The use of directly coupled HPLC and NMR spectroscopy is described with illustrations using various pharmaceutical studies principally of drug metabolism. The additional coupling with MS is also exemplified. This is a specialized application of the more general approach of hyphenating chromatography with NMR spectroscopy as described elsewhere in the encyclopedia.

Second Edition of the Encyclopedia of Spectroscopy and Spectrometry pulls key information into a single source for quick access to answers and/or in-depth examination of topics. SPEC-2 covers theory, methods, and applications for researchers, students, and professionals - combining proven techniques and new insights for comprehensive coverage of the field. The content is available in print and online via ScienceDirect, the latter of which offers optimal flexibility, accessibility, and usability through anytime, anywhere access for multiple users and superior search functionality. No other work gives analytical and physical (bio)chemists such unprecedented access to the literature. With 30% new content, SPEC-2 maintains the 'authoritative, balanced coverage' of the original work while also breaking new ground in spectroscopic research. The key features of the book include: incorporates more than 150 color figures, 5,000 references, and 300 articles (30% of which are new), for a thorough examination of the field; highlights new research and promotes innovation in applied areas ranging from food science and forensics to biomedicine and health; and features a new co-editor: David Koppenaal of Pacific Northwest National Laboratory, Washington, USA, whose work in atomic mass spectrometry has been recognized internationally.

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.

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. (C) 2015 Elsevier Ireland Ltd. All rights reserved.

RATIONALEThe 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.METHODSTo 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‐d6)‐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.RESULTSMALDI‐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‐PLA2) 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‐d6)‐cholesterol within aortic plaque regions and showed distinct spatial accumulation of the cholesterol tracer.CONCLUSIONSThe 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. Copyright © 2014 John Wiley & Sons, Ltd.

MS interfaced with LC and ion mobility (IM) is routinely used to measure the level and variation of metabolites within biofluids as data generated through metabolomics studies may yield insight into disease onset and progression. LC-IM-MS based metabolomics generates large and complex data sets with analysis and interpretation of the results being the rate determining steps. This has led to a demand for improved data analysis, including processing and advanced multivariate approaches, which are described for the large scale analysis of metabolomics datasets. Urine from a healthy individual was centrifuged and the supernatant diluted. The urine was divided into control, low dosed (LD) and high dosed (HD) groups. To create a sample set, 11 different drugs were differentially spiked into LD and HD urine, contrasted with blank urine. A reversed phase gradient was applied and MS data acquired in positive ion data independent (LC-DIA-MS) and mobility assisted data independent (LC-IM-DIA-MS) modes. Distinguishing biological variation and metabolic change from analytical interference is key to data processing and analysis. Samples were randomized and measured six times, including QC runs, to ensure statistically valid analysis. LC-MS data were retention time aligned and deconvoluted to produce a feature list. Identified features were compound searched and interrogated with multivariate statistics to provide marker ions of interest. Relative high abundance levels of the standards were reported for LD and HD compared to controls, confirmed by trend plots analysis showing an increase in LD and HD groups compared to control. The standards were identified with an average score of 91 and mass error of 1.2 ppm. Three sample clusters were produced with the standards being the most differentiating features (top 20 based on q value) between groups. Functionality of the software will be demonstrated using biological samples.

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.

Idiopathic nephrotic syndrome (INS) is the most prevalent glomerular disease in children. In spite of progress, its pathogenesis is still unknown and the therapy options are confined to gross immune modulation. A variety of methods for diagnostic and treatment purposes are available for the patients; however, the lack of understanding regarding the pathogenic mechanisms underlying INS can lead to poor therapeutic response and adverse side-effects. Here, we describe quantitative multi-omics approach to reveal new molecular factors involved in pathogenesis of INS with potential diagnostic and therapeutic significance. Urine samples were collected from 10 children diagnosed with INS receiving no therapy and 10 healthy children. All samples were purified using spin filters followed by affinity depletion of albumin. The purified proteins were recovered and digested with trypsin overnight. Label-free protein expression data were acquired with Synapt G2 using an ion mobility data independent approach, whereby the collision energy was switched between low and elevated energy state during alternate scans and associate precursor and product ions by means of retention and drift time alignment. The acquired data were processed and searched against a human database that was amended to account for N-terminal processed peptides. Normalized label-free quantitation results were generated using TransOmics software. In a similar fashion the diluted neat urine samples were analysed using a small molecule profiling approach. The resulting data was also analyzed using TransOmics. Interpretation of the data has shown a significant number of proteins to be over-expressed in the urine from INS patients, which includes a high percentage of glycosylated proteins. Metabolites of interest showing statistically significant changes include homocysteine, glutamate and uridine. Pathway analysis of the complimentary datasets strongly suggests correlation with the neuronal system disorders network, specifically acute fatigue.

■ ■ Provides a single separation technique and a single instrumentation approach for rapid lipids analyses IN T RO DU C T IO N The analysis of complex lipids has historically been a challenging task and may require a variety of analytical techniques. Lipids are generally recognized as hydrophobic compounds, but the properties of complex lipids containing phosphorus, sulfur, sugar, and nitrogen have a wide polarity range. Developing separation-based methodology utilizing a single technique is recognized as a common challenge for scientists researching lipidomic and metabolomic applications.

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.

Targeted Metabolomics Using the UPLC/MS-based AbsoluteIDQ p180 Kit Evagelia C. Laiakis,1 Ralf Bogumil,2 Cornelia Roehring,2 Michael Daxboeck,2 Steven Lai,3 Marc Breit,2 John Shockcor,3 Steven Cohen,3 James Langridge,4 Albert J. Fornace Jr.,1 and Giuseppe Astarita1,3 1 Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington DC, USA 2 BIOCRATES Life Sciences AG, Innsbruck, Austria 3 Waters Corporation, Milford and Beverly, MA, USA 4 Waters Corporation, Manchester, UK

Lipids play essential roles in health and disease. The discovery of novel alterations in lipid levels related to human diseases could lead to the development of novel biomarkers and shed light on the etiology of many human diseases. The challenge with global lipid analysis is the chemical complexity and the large range of concentrations of thousands of lipid species that are present in biological samples. Here we present a robust workflow for global lipid profiling, which employs UPLC, ion mobility mass spectrometry and TransOmics software for high throughput discovery of lipid alterations in biological samples. Lipid extracts from biological matrices were separated using Charged Surface Hybrid (CSH) C18 UPLC according to acyl chain length and number of double bonds. Major lipid classes were identified using both positive and negative ESI. After chromatographic separation, lipids were analyzed using a hybrid QTOF system upgraded with an Ion Mobility Separation (IMS) cell capable separating lipids according to their size and shape. For example, differences in the acyl chain length or number of double bonds affect the shape and size of lipid molecules, resulting in characteristic migration times across the drift cell. Therefore, IMS provided an additional degree of separation besides chromatography, improving peak capacity and increasing selectivity over conventional UPLC. Data were analyzed using TransOmics software developed for visualization, processing, and interpretation of multi-dimensional MS data. In conclusion, we present a simple and robust solution for the high-throughput, automated identification of lipid alterations using novel analytical and informatics tools. The use of UPLC coupled to ion mobility provides multiple degrees of orthogonal separation, delivering unprecedented peak capacity required for the confident identification of lipid species in a biological mixture.

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.

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.

(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.

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.