantly reduced hepatic triglyceride (TG) levels, while significantly (p < 0.05) increased pyruvate (PYR) and total cholesterol (TC) levels. In addition, the liver sample from the 1000 μg/L ATZ−treated group was used for GC/MS metabolomic analysis. The principal component analysis (PCA) model showed significant separation of the 1000 μg/L ATZ group from the control group, indicating that ATZ exposure altered hepatic metabolism in male adult zebrafish. A total of 29 significantly (p < 0.05) different metabolites were observed and identified in the ATZ−treated group. Moreover, the most disturbed pathways by ATZ were the arginine and proline metabolic pathways, followed by the glutathione metabolic pathway. Three and two metabolites were significantly altered in the arginine and proline metabolic pathways and glutathione metabolic pathway, respectively. Based on these results, we suggested that ATZ was capable of altering liver metabolism in zebrafish and that its ecological risk to aquatic organisms cannot be ignored.

Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression

The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70–80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R² of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)–MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.

Tricky Isomers—The Evolution of Analytical Strategies to Characterize Plasmalogens and Plasmanyl Ether Lipids

Typically, glycerophospholipids are represented with two esterified fatty acids. However, by up to 20%, a significant proportion of this lipid class carries an ether-linked fatty alcohol side chain at the sn-1 position, generally referred to as ether lipids, which shape their specific physicochemical properties. Among those, plasmalogens represent a distinct subgroup characterized by an sn-1 vinyl-ether double bond. The total loss of ether lipids in severe peroxisomal defects such as rhizomelic chondrodysplasia punctata indicates their crucial contribution to diverse cellular functions. An aberrant ether lipid metabolism has also been reported in multifactorial conditions including Alzheimer’s disease. Understanding the underlying pathological implications is hampered by the still unclear exact functional spectrum of ether lipids, especially in regard to the differentiation between the individual contributions of plasmalogens (plasmenyl lipids) and their non-vinyl-ether lipid (plasmanyl) counterparts. A primary reason for this is that exact identification and quantification of plasmalogens and other ether lipids poses a challenging and usually labor-intensive task. Diverse analytical methods for the detection of plasmalogens have been developed. Liquid chromatography–tandem mass spectrometry is increasingly used to resolve complex lipid mixtures, and with optimized parameters and specialized fragmentation strategies, discrimination between ethers and plasmalogens is feasible. In this review, we recapitulate historic and current methodologies for the recognition and quantification of these important lipids and will discuss developments in this field that can contribute to the characterization of plasmalogens in high structural detail.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Applied capillary electrophoresis system affects screening for monoclonal gammopathy in serum: verification study of two eight-capillary systems

Capillary electrophoresis is a method with a long history of developments which enables monitoring of several pathological processes and has an irreplaceable role in screening for presence of M-protein. The aim of this study was to assess analytical performance of Sebia and Helena systems, as well as their screening efficiency for M-protein by identifying characteristic electrophoretic pattern abnormalities. The controls were analyzed in triplicate over a five-day period. Comparability testing was performed on 46 (Capillarys3Octa) and 49 (V8Nexus) serum samples with routinely used Capillarys2. Electropherograms (EPGs) were reviewed by two specialists independently to select samples for further processing by immunofixation. All precision test results met the eligibility criteria by Ricos et al. The correlation coefficients higher than 0.8 indicated excellent comparability although the results were slightly more comparable among the same manufacturer systems. There were no variations in observed abnormalities in EPGs when Capillarys systems were compared, but a disparity was detected in 11/49 EPGs on comparing Capillarys2 and V8Nexus. The cause of the detected difference could be in a different graphical presentation of the findings and in a lesser resolution of the applied buffer. The impression is that the V8Nexus system combined with the utilized buffer provides greater resolution in the alpha-1 and alpha-2 globulin fractions, but that it declines in the gamma globulin fraction. The precision and estimated accuracy criteria were met by both systems. Comparison results implied that capillary systems are not equally effective in M-protein screening, highlighting the necessity to include system screening efficiency in analytical evaluation.

Sensitivity enhancement in capillary electrophoresis and their applications for analyses of pharmaceutical and related biochemical substances

This review aims to illustrate sensitivity enhancement methods in capillary electrophoresis (CE) and their applications for pharmaceutical and related biochemical substance analyses. The first two parts of the article describe the introduction and principle of CE. The main part focuses on strategies for sensitivity improvement in CE including detector and capillary technologies and pre-concentration techniques. Applications of these techniques for pharmaceutical and biomedical substance analyses are surveyed during the years 2018-2021. This article is protected by copyright. All rights reserved.

Recent advances in microscale separation techniques for lipidome analysis

This review paper highlights the recent research on liquid-phase microscale separation techniques for lipidome analysis over the last 10 years, mainly focusing on capillary liquid chromatography (LC) and capillary electrophoresis (CE) coupled with mass spectrometry (MS). Lipids are one of the most important classes of biomolecules which are involved in the cell membrane, energy storage, signal transduction, and so on. Since lipids include a variety of hydrophobic compounds including numerous structural isomers, lipidomes are a challenging target in bioanalytical chemistry. MS is the key technology that comprehensively identifies lipids; however, separation techniques like LC and CE are necessary prior to MS detection in order to avoid ionization suppression and resolve structural isomers. Separation techniques using μm-scale columns, such as a fused silica capillary and microfluidic device, are effective at realizing high-resolution separation. Microscale separation usually employs a nL-scale flow, which is also compatible with nanoelectrospray ionization-MS that achieves high sensitivity. Owing to such analytical advantages, microscale separation techniques like capillary/microchip LC and CE have been employed for more than 100 lipidome studies. Such techniques are still being evolved and achieving further higher resolution and wider coverage of lipidomes. Therefore, microscale separation techniques are promising as the fundamental technology in next-generation lipidome analysis.

nanoCEasy: An Easy, Flexible, and Robust Nanoflow Sheath Liquid Capillary Electrophoresis-Mass Spectrometry Interface Based on 3D Printed Parts

Capillary electrophoresis-mass spectrometry (CE-MS) is a powerful tool in various fields including proteomics, metabolomics, and biopharmaceutical and environmental analysis. Nanoflow sheath liquid (SL) CE-MS interfaces provide sensitive ionization, required in these fields, but are still limited to a few research laboratories as handling is difficult and expertise is necessary. Here, we introduce nanoCEasy, a novel nanoflow SL interface based on 3D printed parts, including our previously reported two capillary approach. The customized plug-and-play design enables the introduction of capillaries and an emitter without any fittings in less than a minute. The transparency of the polymer enables visual inspection of the liquid flow inside the interface. Robust operation was systematically demonstrated regarding the electrospray voltage, the distance between the emitter and MS orifice, the distance between the separation capillary and emitter tip, and different individual emitters of the same type. For the first time, we evaluated the influence of high electroosmotic flow (EOF) separation conditions on a nanoflow SL interface. A high flow from the separation capillary can be outbalanced by increasing the electrospray voltage, leading to an overall increased electrospray flow, which enables stable operation under high-EOF conditions. Overall, the nanoCEasy interface allows easy, sensitive, and robust coupling of CE-MS. We aspire the use of this sensitive, easy-to-use interface in large-scale studies and by nonexperts.

Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites

Metabolomics studies rely on the availability of suitable analytical platforms to determine a vast collection of chemically diverse metabolites in complex biospecimens. Liquid chromatography-mass spectrometry operated under reversed-phase conditions is the most commonly used platform in metabolomics, which offers extensive coverage for nonpolar and moderately polar compounds. However, complementary techniques are required to obtain adequate separation of polar and ionic metabolites, which are involved in several fundamental metabolic pathways. This chapter focuses on the main mass-spectrometry-based analytical platforms used to determine polar and/or ionizable compounds in metabolomics (GC-MS, HILIC-MS, CE-MS, IPC-MS, and IC-MS). Rather than comprehensively describing recent applications related to GC-MS, HILIC-MS, and CE-MS, which have been covered in a regular basis in the literature, a brief discussion focused on basic principles, main strengths, limitations, as well as future trends is presented in this chapter, and only key applications with the purpose of illustrating important analytical aspects of each platform are highlighted. On the other hand, due to the relative novelty of IPC-MS and IC-MS in the metabolomics field, a thorough compilation of applications for these two techniques is presented here.

New insights into the conversion of electropherograms to the effective electrophoretic mobility scale

CE-MS is increasingly gaining momentum as an analytical tool in metabolomics, due to its ability to obtain information about the most polar elements in biological samples. This has been helped by improvements of robustness in peak identification by means of mobility-scale representations of the electropherograms (mobilograms). As a necessary step toward facilitating the use of CE-MS for untargeted metabolomics data, the authors previously developed and introduced ROMANCE, a software automating mobilogram generation for large untargeted datasets through a simple and self-contained user interface. Herein, we introduce a new version of ROMANCE including new features such as compatibility with other types of data (targeted MS data and 2D UV-Vis absorption-like electropherograms), and the much needed additional flexibility in the transformation parameters (including field ramping and the use of secondary markers), more measurement conditions (depending on detection and integration modes), and most importantly tackling the issue of quantitative peak conversion. First, we present a review of the current theoretical framework with regard to peak characterization, and we develop new formulas for multiple marker peak area corrections, for anticipating peak position precision, and for assessing peak shape distortion. Then, the new version of the software is presented and validated experimentally. We contrast the multiple marker mobility transformations with previous results, finding increased peak position precision, and finally we showcase an application to actual untargeted metabolomics data.

A Cross-Platform Metabolomics Comparison Identifies Serum Metabolite Signatures of Liver Fibrosis Progression in Chronic Hepatitis C Patients

Metabolomics offers new insights into disease mechanisms that is enhanced when adopting orthogonal instrumental platforms to expand metabolome coverage, while also reducing false discoveries by independent replication. Herein, we report the first inter-method comparison when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) and nuclear magnetic resonance (NMR) spectroscopy for characterizing the serum metabolome of patients with liver fibrosis in chronic hepatitis C virus (HCV) infection (n = 20) and non-HCV controls (n = 14). In this study, 60 and 30 serum metabolites were detected frequently (>75%) with good technical precision (median CV < 10%) from serum filtrate samples (n = 34) when using standardized protocols for MSI-CE-MS and NMR, respectively. Also, 20 serum metabolite concentrations were consistently measured by both methods over a 500-fold concentration range with an overall mean bias of 9.5% (n = 660). Multivariate and univariate statistical analyses independently confirmed that serum choline and histidine were consistently elevated (p < 0.05) in HCV patients with late-stage (F2-F4) as compared to early-stage (F0-F1) liver fibrosis. Overall, the ratio of serum choline to uric acid provided optimal differentiation of liver disease severity (AUC = 0.848, p = 0.00766) using a receiver operating characteristic curve, which was positively correlated with liver stiffness measurements by ultrasound imaging (r = 0.606, p = 0.0047). Moreover, serum 5-oxo-proline concentrations were higher in HCV patients as compared to non-HCV controls (F = 4.29, p = 0.0240) after adjustment for covariates (age, sex, BMI), indicative of elevated oxidative stress from glutathione depletion with the onset and progression of liver fibrosis. Both instrumental techniques enable rapid yet reliable quantification of serum metabolites in large-scale metabolomic studies with good overlap for biomarker replication. Advantages of MSI-CE-MS include greater metabolome coverage, lower operating costs, and smaller sample volume requirements, whereas NMR offers a robust platform supported by automated spectral and data processing software.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Ultra-high-performance liquid chromatography high-resolution mass spectrometry variants for metabolomics research

Ultra-high-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS) variants currently represent the best tools to tackle the challenges of complexity and lack of comprehensive coverage of the metabolome. UHPLC offers flexible and efficient separation coupled with high-sensitivity detection via HRMS, allowing for the detection and identification of a broad range of metabolites. Here we discuss current common strategies for UHPLC-HRMS-based metabolomics, with a focus on expanding metabolome coverage.

Capillary Electrophoresis-High Resolution Mass Spectrometry for Measuring In Vivo Arginine Isotope Incorporation in Alzheimer's Disease Mouse Models

Immune-based metabolic reprogramming of arginine utilization in the brain contributes to the neuronal pathology associated with Alzheimer's disease (AD). To enable our long-term goals of differentiation of AD mouse model genotypes, ages, and sexes based on activity of this pathway, we describe here the novel dosing (using uniformly labeled (¹³C₆¹⁵N₄) arginine) and analysis methods using capillary electrophoresis high-resolution accurate-mass mass spectrometry for isotope tracing of metabolic products of arginine. We developed a pseudoprimed infusion-dosing regimen, using repeated injections, to achieve a steady state of uniformly labeled arginine in 135-195 min post bolus dose. Incorporation of stable isotope labeled carbon and nitrogen from uniformly labeled arginine into a host of downstream metabolites was measured in vivo in mice using serially sampled dried blood spots from the tail. In addition to the dried blood spot time course samples, total isotope incorporation into arginine-related metabolites was measured in the whole brain and plasma after 285 min. Preliminary demonstration of the technique identified differences isotope incorporation in arginine metabolites between male and female mice in a mouse-model of sporadic Alzheimer's disease (APOE4/huNOS2). The technique described herein will permit arginine pathway activity differentiation between mouse genotypes, ages, sexes, or drug treatments in order to elucidate the contribution of this pathway to Alzheimer's disease.

Recent Advances in Trace Bioanalysis by Capillary Electrophoresis

Recently, single cell analysis is becoming more and more important to elucidate cellular heterogeneity. Except for nucleic acid that can be amplified by PCR, the required technical level for single cell analysis is extremely high and the appropriate design of sample preparation and a sensitive analytical system is necessary. Capillary/microchip electrophoresis (CE/MCE) can separate biomolecules in nL-scale solution with high resolution, and it is highly compatible with trace samples like a single cell. Coupled with highly sensitive detectors such as laser-induced fluorescence and nano-electrospray ionization-mass spectrometry, zmol level analytes can be detected. For further enhancing sensitivity, online sample preconcentration techniques can be employed. By integrating these high-sensitive techniques, single cell analysis of metabolites, proteins, and lipids have been achieved. This review paper highlights successful research on CE/MCE-based trace bioanalysis in recent 10 years. Firstly, an overview of basic knowledge on CE/MCE including sensitivity enhancement techniques is provided. Applications to trace bioanalysis are then introduced with discussion on current issues and future prospects.

Past, present, and future developments in enantioselective analysis using capillary electromigration techniques

Enantioseparation of chiral products has become increasingly important in a large diversity of academic and industrial applications. The separation of chiral compounds is inherently challenging and thus requires a suitable analytical technique that can achieve high resolution and sensitivity. In this context, CE has shown remarkable results so far. Chiral CE offers an orthogonal enantioselectivity and is typically considered less costly than chromatographic techniques, since only minute amounts of chiral selectors are needed. Several CE approaches have been developed for chiral analysis, including chiral EKC and chiral CEC. Enantioseparations by EKC benefit from the wide variety of possible pseudostationary phases that can be employed. Chiral CEC, on the other hand, combines chromatographic separation principles with the bulk fluid movement of CE, benefitting from reduced band broadening as compared to pressure-driven systems. Although UV detection is conventionally used for these approaches, MS can also be considered. CE-MS represents a promising alternative due to the increased sensitivity and selectivity, enabling the chiral analysis of complex samples. The potential contamination of the MS ion source in EKC-MS can be overcome using partial-filling and counter-migration techniques. However, chiral analysis using monolithic and open-tubular CEC-MS awaits additional method validation and a dedicated commercial interface. Further efforts in chiral CE are expected toward the improvement of existing techniques, the development of novel pseudostationary phases, and establishing the use of chiral ionic liquids, molecular imprinted polymers, and metal-organic frameworks. These developments will certainly foster the adoption of CE(-MS) as a well-established technique in routine chiral analysis.

Mass Spectrometry-based Metabolomics in Translational Research

Metabolomics is the systematic study of metabolite profiles of complex biological systems, and involves the systematic identification and quantification of metabolites. Metabolism is integrated with all biochemical reactions in biological systems; thus metabolite profiles provide collective information on biochemical processes induced by genetic or environmental perturbations. Transcriptomes or proteomes may not be functionally active and not always reflect phenotypic variations. The metabolome, however, consists of the biomolecules closest to the phenotype of living organisms, and is often called the molecular phenotype of biological systems. Thus, metabolome alterations can easily result in disease states, providing important clues to understand pathophysiological mechanisms contributing to various biomedical symptoms. The metabolome and metabolomics have been emphasized in translational research related to biomarker discovery, drug target discovery, drug responses, and disease mechanisms. This review describes the basic concepts, workflows, and applications of mass spectrometry-based metabolomics in translational research.

Capillary (Gel) Electrophoresis-Based Methods for Immunoglobulin (G) Glycosylation Analysis

The in-depth characterization of protein glycosylation has become indispensable in many research fields and in the biopharmaceutical industry. Especially knowledge about modulations in immunoglobulin G (IgG) N-glycosylation and their effect on immunity enabled a better understanding of human diseases and the development of new, more effective drugs for their treatment. This chapter provides a deeper insight into capillary (gel) electrophoresis-based (C(G)E) glycan analysis, addressing its impressive performance and possibilities, its great potential regarding real high-throughput for large cohort studies, as well as its challenges and limitations. We focus on the latest developments with respect to miniaturization and mass spectrometry coupling, as well as data analysis and interpretation. The use of exoglycosidase sequencing in combination with current C(G)E technology is discussed, highlighting possible difficulties and pitfalls. The application section describes the detailed characterization of N-glycosylation, utilizing multiplexed CGE with laser-induced fluorescence detection (xCGE-LIF). Besides a comprehensive overview on antibody glycosylation by comparing species-specific IgGs and human immunoglobulins A, D, E, G, and M, the chapter comprises a comparison of therapeutic monoclonal antibodies from different production cell lines, as well as a detailed characterization of Fab and Fc glycosylation. These examples illustrate the full potential of C(G)E, resolving the smallest differences in sugar composition and structure.

Dynamic pH barrage junction focusing of amino acids, peptides, and digested monoclonal antibodies in capillary electrophoresis-mass spectrometry

Dynamic pH barrage junction focusing in CE enables effective signal enhancement, quantitative capture efficiencies, and straightforward optimization. The method is a technical variant of dynamic pH junction focusing. CE separation with dynamic pH barrage junction focusing is compatible with both optical and mass spectrometric detection. We developed a CE-MS/MS method using hydrophilic polyethyleneimine-coated capillaries and validated it for the qualitative analysis of amino acids, peptides, and tryptic peptides of digested monoclonal antibodies. The S/N of extracted ion electropherograms of zwitterionic analytes were enhanced by approximately two orders of magnitude with a tradeoff of a shortened separation window. Online focusing improved the MS signal intensity of a diluted antibody digest, enabling more precursor ions to be analyzed with subsequent tandem mass spectrometric identification. It also broadened the concentration range of protein digest samples for which adequate sequence coverage data can be obtained. With only 0.9 ng of digested infliximab sample loaded into the capillary, 76% and 100% sequence coverage was realized for antibody heavy and light chains, respectively, after online focusing. Full coverage was achieved with 9 ng of injected digest.

The Nanomaterial Metabolite Corona Determined Using a Quantitative Metabolomics Approach: A Pilot Study

Nanomaterials (NMs) are promptly coated with biomolecules in biological systems leading to the formation of the so-called corona. To date, research has predominantly focused on the protein corona and how it affects NM uptake, distribution, and bioactivity by conferring a biological identity to NMs enabling interactions with receptors to mediate cellular responses. Thus, protein corona studies are now integral to nanosafety assessment. However, a larger class of molecules, the metabolites, which are orders of magnitude smaller than proteins (<1000 Da) and regulate metabolic pathways, has been largely overlooked. This hampers the understanding of the bio-nano interface, development of computational predictions of corona formation, and investigations into uptake or toxicity at the cellular level, including identification of molecular initiating events triggering adverse outcome pathways. Here, a capillary electrophoresis-mass spectrometry based metabolomics approach reveals that pure polar ionogenic metabolite standards differentially adsorb to a range of 6 NMs (SiO₂ , 3 TiO₂ with different surface chemistries, and naïve and carboxylated polystyrene NMs). The metabolite corona composition is quantitatively compared using protein-free and complete plasma samples, revealing that proteins in samples significantly change the composition of the metabolite corona. This key finding provides the basis to include the metabolite corona in future nanosafety endeavors.

Analytical techniques for metabolomic studies: a review

Metabolomics is the comprehensive study of small-molecule metabolites. Obtaining a wide coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules. To study the compounds of interest spectroscopic (NMR), spectrometric (MS) and separation techniques (LC, GC, supercritical fluid chromatography, CE) are used. The choice for a given technique is influenced by the sample matrix, the concentration and properties of the metabolites, and the amount of sample. This review discusses the most commonly used analytical techniques for metabolomic studies, including their advantages, drawbacks and some applications.

Metabolomic profiling in children with inflammatory bowel disease

Ulcerative colitis (UC) and Crohn's disease (CD) represent inflammatory bowel diseases (IBD) with multifactorial pathogenesis, involving genetic, environmental and microbial factors. Interactions between gut microbiota and immune system result in changes in metabolic pathways. Metabolomics is a comprehensive and quantitative (or semi-quantitative) analysis of metabolites synthetized in human's biological system. It has been shown that metabolic profiling might be used to identify disease biomarkers. Recent findings confirmed alterations in the number of metabolites in patients with IBD. However, most of the studies included adult individuals with ongoing treatment which might have affected the metabolite profiling. Therefore, the aim of our study was to collect the knowledge about metabolomics in paediatric patients with CD or UC based on the currently published literature.

Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks

Metabolism is a complex network of compartmentalized and coupled chemical reactions, which often involve transfers of substructures of biomolecules, thus requiring metabolite substructures to be tracked. Stable isotope resolved metabolomics (SIRM) enables pathways reconstruction, even among chemically identical metabolites, by tracking the provenance of stable isotope-labeled substructures using NMR and ultrahigh resolution (UHR) MS. The latter can resolve and count isotopic labels in metabolites and can identify isotopic enrichment in substructures when operated in tandem MS mode. However, MS2 is difficult to implement with chromatography-based UHR-MS due to lengthy MS1 acquisition time that is required to obtain the molecular isotopologue count, which is further exacerbated by the numerous isotopologue source ions to fragment. We review here recent developments in tandem MS applications of SIRM to obtain more detailed information about isotopologue distributions in metabolites and their substructures.

Application of CE-MS for the analysis of histones and histone modifications

The analysis, identification and quantification of histones and their post-translational modifications plays a central role in chromatin research and in studying epigenetic regulations during physiological processes. In the last decade analytical strategies based on mass spectrometry have been greatly improved for providing a global view of single modification abundances or to determine combinatorial patterns of modifications. Presented here is a newly developed strategy for histone protein analysis and a number of applications are illustrated with an emphasis on PTM characterization. Capillary electrophoresis is coupled to mass spectrometry (CE-MS) and has proven to be a very promising concept as it enables to study intact histones (top-down proteomics) as well as the analysis of enzymatically digested proteins (bottom-up proteomics). This technology combines highly efficient low-flow CE separations with ionization in a single device and offers an orthogonal separation principle to conventional LC-MS analysis, thus expanding the existing analytical repertoire in a perfect manner.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Integrating a generalized data analysis workflow with the Single-probe mass spectrometry experiment for single cell metabolomics

We conducted single cell metabolomics studies of live cancer cells through online single cell mass spectrometry (SCMS) experiments combined with a generalized comprehensive data analysis workflow. The SCMS experiments were carried out using the Single-probe device coupled with a mass spectrometer to measure molecular profiles of cells in response to two mitotic inhibitors, taxol and vinblastine, under a series of treatment conditions. SCMS metabolomic data were analyzed using a comprehensive approach, including data pre-treatment, visualization, statistical analysis, machine learning, and pathway enrichment analysis. For comparative studies, traditional liquid chromatography-MS (LC-MS) experiments were conducted using lysates prepared from bulk cell samples. Metabolomic profiles of single cells were visualized through Partial Least Square-Discriminant Analysis (PLS-DA), and the phenotypic biomarkers associated with emerging phenotypes induced by drug treatment were discovered and compared through a series of rigorous statistical analysis. Species of interest were further identified at both the single cell and population levels. In addition, four biological pathways potentially involved in the drug treatment were determined through pathway enrichment analysis. Our work demonstrated the capability of comprehensive pipeline studies of single cell metabolomics. This method can be potentially applied to broader types of SCMS datasets for future pharmaceutical and chemotherapeutic research.

Single-cell proteomics in complex tissues using microprobe capillary electrophoresis mass spectrometry

Direct measurement of proteins produced by single cells promises to expand our understanding of molecular cell-to-cell differences (heterogeneity) and their contribution to normal and impaired development. High-resolution mass spectrometry (HRMS) is the modern technology of choice for the label-free identification and quantification of proteins, albeit usually in large populations of cells. Recent advances in microscale sample collection and processing, separation, and ionization have extended this powerful technology to single cells. This chapter describes a protocol based on microprobe capillary electrophoresis (CE) HRMS to enable the direct proteomic profiling of single cells embedded in complex tissues without the requirement for dissociation or whole-cell dissection. We here demonstrate the technology for identified individual cells in early developing embryos of Xenopus laevis and zebrafish as well as electrophysiologically identified single neurons in physiologically active brain slices from the mouse substantia nigra. Instructions are provided step-by-step to identify single cells using physiological or morphological cues, collect the content of the cells using microfabricated capillaries, and perform bottom-up proteomics using a custom-built CE electrospray ionization (ESI) mass spectrometer equipped with a quadrupole time-of-flight or orbitrap mass analyzer. Results obtained by this approach have revealed previously unknown differences between the proteomic state of embryonic cells and neurons. The data from single-cell proteomics by microprobe CE-ESI-HRMS complements those from single-cell transcriptomics, thereby opening exciting potentials to deepen our knowledge of molecular mechanisms governing cell and developmental processes.

Corona Isolation Method Matters: Capillary Electrophoresis Mass Spectrometry Based Comparison of Protein Corona Compositions Following On-Particle versus In-Solution or In-Gel Digestion

Increased understanding of the role of the nanomaterial protein corona in driving nanomaterial uptake into, and impacts on, cells and organisms, and the consequent need for characterization of the corona, has led to a flourishing of methods for isolation and analysis of the constituent proteins over the past decade. However, despite over 700 corona studies to date, very little is understood in terms of which methods provide the most precise and comprehensive characterization of the corona. With the increasing importance of the modeling of corona formation and its correlation with biological impacts, it is timely to properly characterize and validate the isolation approaches used to determine the protein corona. The current work introduces Capillary Electrophoresis with Electro Spray Ionization Mass Spectrometry (CESI-MS) as a novel method for protein corona characterizations and develops an on-particle tryptic digestion method, comparing peptide solubilization solutions and characterizing the recovery of proteins from the nanomaterial surface. The CESI-MS was compared to the gold standard nano-LC-MS for corona analysis and maintained a high degree of reproducibility, while increasing throughput by >3-fold. The on-particle digestion is compared to an in-solution digestion and an in-gel digestion of the protein corona. Interestingly, a range of different protein classes were found to be recovered to greater or lesser extents among the different methods. Apolipoproteins were detected at lower concentrations when a surfactant was used to solubilize peptides, whereas immunoglobulins in general have a high affinity for nanomaterials, and thus show lower recovery using on-particle digestion. The optimized on-particle digestion was validated using 6 nanomaterials and proved capable of recovering in excess of 97% of the protein corona. These are important factors to consider when designing corona studies and modeling corona formation and impacts, highlighting the significance of a comprehensive validation of nanomaterial corona analysis methods.