Technical and Biological Variation in UPLC−MS-Based Untargeted Metabolic Profiling of Liver Extracts: Application in an Experimental Toxicity Study on Galactosamine

Development of metabolomic biomarkers to discriminate the geographical origin of Korean and Russian snow crabs using CE-TOF/MS

The quantity of snow crabs (Chionoecetes opilio) harvested in Korea is subject to seasonal restrictions; therefore, snow crabs are imported from Russia. Metabolites in snow crabs from two geographic origins were compared. The metabolites were subjected to metabolomic analysis to prevent fraudulent sales of marine products from a particular country. Capillary electrophoresis time-of-flight mass spectrometry was used. Seventy-seven target metabolites were identified using a mass spectral library. Through orthogonal partial least squares discriminant analysis, the top 25 biomarker candidates were evaluated based on p-values and fold changes. A total of 246 peaks (187 and 59 in the cation and anion modes, respectively) were identified. Among the biomarker candidates, 2-oxovaleric acid, asymmetric dimethylarginine, hypotaurine, and allo-threonine were selected as final biomarkers to unequivocally determine the geographic origin. Overall, metabolic analyses allowed us to differentiate snow crabs from different geographic origins. This method could also be extended of other marine products.

Development of metabolome extraction strategy for metabolite profiling of skin tissue

INTRODUCTION

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Hippocampal metabolic profile during epileptogenesis in the pilocarpine model of epilepsy

Temporal lobe epilepsy (TLE) is a common form of refractory epilepsy in adulthood. The metabolic profile of epileptogenesis is still poorly investigated. Elucidation of such a metabolic profile using animal models of epilepsy could help identify new metabolites and pathways involved in the mechanisms of epileptogenesis process. In this study, we evaluated the metabolic profile during the epileptogenesis periods. Using a pilocarpine model of epilepsy, we analyzed the global metabolic profile of hippocampal extracts by untargeted metabolomics based on ultra-performance liquid chromatography-high-resolution mass spectrometry, at three time points (3 h, 1 week, and 2 weeks) after status epilepticus (SE) induction. We demonstrated that epileptogenesis periods presented different hippocampal metabolic profiles, including alterations of metabolic pathways of amino acids and lipid metabolism. Six putative metabolites (tryptophan, N-acetylornithine, N-acetyl-L-aspartate, glutamine, adenosine, and cholesterol) showed significant different levels during epileptogenesis compared to their respective controls. These putative metabolites could be associated with the imbalance of neurotransmitters, mitochondrial dysfunction, and cell loss observed during both epileptogenesis and epilepsy. With these findings, we provided an overview of hippocampal metabolic profiles during different stages of epileptogenesis that could help investigate pathways and respective metabolites as predictive tools in epilepsy.

Performance comparison of three scaling algorithms in NMR-based metabolomics analysis

Unit variance (UV) scaling, mean centering (CTR) scaling, and Pareto (Par) scaling are three commonly used algorithms in the preprocessing of metabolomics data. Based on our NMR-based metabolomics studies, we found that the clustering identification performances of these three scaling methods were dramatically different as tested by the spectra data of 48 young athletes’ urine samples, spleen tissue (from mice), serum (from mice), and cell (from Staphylococcus aureus) samples. Our data suggested that for the extraction of clustering information, UV scaling could serve as a robust approach for NMR metabolomics data for the identification of clustering analysis even with the existence of technical errors. However, for the purpose of discriminative metabolite identification, UV scaling, CTR scaling, and Par scaling could equally extract discriminative metabolites efficiently based on the coefficient values. Based on the data presented in this study, we propose an optimal working pipeline for the selection of scaling algorithms in NMR-based metabolomics analysis, which has the potential to serve as guidance for junior researchers working in the NMR-based metabolomics research field.

Alterations in Microbial-Associated Fecal Metabolites in Relation to Arsenic Exposure Among Infants

In utero and early life exposure to inorganic arsenic (iAs) alters immune response in experimental animals and is associated with an increased risk of infant infections. iAs exposure is related to differences in the gut microbiota diversity, community structure, and the relative abundance of individual microbial taxa both in laboratory and human studies. Metabolomics permits a direct measure of molecular products of microbial and host metabolic processes. We conducted NMR metabolomics analysis on infant stool samples and quantified the relative concentrations of 34 known microbial-related metabolites. We examined these metabolites in relation to both in utero and infant log2 urinary total arsenic concentrations (utAs, the sum of iAs and iAs metabolites) collected at approximately 6 weeks of age using linear regression models, adjusted for infant sex, age at sample collection, type of delivery (vaginal vs. cesarean section), feeding mode (breast milk vs. any formula), and specific gravity. Increased fecal butyrate (b = 214.24), propionate (b = 518.33), cholate (b = 8.79), tryptophan (b= 14.23), asparagine (b = 28.80), isoleucine (b = 65.58), leucine (b = 95.91), malonate (b = 50.43), and uracil (b = 36.13), concentrations were associated with a doubling of infant utAs concentrations (p< 0.05). These associations were largely among infants who were formula fed. No clear associations were observed with maternal utAs and infant fecal metabolites. Metabolomic analyses of infant stool samples lend further evidence that the infant gut microbiota is sensitive to As exposure, and these effects may have functional consequences.

Association of Cesarean Delivery and Formula Supplementation with the Stool Metabolome of 6-Week-Old Infants

Cesarean delivery and formula feeding have both been implicated as important factors associated with perturbations to the infant gut microbiome. To investigate the functional metabolic response of the infant gut microbial milieu to these factors, we profiled the stool metabolomes of 121 infants from a US pregnancy cohort study at approximately 6 weeks of life and evaluated associations with delivery mode and feeding method. Multivariate analysis of six-week stool metabolomic profiles indicated discrimination by both delivery mode and diet. For diet, exclusively breast-fed infants exhibited metabolomic profiles that were distinct from both exclusively formula-fed and combination-fed infants, which were relatively more similar to each other in metabolomic profile. We also identified individual metabolites that were important for differentiating delivery mode groups and feeding groups and metabolic pathways related to delivery mode and feeding type. We conclude based on previous work and this current study that the microbial communities colonizing the gastrointestinal tracts of infants are not only taxonomically, but also functionally distinct when compared according to delivery mode and feeding groups. Further, different sets of metabolites and metabolic pathways define delivery mode and diet metabotypes.

New Advances in Tissue Metabolomics: A Review

Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.

Evaluation of liver tissue extraction protocol for untargeted metabolomics analysis by ultra-high-performance liquid chromatography/tandem mass spectrometry

The aim of the untargeted metabolomics study is to obtain a global metabolome coverage from biological samples. Therefore, a comprehensive and systematic protocol for tissue metabolite extraction is highly desirable. In this study, we evaluated a comprehensive liver pretreatment strategy based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry to obtain more metabolites using four different protocols. These protocols included (A) methanol protein precipitation, (B) two-step extraction of dichloromethane-methanol followed by methanol-water, (C) two-step extraction of methyl tert-butyl ether-methanol followed by methanol-water, and (D) two-step extraction of isopropanol-methanol followed by methanol-water. Our results showed that protocol D was superior to the others due to more extracted features, annotated metabolites, and better reproducibility. And then, the stability and extraction sequence of protocol D were evaluated. The results showed that extraction with isopropanol-methanol followed by methanol-water was the optimum preparation sequence, which offered higher extraction efficiency, satisfactory repeatability, and acceptable stability. Furthermore, the optimal protocol was successfully applied by liver samples of rats after high-fat intervention. In summary, our protocol enabled a comprehensive and systematic evaluation of liver pretreatment to obtain more medium-polar and nonpolar metabolites and was suitable for high-throughput metabolomics analysis.

Pharmacometabonomics: data processing and statistical analysis

Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.

Metabolic Biomarkers of Squamous Cell Carcinoma of the Aerodigestive Tract: A Systematic Review and Quality Assessment

Introduction. Aerodigestive squamous cell carcinomas (ASCC) constitute a major source of global cancer deaths. Patients typically present with advanced, incurable disease, so new means of detecting early disease are a research priority. Metabolite quantitation is amenable to point-of-care analysis and can be performed in ASCC surrogates such as breath and saliva. The purpose of this systematic review is to summarise progress of ASCC metabolomic studies, with an emphasis on the critical appraisal of methodological quality and reporting.

METHOD

A systematic online literature search was performed to identify studies reporting metabolic biomarkers of ASCC. This review was conducted in accordance with the recommendations of the Cochrane Library and MOOSE guidelines.

RESULTS

Thirty studies comprising 2117 patients were included in the review. All publications represented phase-I biomarker discovery studies, and none validated their findings in an independent cohort. There was heterogeneity in study design and methodological and reporting quality. Sensitivities and specificities were higher in oesophageal and head and neck squamous cell carcinomas compared to those in lung squamous cell carcinoma. The metabolic phenotypes of these cancers were similar, as was the kinetics of metabolite groups when comparing blood, tissue, and breath/saliva concentrations. Deregulation of amino acid metabolism was the most frequently reported theme.

CONCLUSION

Metabolite analysis has shown promising diagnostic performance, especially for oesophageal and head and neck ASCC subtypes, which are phenotypically similar. However, shortcomings in study design have led to inconsistencies between studies. To support future studies and ultimately clinical adoption, these limitations are discussed.

Analysis of NMR Metabolomics Data

In this chapter, we summarize data preprocessing and data analysis strategies used for analysis of NMR data for metabolomics studies. Metabolomics consists of the analysis of the low molecular weight compounds in cells, tissues, or biological fluids, and has been used to reveal biomarkers for early disease detection and diagnosis, to monitor interventions, and to provide information on pathway perturbations to inform mechanisms and identifying targets. Metabolic profiling (also termed metabotyping) involves the analysis of hundreds to thousands of molecules using mainly state-of-the-art mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy technologies. While NMR is less sensitive than mass spectrometry, NMR does provide a wealth of complex and information rich metabolite data. NMR data together with the use of conventional statistics, modeling methods, and bioinformatics tools reveals biomarker and mechanistic information. A typical NMR spectrum, with up to 64k data points, of a complex biological fluid or an extract of cells and tissues consists of thousands of sharp signals that are mainly derived from small molecules. In addition, a number of advanced NMR spectroscopic methods are available for extracting information on high molecular weight compounds such as lipids or lipoproteins. There are numerous data preprocessing, data reduction, and analysis methods developed and evolving in the field of NMR metabolomics. Our goal is to provide an extensive summary of NMR data preprocessing and analysis strategies by providing examples and open source and commercially available analysis software and bioinformatics tools.

Metabolomics profiling in a mouse model reveals protective effect of Sancao granule on Con A-Induced liver injury

ETHNOPHARMACOLOGICAL RELEVANCE

Sancao granule (SCG) is a traditional Chinese herb formula, which has been used for autoimmune liver disease for decades. Previous study demonstrated that there was an exactly therapeutic effect of SCG on autoimmune hepatitis (AIH) by improving liver function and alleviating the clinical symptoms. However, studies of the mechanism by which SCG alleviates Con A-induced liver injury (CILI) should be complemented.

MATERIALS AND METHODS

An ultraperformance liquid chromatography with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS)-based metabolomics approach combined with principle component analysis (PCA) and orthogonal projection to latent structures discriminate analysis (OPLS-DA) were integrated applied to obtain metabolites for clarifying mechanisms of disease.

RESULTS

In accordance with previously study, the present study demonstrated that SCG could obviously improve the liver injury in mouse induced by Con A via downregulating serum biochemical indexes, alleviating the histological damage and inhibiting the neutrophil infiltration in liver tissues. Different expression of 9 metabolites related to 8 pathways, including fatty acid biosynthesis, arachidonic acid metabolisms, linoleic acid metabolisms, sphingolipid metabolisms, fatty acid elongation in mitochondria, glycerophospholipid metabolism, fatty acid metabolism, pyrimidine metabolism were demonstrated responsible for the efficacy of SCG in treating CILI.

CONCLUSION

In sum up, SCG has been indicated favorable therapeutic effect on Con A induced liver injury. And metabolomics could be a promising approach, which provide insights into mechanisms of SCG in treating CILI.

Broad spectrum metabolomics for detection of abnormal metabolic pathways in a mouse model for retinitis pigmentosa

Retinitis pigmentosa (RP) is a degenerative disease of the retina that affects approximately 1 million people worldwide. There are multiple genetic causes of this disease, for which, at present, there are no effective therapeutic strategies. In the present report, we utilized broad spectrum metabolomics to identify perturbations in the metabolism of the rd10 mouse, a genetic model for RP that contains a mutation in Pde6β. These data provide novel insights into mechanisms that are potentially critical for retinal degeneration. C57BL/6J and rd10 mice were raised in cyclic light followed by either light or dark adaptation at postnatal day (P) 18, an early stage in the degeneration process. Mice raised entirely in the dark until P18 were also evaluated. After euthanasia, retinas were removed and extracted for analysis by ultra-performance liquid chromatography-time of flight-mass spectrometry (UPLC-QTOF-MS). Compared to wild type mice, rd10 mice raised in cyclic light or in complete darkness demonstrate significant alterations in retinal pyrimidine and purine nucleotide metabolism, potentially disrupting deoxynucleotide pools necessary for mitochondrial DNA replication. Other metabolites that demonstrate significant increases are the Coenzyme A intermediate, 4'-phosphopantothenate, and acylcarnitines. The changes in these metabolites, identified for the first time in a model of RP, are highly likely to disrupt normal energy metabolism. High levels of nitrosoproline were also detected in rd10 retinas relative to those from wild type mice. These results suggest that nitrosative stress may be involved in retinal degeneration in this mouse model.

Optimized GC-MS metabolomics for the analysis of kidney tissue metabolites

INTRODUCTION

Metabolomics is a promising approach for discovery of relevant biomarkers in cells, tissues, organs, and biofluids for disease identification and prediction. The field has mostly relied on blood-based biofluids (serum, plasma, urine) as non-invasive sources of samples as surrogates of tissue or organ-specific conditions. However, the tissue specificity of metabolites pose challenges in translating blood metabolic profiles to organ-specific pathophysiological changes, and require further downstream analysis of the metabolites.

OBJECTIVES

As part of this project, we aim to develop and optimize an efficient extraction protocol for the analysis of kidney tissue metabolites representative of key primate metabolic pathways.

METHODS

Kidney cortex and medulla tissues of a baboon were homogenized and extracted using eight different extraction protocols including methanol/water, dichloromethane/methanol, pure methanol, pure water, water/methanol/chloroform, methanol/chloroform, methanol/acetonitrile/water, and acetonitrile/isopropanol/water. The extracts were analyzed by a two-dimensional gas chromatography time-of-flight mass-spectrometer (2D GC-ToF-MS) platform after methoximation and silylation.

RESULTS

Our analysis quantified 110 shared metabolites in kidney cortex and medulla tissues from hundreds of metabolites found among the eight different solvent extractions spanning low to high polarities. The results revealed that medulla is metabolically richer compared to the cortex. Dichloromethane and methanol mixture (3:1) yielded highest number of metabolites across both the tissue types. Depending on the metabolites of interest, tissue type, and the biological question, different solvents can be used to extract specific groups of metabolites.

CONCLUSION

This investigation provides insights into selection of extraction solvents for detection of classes of metabolites in renal cortex and medulla, which is fundamentally important for identification of prognostic and diagnostic metabolic kidney biomarkers for future therapeutic applications.

Tissue Multiplatform-Based Metabolomics/Metabonomics for Enhanced Metabolome Coverage

The use of tissue as a matrix to elucidate disease pathology or explore intervention comes with several advantages. It allows investigation of the target alteration directly at the focal location and facilitates the detection of molecules that could become elusive after secretion into biofluids. However, tissue metabolomics/metabonomics comes with challenges not encountered in biofluid analyses. Furthermore, tissue heterogeneity does not allow for tissue aliquoting. Here we describe a multiplatform, multi-method workflow which enables metabolic profiling analysis of tissue samples, while it can deliver enhanced metabolome coverage. After applying a dual consecutive extraction (organic followed by aqueous), tissue extracts are analyzed by reversed-phase (RP-) and hydrophilic interaction liquid chromatography (HILIC-) ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) spectroscopy. This pipeline incorporates the required quality control features, enhances versatility, allows provisional aliquoting of tissue extracts for future guided analyses, expands the range of metabolites robustly detected, and supports data integration. It has been successfully employed for the analysis of a wide range of tissue types.

The hematinic effect of Colla corii asini (Ejiao) using 1H-NMR metabolomics coupled with correlation analysis in APH-induced anemic rats

Colla corii asini (Ejiao), a gelatin-like food and drug obtained from Equus asinus Linnaeus, has been widely used in clinical hematic antianemic therapy for more than 2000 years.

A Conversation on Data Mining Strategies in LC-MS Untargeted Metabolomics: Pre-Processing and Pre-Treatment Steps

Untargeted metabolomic studies generate information-rich, high-dimensional, and complex datasets that remain challenging to handle and fully exploit. Despite the remarkable progress in the development of tools and algorithms, the "exhaustive" extraction of information from these metabolomic datasets is still a non-trivial undertaking. A conversation on data mining strategies for a maximal information extraction from metabolomic data is needed. Using a liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomic dataset, this study explored the influence of collection parameters in the data pre-processing step, scaling and data transformation on the statistical models generated, and feature selection, thereafter. Data obtained in positive mode generated from a LC-MS-based untargeted metabolomic study (sorghum plants responding dynamically to infection by a fungal pathogen) were used. Raw data were pre-processed with MarkerLynx^TM software (Waters Corporation, Manchester, UK). Here, two parameters were varied: the intensity threshold (50-100 counts) and the mass tolerance (0.005-0.01 Da). After the pre-processing, the datasets were imported into SIMCA (Umetrics, Umea, Sweden) for more data cleaning and statistical modeling. In addition, different scaling (unit variance, Pareto, etc.) and data transformation (log and power) methods were explored. The results showed that the pre-processing parameters (or algorithms) influence the output dataset with regard to the number of defined features. Furthermore, the study demonstrates that the pre-treatment of data prior to statistical modeling affects the subspace approximation outcome: e.g., the amount of variation in X-data that the model can explain and predict. The pre-processing and pre-treatment steps subsequently influence the number of statistically significant extracted/selected features (variables). Thus, as informed by the results, to maximize the value of untargeted metabolomic data, understanding of the data structures and exploration of different algorithms and methods (at different steps of the data analysis pipeline) might be the best trade-off, currently, and possibly an epistemological imperative.

Untargeted Metabolomics Analysis of ABCC6-Deficient Mice Discloses an Altered Metabolic Liver Profile

Loss-of-function mutations in the transmembrane ABCC6 transport protein cause pseudoxanthoma elasticum (PXE), an ectopic, metabolic mineralization disorder that affects the skin, eye, and vessels. ABCC6 is assumed to mediate efflux of one or several small molecule compounds from the liver cytosol to the circulation. Untargeted metabolomics using liquid chromatography-mass spectrometry was employed to inspect liver cytosolic extracts from mice with targeted disruption of the Abcc6 gene. Absence of the ABCC6 protein induced an altered profile of metabolites in the liver causing accumulation of compounds as more features were upregulated than downregulated in ABCC6-deficient mice. However, no differences of the identified metabolites in liver could be detected in plasma, whereas urine reflected some of the changes. Of note, N-acetylated amino acids and pantothenic acid (vitamin B5), which is involved in acetylation reactions, were accumulated in the liver. None of the identified metabolites seems to explain mineralization in extrahepatic tissues, but the present study now shows that abrogated ABCC6 function does cause alterations in the metabolic profile of the liver in accordance with PXE being a metabolic disease originating from liver disturbance. Further studies of these changes and the further identification of yet unknown metabolites may help to clarify the liver-related pathomechanism of PXE.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

INTRODUCTION

The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.

OBJECTIVES

Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.

METHODS

We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.

RESULTS

Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.

CONCLUSION

The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

Thermal-assisted gasification injector for analyzing high-salt solution samples: a novel device developed for online coupling of liquid chromatography with direct analysis in real time mass spectrometry

A thermal-assisted gasification injector was designed for online coupling of liquid-chromatography to direct-analysis-in-real-time mass-spectrometry. The method can be used in analysis with an inorganic salt matrix and weak polar solvent.

(1)H NMR-based metabolic profiling of liver in chronic unpredictable mild stress rats with genipin treatment

Genipin, a hydrolyzed metabolite of geniposide extracted from the fruit of Gardenia jasminoides Ellis, has shown promise in alleviating depressive symptoms, however, the antidepressant mechanism of genipin remains unclear and incomprehensive. In this study, the metabolic profiles of aqueous and lipophilic extracts in liver of the chronic unpredictable mild stress (CUMS)-induced rat with genipin treatment were investigated using proton nuclear magnetic resonance ((1)H NMR) spectroscopy coupled with multivariate data analysis. Significant differences in the metabolic profiles of rats in the CUMS model group (MS) and the control group (NS) were observed with metabolic effects including decreasing in choline, glycerol and glycogen, increasing in lactate, alanine and succinate, and a disordered lipid metabolism, while the moderate dose (50mg/kg) of genipin could significantly regulate the concentrations of glycerol, lactate, alanine, succinate and the lipid to their normal levels. These biomakers were involved in metabolism pathways such as glycolysis/gluconeogensis, tricarboxylic acid (TCA) cycle and lipid metabolism, which may be helpful for understanding of antidepressant mechanism of genipin.

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

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.

Optimization of metabolite extraction of human vein tissue for ultra performance liquid chromatography-mass spectrometry and nuclear magnetic resonance-based untargeted metabolic profiling

Tissue extraction optimization for untargeted metabolic profiling.

Global metabolic profiling using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Currently, liquid chromatography-mass spectrometry (LC-MS) is one of the most important analytical technologies for detecting hundreds of metabolites in the field of metabolomics. A recent advance in LC that has impacted metabolomics is the development of UPLC (ultra-performance liquid chromatography). In this chapter, we describe the analytical methodologies for the global metabolic profiling of serum, urine, and tissue samples using UPLC-Q-TOF (quadrupole-time-of-flight)-MS. Aqueous metabolites are extracted after adding methanol/acetonitrile/acetone and then analyzed by UPLC-MS under positive and/or negative ionization mode. With the aid of multivariate statistical analysis, separation between various groups can be observed in the score plots, and biomarkers are screened in the loading/weight/VIP (variable importance in the projection) scatterplots. Furthermore, putative markers can be identified through comparison with the authentic standards based on tandem mass spectrometry (MS/MS) fragmentation pattern and LC retention. We expect that our protocol, with modifications if necessary, can be useful in many metabolomics studies and a wide range of research areas related to small molecules and LC-MS.

Liquid Chromatographic Techniques in Metabolomics

In the past decade, LC‐MS‐based metabolomic/metabonomic profiling has become a major analytical focus for biomarker research. Chromatographic resolution is continually improving with the development of more advanced separation platforms based on smaller particle sizes, new types of stationary phase and miniaturized systems allowing the profiling of biological samples for metabolites in ways that were simply not possible before. Chromatographic advances, combined with increased mass resolution instruments that provide sub‐2 ppm mass accuracy and high sensitivity, have greatly facilitated the detection and identification of potential biomarkers. In this chapter, the most common LC(‐MS) methods utilized in metabolic analyses are presented, with emphasis on novel high‐efficiency and high‐throughput analyses and their suitability for metabolic analyses. Guidelines for the selection of the appropriate method for different applications are given, with emphasis on the use of LC‐MS.

Brief Communication

Previous reports investigating the mechanisms of galactosamine toxicity have discussed the presence of responders and nonresponders after intraperitoneal (IP) administration of a toxic dose. The incidence of nonresponders has been reported to be as high as 47%. To rule out inadvertent intestinal, solid organ, or subcutaneous injection as at least a partial cause for the variability, we performed midline incisions and dosed 10 rats via a flexible catheter, with a toxic dose of galactosamine. Results were compared to a previous range finding study with IP-injected rats. As opposed to the IP-injected rats that had a roughly 50% response rate (based on serum alanine aminotransferase [ALT] elevation) and 100% of the midline incision catheter-instilled rats had elevations in ALT. Saline controls had no elevations. Histopathologic examination of livers from 5 midline-incisioned rats euthanized 48 hr after dosing with the lowest ALT responses revealed portal eosinophilic infiltrates and biliary hypertrophy/hyperplasia contiguous with areas of necrosis. Examination of 5 rats with the highest ALT elevations euthanized 10 days post dose revealed similar lesions to be resolving. We conclude that a significant contribution to variability in response to IP-injected galactosamine and possibly other investigative drugs is inadvertent misinjection of all or part of the dose.

Simultaneous extraction of metabolome and lipidome with methyl tert-butyl ether from a single small tissue sample for ultra-high performance liquid chromatography/mass spectrometry

A common challenge for scientists working with animal tissue or human biopsy samples is the limitation of material and consequently, the difficulty to perform comprehensive metabolic profiling within one experiment. Here, we present a novel approach to simultaneously perform targeted and non-targeted metabolomics as well as lipidomics from one small piece of liver or muscle tissue by ultra-high performance liquid chromatography/mass spectrometry (UHPLC/MS) following a methyl tert-butyl ether (MTBE)-based extraction. Equal relative amounts of the resulting polar and non-polar fractions were pooled, evaporated and reconstituted in the appropriate solvent for UHPLC/MS analysis. This mix was comparable or superior in yield and reproducibility to a standard 80% methanol extraction for the profiling of polar and lipophilic metabolites (free carnitine, acylcarnitines and FFA). The mix was also suitable for non-targeted metabolomics, an easy measure to increase the metabolite coverage by 30% relative to using the polar fraction alone. Lipidomics was performed from an aliquot of the non-polar fraction. This novel strategy could successfully be applied to one mouse soleus muscle with a dry weight of merely 2.5 mg. By enabling a simultaneous profiling of lipids and metabolites with mixed polarity while saving material for molecular, biochemical or histological analyses, our approach may open up new perspectives toward a comprehensive investigation of small, valuable tissue samples.

Profiling of secondary metabolites in tissues from Rheum palmatum L. using laser microdissection and liquid chromatography mass spectrometry

Evaluating the quality of herbal medicines by morphological features is a convenient, quick, and practical method compared with other methods that mostly depend on modern instruments. Here, laser microdissection and ultra-performance liquid chromatography are combined with mass spectrometry to map the distribution of secondary metabolites in cells or tissues of a herb itself for correlating its bioactive components and morphological features. The root and rhizome of Rheum palmatum L. were taken as research target, which is the Chinese medicine, Radix et Rhizoma Rhei. According to fluorescent microscopic characteristics, 12 herbal cells or tissues of Radix et Rhizoma Rhei were separated by laser microdissection. Thirty-eight compounds were identified or tentatively characterized in the microdissected tissues. (+)-Catechin, 1-O-galloyl-2-O-cinnamoyl-β-D-glucose, and emodin were found to be the major components in most of the tissues. The brown ergastic substances found in rays of normal and anomalous vascular bundles as well as the parenchymatous cells of rhizome pith and the parenchymatous cells of root xylem contained higher than average amounts of these three components and more kinds of secondary metabolites. Overall, results suggest that Radix et Rhizoma Rhei of larger size and with conspicuous "brocaded patterns" and star spots are of higher quality as they tend to have greater contents of bioactive components. The study provides quantitative and specific criteria by which the quality of Radix et Rhizoma Rhei can be judged. This research also established a new, reliable, and practical method for direct profiling and imaging of secondary metabolites in any herbal tissue.

Discovery of safety biomarkers for realgar in rat urine using UFLC-IT-TOF/MS and 1H NMR based metabolomics

As an arsenical, realgar (As4S4) is known as a poison and paradoxically as a therapeutic agent. However, a complete understanding of the precise biochemical alterations accompanying the toxicity and therapy effects of realgar is lacking. Using a combined ultrafast liquid chromatography (UFLC) coupled with ion trap time-of-flight mass spectrometry (IT-TOF/MS) and (1)H NMR spectroscopy based metabolomics approach, we were able to delineate significantly altered metabolites in the urine samples of realgar-treated rats. The platform stability of the liquid chromatography LC/MS and NMR techniques was systematically investigated, and the data processing method was carefully optimized. Our results indicate significant perturbations in amino acid metabolism, citric acid cycle, choline metabolism, and porphyrin metabolism. Thirty-six metabolites were proposed as potential safety biomarkers related to disturbances caused by realgar, and glycine and serine are expected to serve as the central contacts in the metabolic pathways related to realgar-induced disturbance. The LC/MS and NMR based metabolomics approach established provided a systematic and holistic view of the biochemical effects of realgar on rats, and might be employed to investigate other drugs or xenobiotics in the future.

Advances in Nutritional Metabolomics

Metabolomics is maturing as an experimental approach in nutrition science, and it is a useful analysis for revealing systems biology outcomes associated with changes in diet. A major goal of this review is to present the rapidly evolving body of scientific literature that seeks to reveal connections between an individual's metabolic profile and experimentally manipulated or naturally varied dietary intakes. Metabolite profiles in tissue, serum, urine, or stool reflect changes in metabolic pathways that respond to dietary intervention which makes them accessible samples for revealing metabolic effects of diet. Three broadly defined areas of investigation related to dietary-metabolomic strategies include: (1) describing the metabolite variation within and between dietary exposures or interventions; (2) characterizing the metabolic response to dietary interventions with respect to time; and (3) assessing individual variation in baseline nutritional health and/or disease status. An overview of metabolites that were responsive to dietary interventions as reported from original research in human or animal studies is provided and illustrates the breadth of metabolites affected by dietary intervention. Advantages and drawbacks for assessing metabolic changes are discussed in relation to types of metabolite analysis platforms. A combination of targeted and non-targeted global profiling studies as a component of future dietary intervention trials will increase our understanding of nutrition in a systems context.

Global metabolic profiling of animal and human tissues via UPLC-MS

Obtaining comprehensive, untargeted metabolic profiles for complex solid samples, e.g., animal tissues, requires sample preparation and access to information-rich analytical methodologies such as mass spectrometry (MS). Here we describe a practical two-step process for tissue samples that is based on extraction into 'aqueous' and 'organic' phases for polar and nonpolar metabolites. Separation methods such as ultraperformance liquid chromatography (UPLC) in combination with MS are needed to obtain sufficient resolution to create diagnostic metabolic profiles and identify candidate biomarkers. We provide detailed protocols for sample preparation, chromatographic procedures, multivariate analysis and metabolite identification via tandem MS (MS/MS) techniques and high-resolution MS. By using these optimized approaches, analysis of a set of samples using a 96-well plate format would take ~48 h: 1 h for system setup, 8-10 h for sample preparation, 34 h for UPLC-MS analysis and 2-3 h for preliminary/exploratory data processing, representing a robust method for untargeted metabolic screening of tissue samples.

Metabolomics and its potential in drug development

Metabolomics is the global and unbiased survey of the complement of small molecules (say, <1 kDa) in a biofluid, tissue, organ or organism and measures the end-products of the cellular metabolism of both endogenous and exogenous substrates. Many drug candidates fail during Phase II and III clinical trials at an enormous cost to the pharmaceutical industry in terms of both time lost and of financial resources. The constantly evolving model of drug development now dictates that biomarkers should be employed in preclinical development for the early detection of likely-to-fail candidates. Biomarkers may also be useful in the preselection of patients and through the subclassification of diseases in clinical drug development. Here we show with examples how metabolomics can assist in the preclinical development phases of discovery, pharmacology, toxicology, and ADME. Although not yet established as a clinical trial patient prescreening procedure, metabolomics shows considerable promise in this regard. We can be certain that metabolomics will join genomics and transcriptomics in lubricating the wheels of clinical drug development in the near future.

Systematic evaluation of extraction methods for multiplatform-based metabotyping: application to the Fasciola hepatica metabolome

Combining data from multiple analytical platforms is essential for comprehensive study of the molecular phenotype (metabotype) of a given biological sample. The metabolite profiles generated are intrinsically dependent on the analytical platforms, each requiring optimization of instrumental parameters, separation conditions, and sample extraction to deliver maximal biological information. An in-depth evaluation of extraction protocols for characterizing the metabolome of the hepatobiliary fluke Fasciola hepatica, using ultra performance liquid chromatography and capillary electrophoresis coupled with mass spectroscopy is presented. The spectrometric methods were characterized by performance, and metrics of merit were established, including precision, mass accuracy, selectivity, sensitivity, and platform stability. Although a core group of molecules was common to all methods, each platform contributed a unique set, whereby 142 metabolites out of 14,724 features were identified. A mixture design revealed that the chloroform:methanol:water proportion of 15:59:26 was globally the best composition for metabolite extraction across UPLC-MS and CE-MS platforms accommodating different columns and ionization modes. Despite the general assumption of the necessity of platform-adapted protocols for achieving effective metabotype characterization, we show that an appropriately designed single extraction procedure is able to fit the requirements of all technologies. This may constitute a paradigm shift in developing efficient protocols for high-throughput metabolite profiling with more-general analytical applicability.

Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry

The choice of sample-preparation method is extremely important in metabolomic studies because it affects both the observed metabolite content and biological interpretation of the data. An ideal sample-preparation method for global metabolomics should (i) be as non-selective as possible to ensure adequate depth of metabolite coverage; (ii) be simple and fast to prevent metabolite loss and/or degradation during the preparation procedure and enable high-throughput; (iii) be reproducible; and (iv) incorporate a metabolism-quenching step to represent true metabolome composition at the time of sampling. Despite its importance, sample preparation is often an overlooked aspect of metabolomics, so the focus of this review is to explore the role, challenges, and trends in sample preparation specifically within the context of global metabolomics by liquid chromatography-mass spectrometry (LC-MS). This review will cover the most common methods including solvent precipitation and extraction, solid-phase extraction and ultrafiltration, and discuss how to improve analytical quality and metabolite coverage in metabolomic studies of biofluids, tissues, and mammalian cells. Recent developments in this field will also be critically examined, including in vivo methods, turbulent-flow chromatography, and dried blood spot sampling.

Meta-analysis of untargeted metabolomic data from multiple profiling experiments

metaXCMS is a software program for the analysis of liquid chromatography/mass spectrometry-based untargeted metabolomic data. It is designed to identify the differences between metabolic profiles across multiple sample groups (e.g., 'healthy' versus 'active disease' versus 'inactive disease'). Although performing pairwise comparisons alone can provide physiologically relevant data, these experiments often result in hundreds of differences, and comparison with additional biologically meaningful sample groups can allow for substantial data reduction. By performing second-order (meta-) analysis, metaXCMS facilitates the prioritization of interesting metabolite features from large untargeted metabolomic data sets before the rate-limiting step of structural identification. Here we provide a detailed step-by-step protocol for going from raw mass spectrometry data to metaXCMS results, visualized as Venn diagrams and exported Microsoft Excel spreadsheets. There is no upper limit to the number of sample groups or individual samples that can be compared with the software, and data from most commercial mass spectrometers are supported. The speed of the analysis depends on computational resources and data volume, but will generally be less than 1 d for most users. metaXCMS is freely available at http://metlin.scripps.edu/metaxcms/.

UPLC-MS metabolic profiling of second trimester amniotic fluid and maternal urine and comparison with NMR spectral profiling for the identification of pregnancy disorder biomarkers

We report on the first untargeted UPLC-MS study of 2nd trimester maternal urine and amniotic fluid (AF), to investigate the possible metabolic effects of fetal malformations (FM), gestational diabetes mellitus (GDM) and preterm delivery (PTD). For fetal malformations, considerable metabolite variations were identified in AF and, to a lesser extent, in urine. Using validated PLS-DA models and statistical correlations between UPLC-MS data and previously acquired NMR data, a metabolic picture of fetal hypoxia, enhanced gluconeogenesis, TCA activity and hindered kidney development affecting FM pregnancies was reinforced. Moreover, changes in carnitine, pyroglutamate and polyols were newly noted, respectively, reflecting lipid oxidation, altered placental amino acid transfer and alterations in polyol pathways. Higher excretion of conjugated products in maternal urine was seen suggesting alterations in conjugation reactions. For the pre-diagnostic GDM group, no significant changes were observed, either considering amniotic fluid or maternal urine, whereas, for the pre-PTD group, some newly observed changes were noted, namely, the decrease of particular amino acids and the increase of an hexose (possibly glucose), suggesting alteration in placental amino acid fluxes and a possible tendency for hyperglycemia. This work shows the potential of UPLC-MS for the study of fetal and maternal biofluids, particularly when used in tandem with comparable NMR data. The important roles played by sampling characteristics (e.g. group dimensions) and the specific experimental conditions chosen for MS methods are discussed.

Standard operation protocol for analysis of lipid hydroperoxides in human serum using a fully automated method based on solid-phase extraction and liquid chromatography-mass spectrometry in selected reaction monitoring

Standard operating procedures (SOPs) are of paramount importance in the analytical field to ensure the reproducibility of the results obtained among laboratories. SOPs gain special interest when the aim is the analysis of potentially unstable compounds. An SOP for analysis of lipid hydroperoxides (HpETEs) is here reported after optimization of the critical steps to be considered in their analysis in human serum from sampling to final analysis. The method is based on automated hyphenation between solid-phase extraction (SPE) and liquid chromatography-mass spectrometry (LC-MS). The developed research involves: (i) optimization of the SPE and LC-MS steps with a proper synchronization; (ii) validation of the method-viz. accuracy study (estimated as 86.4% as minimum value), evaluation of sensitivity and precision, which ranged from 2.5 to 7.0 ng/mL (0.25-0.70 ng on column) as quantification limit and precision below 13.2%), and robustness study (reusability of the cartridge for 5 times without affecting the accuracy and precision of the method); (iii) stability study, involving freeze-thaw stability, short-term and long-term stability and stock solution stability tests. The results thus obtained allow minimizing both random and systematic variation of the metabolic profiles of the target compounds by correct application of the established protocol.