Multiplatform Analytical Methodology for Metabolic Fingerprinting of Lung Tissue

Metabolic Profiling of Liver Tissue in Diabetic Mice Treated with Artemisia Capillaris and Alisma Rhizome Using LC-MS and CE-MS

Artemisia Capillaris (AC) and Alisma Rhizome (AR) are natural products for the treatment of liver disorders in oriental medicine clinics. Here, we report metabolomic changes in the evaluation of the treatment effects of AC and AR on fatty livers in diabetic mice, along with a proposition of the underlying metabolic pathway. Hydrophobic and hydrophilic metabolites extracted from mouse livers were analyzed using HPLC-QTOF and CE-QTOF, respectively, to generate metabolic profiles. Statistical analysis of the metabolites by PLS-DA and OPLA-DA fairly discriminated between the diabetic, and the AC- and AR-treated mice groups. Various PEs mostly contributed to the discrimination of the diabetic mice from the normal mice, and besides, DG (18:1/16:0), TG (16:1/16:1/20:1), PE (21:0/20:5), and PA (18:0/21:0) were also associated with discrimination by s-plot. Nevertheless, the effects of AC and AR treatment were indistinct with respect to lipid metabolites. Of the 97 polar metabolites extracted from the CE-MS data, 40 compounds related to amino acid, central carbon, lipid, purine, and pyrimidine metabolism, with [Formula: see text] values less than 0.05, were shown to contribute to liver dysregulation. Following treatment with AC and AR, the metabolites belonging to purine metabolism preferentially recovered to the metabolic state of the normal mice. The AMP/ATP ratio of cellular energy homeostasis in AR-treated mice was more apparently increased ([Formula: see text]) than that of AC-treated mice. On the other hand, amino acids, which showed the main alterations in diabetic mice, did not return to the normal levels upon treatment with AR or AC. In terms of metabolomics, AR was a more effective natural product in the treatment of liver dysfunction than AC. These results may provide putative biomarkers for the prognosis of fatty liver disorder following treatment with AC and AR extracts.

Sheathless Capillary Electrophoresis-Mass Spectrometry for Metabolic Profiling of Biological Samples

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a protocol is presented for the analysis of anionic and cationic metabolites in biological samples by capillary electrophoresis–mass spectrometry (CE-MS). CE is well-suited for the analysis of highly polar and charged metabolites as compounds are separated on the basis of their charge-to-size ratio. A recently developed sheathless interfacing design, i.e., a porous tip interface, is used for coupling CE to electrospray ionization (ESI) MS. This interfacing approach allows the effective use of the intrinsically low-flow property of CE in combination with MS, resulting in nanomolar detection limits for a broad range of polar metabolite classes. The protocol presented here is based on employing a bare fused-silica capillary with a porous tip emitter at low-pH separation conditions for the analysis of a broad array of metabolite classes in biological samples. It is demonstrated that the same sheathless CE-MS method can be used for the profiling of cationic metabolites, including amino acids, nucleosides and small peptides, or anionic metabolites, including sugar phosphates, nucleotides and organic acids, by only switching the MS detection and separation voltage polarity. Highly information-rich metabolic profiles in various biological samples, such as urine, cerebrospinal fluid and extracts of the glioblastoma cell line, can be obtained by this protocol in less than 1 hr of CE-MS analysis.

Metabolomics - A wide-open door to personalized treatment in chronic heart failure?

Heart failure (HF) is a complex syndrome representing a final stage of various cardiovascular diseases. Despite significant improvement in the diagnosis and treatment (e.g. ACE-inhibitors, β-blockers, aldosterone antagonists, cardiac resynchronization therapy) of the disease, prognosis of optimally treated patients remains very serious and HF mortality is still unacceptably high. Therefore there is a strong need for further exploration of novel analytical methods, predictive and prognostic biomarkers and more personalized treatment. The metabolism of the failing heart being significantly impaired from its baseline state may be a future target not only for biomarker discovery but also for the pharmacologic intervention. However, an assessment of a particular, isolated metabolite or protein cannot be fully informative and makes a correct interpretation difficult. On the other hand, metabolites profile analysis may greatly assist investigator in an interpretation of the altered pathway dynamics, especially when combined with other lines of evidence (e.g. metabolites from the same pathway, transcriptomics, proteomics). Despite many prior studies on metabolism, the knowledge of peripheral and cardiac pathophysiological mechanisms responsible for the metabolic imbalance and progression of the disease is still insufficient. Metabolomics enabling comprehensive characterization of low molecular weight metabolites (e.g. lipids, sugars, organic acids, amino acids) that reflects the complete metabolic phenotype seems to be the key for further potential improvement in HF treatment (diet-based or biochemical-based). Will this -omics technique one day open a door to easy patients identification before they have a heart failure onset or its decompensation?

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Analysis of multi-source metabolomic data using joint and individual variation explained (JIVE)

Metabolic profiling is increasingly being used for understanding biological processes but there is no single analytical technique that provides a complete quantitative or qualitative profiling of the metabolome. Data fusion (i.e. joint analysis of data from multiple sources) has the potential to circumvent this issue facilitating knowledge discovery and reliable biomarker identification. Another field of application of data fusion is the simultaneous analysis of metabolomic changes through several biofluids or tissues. However, metabolomics typically deals with large datasets, with hundreds to thousands of variables and the identification of shared and individual factors or structures across multiple sources is challenging due to the high variable to sample ratios and differences in intensity and noise range. In this work we apply a recent method, Joint and Individual Variation Explained (JIVE), for the integrated unsupervised analysis of metabolomic profiles from multiple data sources. This method separates the shared patterns among data sources (i.e. the joint structure) from the individual structure of each data source that is unrelated to the joint structure. Two examples are described to show the applicability of JIVE for the simultaneous analysis of multi-source data using: (i) plasma samples subjected to different analytical techniques, sample treatment and measurement conditions; and (ii) plasma and urine samples subjected to liquid chromatography-mass spectrometry measured using two ionization conditions.

Capillary Electrophoresis-Mass Spectrometry for Clinical Metabolomics

In clinical metabolomics, capillary electrophoresis-mass spectrometry (CE-MS) has become a very useful technique for the analysis of highly polar and charged metabolites in complex biologic samples. A comprehensive overview of recent developments in CE-MS for metabolic profiling studies is presented. This review covers theory, CE separation modes, capillary coatings, and practical aspects of CE-MS coupling. Attention is also given to sample pretreatment and data analysis strategies used for metabolomics. The applicability of CE-MS for clinical metabolomics is illustrated using samples ranging from plasma and urine to cells and tissues. CE-MS application to large-scale and quantitative clinical metabolomics is addressed. Conclusions and perspectives on this unique analytic strategy are presented.

Capillary Electrophoresis in Metabolomics

Metabolomics is an analytical toolbox to describe (all) low-molecular-weight compounds in a biological system, as cells, tissues, urine, and feces, as well as in serum and plasma. To analyze such complex biological samples, high requirements on the analytical technique are needed due to the high variation in compound physico-chemistry (cholesterol derivatives, amino acids, fatty acids as SCFA, MCFA, or LCFA, or pathway-related metabolites belonging to each individual organism) and concentration dynamic range. All main separation techniques (LC-MS, GC-MS) are applied in routine to metabolomics hyphenated or not to mass spectrometry, and capillary electrophoresis is a powerful high-resolving technique but still underused in this field of complex samples. Metabolomics can be performed in the non-targeted way to gain an overview on metabolite profiles in biological samples. Targeted metabolomics is applied to analyze quantitatively pre-selected metabolites. This chapter reviews the use of capillary electrophoresis in the field of metabolomics and exemplifies solutions in metabolite profiling and analysis in urine and plasma.

From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS

This tutorial provides a comprehensive description of the GC-MS-based untargeted metabolomics workflow including: ethical approval requirement, sample collection and storage, equipment maintenance and setup, sample treatment, monitoring of analytical variability, data pre-processing including deconvolution by free software such as AMDIS, data processing, statistical analysis and validation, detection of outliers and biological interpretation of the results. For each stage tricks will be suggested, pitfalls will be highlighted and advice will be provided on how to get the best from this methodology and technique. In addition, a step-by-step procedure and an example of our in-house library have been included in the supplementary material to lead the user through the concepts described herein. As a case study, an interesting example from one of our experiments at CEMBIO Research Centre is described, presenting an example of the use of this ready-to use protocol for identification of a metabolite that was not previously included in Fiehn commercial target library.

Analytical methodology for metabolomics study of adherent mammalian cells using NMR, GC-MS and LC-HRMS

We developed a methodology for the analysis of intracellular metabolites using nuclear magnetic resonance spectrometry (NMR), gas-chromatography coupled with mass spectrometry (GC-MS), and liquid chromatography coupled with high resolution mass spectrometry (LC-HRMS). The main steps for analysis of adherent cells in order to recover the widest possible range of intracellular compounds are blocking metabolic activity by quenching and extraction of intracellular metabolites. We explored three protocols to quench NSC-34 cell metabolism and four different extraction methods, analyzed by NMR. On the basis of the number of metabolites extracted and their relative standard deviation (RSD) analyzed by NMR, the most reproducible protocol [quenching by MeOH at -40 °C and extraction with CH2Cl2/MeOH/H2O (3:3:2)] was used to obtain intracellular media to be analyzed by GC-MS and LC-HRMS. GC-MS analysis was optimized by three oximation procedures followed by silylation derivatization and these were compared to silylation alone. Using reversed-phase liquid chromatography (C18), four different gradients for LC-MS were compared. The analytical protocols were determined to establish the reliability and suitability of sample treatments required to achieve the correct biological analysis of untargeted mammalian cell metabolomics.

Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis

Differences in the degree and severity of Acute Coronary Syndrome, associated to differences in the electrocardiogram, together with blood tests of biomarkers classify patients for diagnosis and treatment. Cases where the electrocardiogram and/or biomarkers are not conclusive still appear, and there is a need for complementary biomarkers for routine determinations. Metabolomics approaches with blind fingerprinting could reveal differences in metabolites, which must be confirmed by means of targeted determinations. CE-MS and HILIC-MS are well suited for the determination of highly polar compounds, like those from to the intermediate metabolism, altered due to acute stress induced by myocardial infarction. Serum from patients with ST-elevated and non-ST elevated myocardial infarction was collected at intensive care and emergency units, and fingerprinted with CE-MS. Data pretreatment and analysis showed up carnitine-related compounds and amino acids differentially present in both groups. Acylcarnitines and amino acids were then quantitatively measured with HILIC-MS-QqQ. The significance of the differences and the sensitivity/specificity of each compound were individually evaluated. The ratio of free carnitine to acylcarnitines, together with the ratios of acetylcarnitine to betaine, to threonine, and to citrulline, showed high significance and area under the curve in the respective receiver operating characteristic curves. This study opens new possibilities for defining new sets of biomarkers for refining the diagnosis of the patients with difficult classification.

Solutions for low and high accuracy mass spectrometric data matching: a data-driven annotation strategy in nontargeted metabolomics

Ultra high pressure liquid chromatography coupled to mass spectrometry (UHPLC-MS) has become a widespread analytical technique in metabolomics investigations, however the benefit of high-performance chromatographic separation is often blunted due to insufficient mass spectrometric accuracy. A strategy that allows for the matching of UHPLC-MS data to highly accurate direct infusion electrospray ionization (DI-ESI) Fourier transform ion cyclotron resonance/mass spectrometry (FTICR/MS) data is developed in this manuscript. Mass difference network (MDiN) based annotation of FTICR/MS data and matching to unique UHPLC-MS peaks enables the consecutive annotation of the chromatographic data set. A direct comparison of experimental m/z values provided no basis for the matching of both platforms. The matching of annotation-based exact neutral masses finally enabled the integration of platform specific multivariate statistical evaluations, minimizing the danger to compare artifacts generated on either platform. The approach was developed on a non-alcoholic fatty liver disease (NAFLD) data set.

Metabolomics techniques for nanotoxicity investigations

Nanomaterials are commonly defined as engineered structures with at least one dimension of 100 nm or less. Investigations of their potential toxicological impact on biological systems and the environment have yet to catch up with the rapid development of nanotechnology and extensive production of nanoparticles. High-throughput methods are necessary to assess the potential toxicity of nanoparticles. The omics techniques are well suited to evaluate toxicity in both in vitro and in vivo systems. Besides genomic, transcriptomic and proteomic profiling, metabolomics holds great promises for globally evaluating and understanding the molecular mechanism of nanoparticle–organism interaction. This manuscript presents a general overview of metabolomics techniques, summarizes its early application in nanotoxicology and finally discusses opportunities and challenges faced in nanotoxicology.

Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease

Metabolic profiling studies aim to achieve broad metabolome coverage in specific biological samples. However, wide metabolome coverage has proven difficult to achieve, mostly because of the diverse physicochemical properties of small molecules, obligating analysts to seek multiplatform and multimethod approaches. Challenges are even greater when it comes to applications to tissue samples, where tissue lysis and metabolite extraction can induce significant systematic variation in composition. We have developed a pipeline for obtaining the aqueous and organic compounds from diseased arterial tissue using two consecutive extractions, followed by a different untargeted UPLC-MS analysis method for each extract. Methods were rationally chosen and optimized to address the different physicochemical properties of each extract: hydrophilic interaction liquid chromatography (HILIC) for the aqueous extract and reversed-phase chromatography for the organic. This pipeline can be generic for tissue analysis as demonstrated by applications to different tissue types. The experimental setup and fast turnaround time of the two methods contributed toward obtaining highly reproducible features with exceptional chromatographic performance (CV % < 0.5%), making this pipeline suitable for metabolic profiling applications. We structurally assigned 226 metabolites from a range of chemical classes (e.g., carnitines, α-amino acids, purines, pyrimidines, phospholipids, sphingolipids, free fatty acids, and glycerolipids) which were mapped to their corresponding pathways, biological functions and known disease mechanisms. The combination of the two untargeted UPLC-MS methods showed high metabolite complementarity. We demonstrate the application of this pipeline to cardiovascular disease, where we show that the analyzed diseased groups (n = 120) of arterial tissue could be distinguished based on their metabolic profiles.

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

Invasive, site-specific metabolite information could be better obtained from tissues. Hence, highly sensitive mass spectrometry-based metabolomics coupled with separation techniques are increasingly in demand in clinical research for tissue metabolomics application. Applying these techniques to nontargeted tissue metabolomics provides identification of distinct metabolites. These findings could help us to understand alterations at the molecular level, which can also be applied in clinical practice as screening markers for early disease diagnosis. However, tissues as solid and heterogeneous samples pose an additional analytical challenge that should be considered in obtaining broad, reproducible and representative analytical profiles. This manuscript summarizes the state of the art in tissue (human and animal) treatment (quenching, homogenization and extraction) for nontargeted metabolomics with mass spectrometry.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.