Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping.

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

Label-free mass spectrometric profiling of urinary proteins and metabolites from paediatric idiopathic nephrotic syndrome.

Idiopathic nephrotic syndrome (INS) is caused by renal diseases that increase the permeability of the glomerular filtration barrier without evidence of a specific systemic cause. The aim of the present work was to reveal inherent molecular features of INS in children using combined urinary proteomics and metabolomics profiling. In this study, label-free mass spectrometric analysis of urinary proteins and small molecule metabolites was carried out in 12 patients with INS versus 12 sex- and age-matched control subjects with normal renal function. Integration and biological interpretation of obtained results were carried out by Ingenuity IPA software. Validation of obtained proteomics data was carried out by Western blot method. Proteomics data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000765. This study indicates for the first time that paediatric INS is associated with up-regulation of afamin, hydroxyphenylacetate and uridine, and concomitant down-regulation in glutamine and phenylalanine levels, and many of these molecular species were previously shown to be involved in oxidative stress. Further studies in larger patient population are underway to investigate the role of oxidative stress in renal injury in paediatric INS.

In vivo isotopically labeled atherosclerotic aorta plaques in ApoE KO mice and molecular profiling by matrix-assisted laser desorption/ionization mass spectrometric imaging.

RATIONALE: The ability to quantify rates of formation, regression and/or remodeling of atherosclerotic plaque should facilitate a better understanding of the pathogenesis and management of cardiovascular disease. In the current study, we coupled a stable isotope labeled tracer protocol with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to examine spatial and temporal lipid dynamics in atherosclerotic plaque. METHODS: To promote plaque formation in the aorta region, ApoE KO mice were fed a high cholesterol diet (0.15% cholesterol) and orally dosed with (2,2,3,4,4,6-d(6))-cholesterol over several weeks. Tissue sections of ~10 µm thickness were analyzed by MALDI-MSI using matrix deposition by either chemical sublimation or acoustic droplet ejection. RESULTS: MALDI-MSI yielded distinct spatial distribution information for a variety of lipid classes including specific lysophosphatidylcholines typically associated with atherosclerosis-related tissue damage such as phospholipase 2 (Lp-PLA(2)) that mediate chemotactic responses to inflammation (e.g. LPC 16:0, LPC 18:0 and LPC 18:1) as well as free cholesterol and cholesteryl esters that contribute to atheroma formation. MALDI mass spectra acquired from aorta tissue sections clearly distinguished non-esterified and esterified versions of (2,2,3,4,4,6-d(6))-cholesterol within aortic plaque regions and showed distinct spatial accumulation of the cholesterol tracer. CONCLUSIONS: The ability to couple stable isotope based protocols with MALDI-MSI enables a novel strategy to characterize the effects of therapeutic treatments on atherosclerotic plaque formation, regression and potential remodeling of the complex lipid components with high chemical specificity and spatiotemporal information.

Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach.

A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

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.

Use of an atmospheric solids analysis probe (ASAP) for high throughput screening of biological fluids: preliminary applications on urine and bile.

A hybrid quadrupole orthogonal time-of-flight mass spectrometer (QToF) equipped with a solids analysis probe (atmospheric solids analysis probe-mass spectrometry (ASAP-MS)) has been applied to the high throughput qualitative analysis of bile (rat and dog) and urine (rat) samples. The metabolic profiles generated by ASAP-MS was less comprehensive than that provided by liquid chromatography (LC) or gas chromatography-mass spectrometry (GC-MS) metabonomic profiling, though simple types of sample preparation were found to increase the range of ions detected for bile (a complex, multicompartment sample type). While unsuited to biomarker discovery, ASAP-MS of these biofluids generated sufficiently complex metabolic fingerprints to enable them to be distinguished from each other using multivariate statistical methods such as principal components analysis (PCA). This ability to provide an effective means of sample classification suggests possible diagnostic applications.

Comprehensive LC-MS E lipidomic analysis using a shotgun approach and its application to biomarker detection and identification in osteoarthritis patients.

A fast and robust method for lipid profiling utilizing liquid chromatography coupled with mass spectrometry has been demonstrated and validated for the analysis of human plasma. This method allowed quantification and identification of lipids in human plasma using parallel alternating low energy and high energy collision spectral acquisition modes. A total of 275 [corrected] lipids were identified and quantified (as relative concentrations) in both positive and negative ion electrospray ionization mode. The method was validated with five nonendogenous lipids, and the linearity (r(2) better than 0.994) and the intraday and interday repeatability (relative standard deviation, 4-6% and 5-8%, respectively) were satisfactory. The developed lipid profiling method was successfully applied for the analysis of plasma from osteoarthritis (OA) patients. The multivariate statistical analysis by partial least-squares-discrimination analysis suggested an altered lipid metabolism associated with osteoarthritis and the release of arachidonic acid from phospholipids.

A combined 1H-NMR spectroscopy- and mass spectrometry-based metabolomic study of the PPAR-alpha null mutant mouse defines profound systemic changes in metabolism linked to the metabolic syndrome.

The mobilization of triacylglycerides from storage in adipocytes to the liver is a vital response to the fasting state in mammalian metabolism. This is accompanied by a rapid translational activation of genes encoding mitochondrial, microsomal, and peroxisomal beta-oxidation in the liver, in part under the regulation of peroxisome proliferator-activated receptor-alpha (PPAR-alpha). A failure to express PPAR-alpha results in profound metabolic perturbations in muscle tissue as well as the liver. These changes represent a number of deficits that accompany diabetes, dyslipidemia, and the metabolic syndrome. In this study, the metabolic role of PPAR-alpha has been investigated in heart, skeletal muscle, liver, and adipose tissue of PPAR-alpha null mice at 1 mo of age using metabolomics. To maximize the coverage of the metabolome in these tissues, (1)H-NMR spectroscopy, magic angle spinning (1)H-NMR spectroscopy, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry were used to examine metabolites in aqueous tissue extracts and intact tissue. The data were analyzed by the multivariate approaches of principal components analysis and partial least squares. Across all tissues, there was a profound decrease in glucose and a number of amino acids, including glutamine and alanine, and an increase in lactate, demonstrating that a failure to express PPAR-alpha results in perturbations in glycolysis, the citric acid cycle, and gluconeogenesis. Furthermore, despite PPAR-alpha being weakly expressed in adipose tissue, a profound metabolic perturbation was detected in this tissue.

Perturbations in fatty acid metabolism and apoptosis are manifested in calcific coronary artery disease: An exploratory lipidomic study.

BACKGROUND: Controversy exists concerning the beneficial or harmful effects of the presence of ectopic calcification in the coronary arteries. Additionally, further elucidation of the exact pathophysiological mechanism is needed. In this study, we sought to identify metabolic markers of vascular calcification that could assist in understanding the disease, monitoring its progress and generating hypotheses describing its pathophysiology. METHODS: Untargeted lipid profiling and complementary modeling strategies were employed to compare serum samples from patients with different levels of calcific coronary artery disease (CCAD) based on their calcium score (CS). Subsequently, patients were divided into three groups: no calcification (NC; CS=0; n=26), mild calcification (MC; CS:1-250; n=27) and severe (SC; CS>250; n=17). RESULTS: Phosphatidylcholine levels were found to be significantly altered in the disease states (p=0.001-0.04). Specifically, 18-carbon fatty acyl chain (FAC) phosphatidylcholines were detected in lower levels in the SC group, while 20:4 FAC lipid species were detected in higher concentrations. A statistical trend was observed with phosphatidylcholine lipids in the MC group, showing the same tendency as with the SC group. We also observed several sphingomyelin signals present at lower intensities in SC when compared with NC or MC groups (p=0.000001-0.01). CONCLUSIONS: This is the first lipid profiling study reported in CCAD. Our data demonstrate dysregulations of phosphatidylcholine lipid species, which suggest perturbations in fatty acid elongation/desaturation. The altered levels of the 18-carbon and 20:4 FAC lipids may be indicative of disturbed inflammation homeostasis. The marked sphingomyelin dysregulation in SC is consistent with profound apoptosis as a potential mechanism of CCAD.

Metabonomic applications in toxicity screening and disease diagnosis.

Biofluid NMR spectroscopy is a powerful tool providing a comprehensive metabolic profile of the low molecular weight components in biofluids that reflect concentrations and fluxes of endogenous metabolites involved in key intermediary cellular pathways, thereby giving an indication of an organisms physiological or pathophysiological status [1]. The interaction of pharmacological agents with cells and tissues can also be monitored using recently developed high resolution magic-angle spinning (HRMAS) NMR spectroscopic technology for biological matrices [1]. However, recent developments in both spectrometer and software technology has resulted in improved capacity for sample handling, leading to a rapid growth in the size of toxicological spectral databases, and increased the complexity of the biological spectral data generated. Thus more emphasis has been placed on the need to develop improved automated procedures for data processing and interpretation. By harnessing chemometric tools for analysis of complex spectral data, the toxicological consequences of xenobiotic exposure can be evaluated efficiently on line. Automation of spectral processing procedures and the construction of mathematically based 'expert systems' for the prediction of drug-induced toxicity founded on IH NMR spectral profiles have now been achieved. Chemometric analysis of biological NMR spectra has provided the main analytical platform for metabonomic analysis, providing a systems approach to evaluating pathophysiological or genetic influences on the metabolic status of an organism [1]. This technology is currently being given high-priority in the pharmaceutical industry with respect to development of efficient high throughput toxicity screening systems for lead candidate selection. In this article, we review the recent developments in metabonomics and consider their application in toxicological screening, disease diagnosis and functional genomics.

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.

Application of combined omics platforms to accelerate biomedical discovery in diabesity.

Diabesity has become a popular term to describe the specific form of diabetes that develops late in life and is associated with obesity. While there is a correlation between diabetes and obesity, the association is not universally predictive. Defining the metabolic characteristics of obesity that lead to diabetes, and how obese individuals who develop diabetes different from those who do not, are important goals. The use of large-scale omics analyses (e.g., metabolomic, proteomic, transcriptomic, and lipidomic) of diabetes and obesity may help to identify new targets to treat these conditions. This report discusses how various types of omics data can be integrated to shed light on the changes in metabolism that occur in obesity and diabetes.

The lipid composition of isolated cytoplasmic lipid droplets from a human cancer cell line, BE(2)M17.

(1)H nuclear magnetic resonance spectroscopy (NMR) resonances from lipids in tumours are associated with tumour grade and treatment response. The origin of these NMR signals is mainly considered to be cytoplasmic lipid droplets (LDs). Techniques exist for isolating LDs but little is known about their composition and its relationship to NMR signals. In this work, density-gradient ultracentrifugation was performed on homogenised human cancer cells to isolate LDs. (1)H NMR was performed on whole cells, isolated LDs and their extracts. Heteronuclear single quantum coherence spectroscopy (HSQC) and liquid chromatography mass spectroscopy (LC-MS) were performed on lipid extracts of LDs. Staining and microscopy were used to characterize isolated LDs. An excellent agreement in chemical shift and relative signal intensity was observed between lipid resonances in cells and isolated LD spectra supporting that NMR-visible lipids originate primarily from LDs. Isolated LDs showed high concentrations of unsaturated lipids, a oleic-to-linoleic acid ratio greater than two and a cholesteryl ester (ChE)-to-cholesterol (Ch) ratio close to unity. These ratios were several-fold greater than respective ratios in whole cells, demonstrating isolation is important to characterize LD composition. LDs contain a specific group of lipid species that are likely to contribute to the (1)H NMR spectrum of cells.

Metabolomics as a tool for cardiac research.

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.

H NMR spectroscopy-based metabolomic assessment of uremic toxicity, with toxicological outcomes, in male rats following an acute, mid-life insult from ochratoxin a.

Overt response to a single 6.25 mg dose of ochratoxin A (OTA) by oral gavage to 15 months male rats was progressive loss of weight during the following four days. Lost weight was restored within one month and animals had a normal life-span without OTA-related terminal disease. Decline in plasma OTA concentration only commenced four days after dosing, while urinary excretion of OTA and ochratoxin alpha was ongoing. During a temporary period of acute polyuria, a linear relationship between urine output and creatinine concentration persisted. Elimination of other common urinary solutes relative to creatinine was generally maintained during the polyuria phase, except that phosphate excretion increased temporarily. (1)H NMR metabolomic analysis of urine revealed a progressive cyclic shift in the group principal components data cluster from before dosing, throughout the acute insult phase, and returning almost completely to normality when tested six months later. Renal insult by OTA was detected by (1)H NMR within a day of dosing, as the most sensitive early indicator. Notable biomarkers were trimethylamine N-oxide and an aromatic urinary profile dominated by phenylacetylglycine. Tolerance of such a large acute insult by OTA, assessed by rat natural lifetime outcomes, adds a new dimension to toxicology of this xenobiotic.

Global metabolic profiling procedures for urine using UPLC-MS.

The production of 'global' metabolite profiles involves measuring low molecular-weight metabolites (<1 kDa) in complex biofluids/tissues to study perturbations in response to physiological challenges, toxic insults or disease processes. Information-rich analytical platforms, such as mass spectrometry (MS), are needed. Here we describe the application of ultra-performance liquid chromatography-MS (UPLC-MS) to urinary metabolite profiling, including sample preparation, stability/storage and the selection of chromatographic conditions that balance metabolome coverage, chromatographic resolution and throughput. We discuss quality control and metabolite identification, as well as provide details of multivariate data analysis approaches for analyzing such MS data. Using this protocol, the analysis of a sample set in 96-well plate format, would take ca. 30 h, including 1 h for system setup, 1-2 h for sample preparation, 24 h for UPLC-MS analysis and 1-2 h for initial data processing. The use of UPLC-MS for metabolic profiling in this way is not faster than the conventional HPLC-based methods but, because of improved chromatographic performance, provides superior metabolome coverage.

omega-3 oil intake during weight loss in obese women results in remodelling of plasma triglyceride and fatty acids.

Previous studies have shown that a combination of weight loss and fish oil supplementation reduce cardiovascular disease and diabetes risks by increasing adiponectin and reducing triacylglyceride concentrations, while weight loss alone significantly improves insulin sensitivity and reduces inflammation. Here, a metabolomic approach, using a combination of (1)H-Nuclear Magnetic Resonance spectroscopy, and gas and liquid chromatography and mass spectrometry, was employed to elucidate the metabolic changes in blood plasma following weight loss and fish oil supplementation. The intervention study was conducted over 24 weeks, with 93 female subjects randomised to one of three groups. Two groups followed a 12-week weight loss program, followed by a 12-week weight maintenance period and were randomised to fish or placebo oil capsules; a control group did not follow the weight loss program and were given placebo oil capsules. Lipid profiles changed dramatically upon fish oil intake and subtly across the two weight loss groups. While the fish oil supplementation increased the proportion of various phospholipid species, previously reported reductions in total triacylglycerides (TAGs) upon fish oil intake were shown to be driven by a reduction in a specific subset of the measured TAGs. This remodelling of triglycerides may represent further beneficial effects of fish oil supplementation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0161-7) contains supplementary material, which is available to authorized users.

Generic dealkylation: a tool for increasing the hit-rate of metabolite rationalization, and automatic customization of mass defect filters.

The use of exact mass liquid chromatography/mass spectrometry (LC/MS) for drug metabolism studies has increased significantly in recent years. Firstly, exact mass measurements facilitate identification of standard biotransformations through the use of narrow window extracted ion chromatograms, which are typically highly selective relative to signals from matrix or dosing components. Secondly, novel metabolites can be characterized via elemental formula calculations and high-resolution product ion spectra. Furthermore, biological background ions can be removed by the use of mass defect filters (MDFs) which filter out ions based on the decimal component of their m/z value. Here, we describe an approach which we term 'generic dealkylation' that in association with other data interpretation tools adds significant value to the assignment process. Generic dealkylation uses a simple strategy to identify those bonds which have the potential to be cleaved by metabolism. In combination with standard phase 1 and phase 2 biotransformations, this allows creation of a chemically intelligent MDF which balances the need to remove matrix background with the requirement of avoiding filtering true metabolites. Secondly, generic dealkylation increases the hit-rate at which non-trivial (i.e. not covered by simple phase 1 oxidations or direct phase 2 conjugations) metabolites can be directly rationalized. The value of the generic dealkylation approach is illustrated by its application to determination of in vitro metabolic routes for two commercial drugs, nefazodone and indinavir.

Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models.

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.