Combining desorption electrospray ionization mass spectrometry and nuclear magnetic resonance for differential metabolomics without sample preparation

Multimodal Imaging of Amyloid Plaques: Fusion of the Single-Probe Mass Spectrometry Image and Fluorescence Microscopy Image

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. The formation of amyloid plaques by aggregated amyloid beta (Aβ) peptides is a primary event in AD pathology. Understanding the metabolomic features and related pathways is critical for studying plaque-related pathological events (e.g., cell death and neuron dysfunction). Mass spectrometry imaging (MSI), due to its high sensitivity and ability to obtain the spatial distribution of metabolites, has been applied to AD studies. However, limited studies of metabolites in amyloid plaques have been performed due to the drawbacks of the commonly used techniques such as matrix-assisted laser desorption/ionization MSI. In the current study, we obtained high spatial resolution (∼17 μm) MS images of the AD mouse brain using the Single-probe, a microscale sampling and ionization device, coupled to a mass spectrometer under ambient conditions. The adjacent slices were used to obtain fluorescence microscopy images to locate amyloid plaques. The MS image and the fluorescence microscopy image were fused to spatially correlate histological protein hallmarks with metabolomic features. The fused images produced significantly improved spatial resolution (∼5 μm), allowing for the determination of fine structures in MS images and metabolomic biomarkers representing amyloid plaques.

Ambient mass spectrometry in metabolomics

Since the introduction of desorption electrospray ionization (DESI) mass spectrometry (MS), ambient MS methods have seen increased use in a variety of fields from health to food science. Increasing its popularity in metabolomics, ambient MS offers limited sample preparation, rapid and direct analysis of liquids, solids, and gases, in situ and in vivo analysis, and imaging. The metabolome consists of a constantly changing collection of small (<1.5 kDa) molecules. These include endogenous molecules that are part of primary metabolism pathways, secondary metabolites with specific functions such as signaling, chemicals incorporated in the diet or resulting from environmental exposures, and metabolites associated with the microbiome. Characterization of the responsive changes of this molecule cohort is the principal goal of any metabolomics study. With adjustments to experimental parameters, metabolites with a range of chemical and physical properties can be selectively desorbed and ionized and subsequently analyzed with increased speed and sensitivity. This review covers the broad applications of a variety of ambient MS techniques in four primary fields in which metabolomics is commonly employed.

Beyond the paradigm: Combining mass spectrometry and nuclear magnetic resonance for metabolomics

Metabolomics is undergoing tremendous growth and is being employed to solve a diversity of biological problems from environmental issues to the identification of biomarkers for human diseases. Nuclear magnetic resonance (NMR) and mass spectrometry (MS) are the analytical tools that are routinely, but separately, used to obtain metabolomics data sets due to their versatility, accessibility, and unique strengths. NMR requires minimal sample handling without the need for chromatography, is easily quantitative, and provides multiple means of metabolite identification, but is limited to detecting the most abundant metabolites (⩾1μM). Conversely, mass spectrometry has the ability to measure metabolites at very low concentrations (femtomolar to attomolar) and has a higher resolution (∼10³-10⁴) and dynamic range (∼10³-10⁴), but quantitation is a challenge and sample complexity may limit metabolite detection because of ion suppression. Consequently, liquid chromatography (LC) or gas chromatography (GC) is commonly employed in conjunction with MS, but this may lead to other sources of error. As a result, NMR and mass spectrometry are highly complementary, and combining the two techniques is likely to improve the overall quality of a study and enhance the coverage of the metabolome. While the majority of metabolomic studies use a single analytical source, there is a growing appreciation of the inherent value of combining NMR and MS for metabolomics. An overview of the current state of utilizing both NMR and MS for metabolomics will be presented.

Direct infusion mass spectrometry for metabolomic phenotyping of diseases

Metabolomics based on direct mass spectrometry (MS) analysis, either by direct infusion or flow injection of crude sample extracts, shows a great potential for metabolic fingerprinting because of its high-throughput screening capability, wide metabolite coverage and reduced time of analysis. Considering that numerous metabolic pathways are significantly perturbed during the initiation and progression of diseases, these metabolomic tools can be used to get a deeper understanding about disease pathogenesis and discover potential biomarkers for early diagnosis. In this work, we describe the most common metabolomic platforms used in biomedical research, with special focus on strategies based on direct MS analysis. Then, a comprehensive review on the application of direct MS fingerprinting in clinical issues is provided.

Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine

We propose a new method for the rapid determination of five volatile compounds described in the literature as possible biomarkers of lung cancer in urine samples. The method is based on the coupling of a headspace sampler, a programmed temperature vaporizer in solvent-vent injection mode, and a mass spectrometer (HS-PTV-MS). This configuration is known as an electronic nose based on mass spectrometry. Once the method was developed, it was used for the analysis of urine samples from lung cancer patients and healthy individuals. Multivariate calibration models were employed to quantify the biomarker concentrations in the samples. The detection limits ranged between 0.16 and 21 μg/L. For the assignment of the samples to the patient group or the healthy individuals, the Wilcoxon signed-rank test was used, comparing the concentrations obtained with the median of a reference set of healthy individuals. To date, this is the first time that multivariate calibration and non-parametric methods have been combined to classify biological samples from profile signals obtained with an electronic nose. When significant differences in the concentration of one or more biomarkers were found with respect to the reference set, the sample is considered as a positive one and a new analysis was performed using a chromatographic method (HS-PTV-GC/MS) to confirm the result. The main advantage of the proposed HS-PTV-MS methodology is that no prior chromatographic separation and no sample manipulation are required, which allows an increase of the number of samples analyzed per hour and restricts the use of time-consuming techniques to only when necessary. Graphical abstract Schematic diagram of the developed methodology.

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

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

Combining DI-ESI-MS and NMR datasets for metabolic profiling

Metabolomics datasets are commonly acquired by either mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR), despite their fundamental complementarity. In fact, combining MS and NMR datasets greatly improves the coverage of the metabolome and enhances the accuracy of metabolite identification, providing a detailed and high-throughput analysis of metabolic changes due to disease, drug treatment, or a variety of other environmental stimuli. Ideally, a single metabolomics sample would be simultaneously used for both MS and NMR analyses, minimizing the potential for variability between the two datasets. This necessitates the optimization of sample preparation, data collection and data handling protocols to effectively integrate direct-infusion MS data with one-dimensional (1D) 1H NMR spectra. To achieve this goal, we report for the first time the optimization of (i) metabolomics sample preparation for dual analysis by NMR and MS, (ii) high throughput, positive-ion direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) for the analysis of complex metabolite mixtures, and (iii) data handling protocols to simultaneously analyze DI-ESI-MS and 1D 1H NMR spectral data using multiblock bilinear factorizations, namely multiblock principal component analysis (MB-PCA) and multiblock partial least squares (MB-PLS). Finally, we demonstrate the combined use of backscaled loadings, accurate mass measurements and tandem MS experiments to identify metabolites significantly contributing to class separation in MB-PLS-DA scores. We show that integration of NMR and DI-ESI-MS datasets yields a substantial improvement in the analysis of neurotoxin involvement in dopaminergic cell death.

Mass spectral similarity for untargeted metabolomics data analysis of complex mixtures

While in nucleotide sequencing, the analysis of DNA from complex mixtures of organisms is common, this is not yet true for mass spectrometric data analysis of complex mixtures. The comparative analyses of mass spectrometry data of microbial communities at the molecular level is difficult to perform, especially in the context of a host. The challenge does not lie in generating the mass spectrometry data, rather much of the difficulty falls in the realm of how to derive relevant information from this data. The informatics based techniques to visualize and organize datasets are well established for metagenome sequencing; however, due to the scarcity of informatics strategies in mass spectrometry, it is currently difficult to cross correlate two very different mass spectrometry data sets from microbial communities and their hosts. We highlight that molecular networking can be used as an organizational tool of tandem mass spectrometry data, automated database search for rapid identification of metabolites, and as a workflow to manage and compare mass spectrometry data from complex mixtures of organisms. To demonstrate this platform, we show data analysis from hard corals and a human lung associated with cystic fibrosis.

Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Metabolomics aims at the comprehensive assessment of a wide range of endogenous metabolites and attempts to identify and quantify the attractive metabolites in a given biological sample.

Alterations in energy/redox metabolism induced by mitochondrial and environmental toxins: a specific role for glucose-6-phosphate-dehydrogenase and the pentose phosphate pathway in paraquat toxicity

Parkinson’s disease (PD) is a multifactorial disorder with a complex etiology including genetic risk factors, environmental exposures, and aging. While energy failure and oxidative stress have largely been associated with the loss of dopaminergic cells in PD and the toxicity induced by mitochondrial/environmental toxins, very little is known regarding the alterations in energy metabolism associated with mitochondrial dysfunction and their causative role in cell death progression. In this study, we investigated the alterations in the energy/redox-metabolome in dopaminergic cells exposed to environmental/mitochondrial toxins (paraquat, rotenone, 1-methyl-4-phenylpyridinium [MPP⁺], and 6-hydroxydopamine [6-OHDA]) in order to identify common and/or different mechanisms of toxicity. A combined metabolomics approach using nuclear magnetic resonance (NMR) and direct-infusion electrospray ionization mass spectrometry (DI-ESI-MS) was used to identify unique metabolic profile changes in response to these neurotoxins. Paraquat exposure induced the most profound alterations in the pentose phosphate pathway (PPP) metabolome. ¹³C-glucose flux analysis corroborated that PPP metabolites such as glucose-6-phosphate, fructose-6-phosphate, glucono-1,5-lactone, and erythrose-4-phosphate were increased by paraquat treatment, which was paralleled by inhibition of glycolysis and the TCA cycle. Proteomic analysis also found an increase in the expression of glucose-6-phosphate dehydrogenase (G6PD), which supplies reducing equivalents by regenerating nicotinamide adenine dinucleotide phosphate (NADPH) levels. Overexpression of G6PD selectively increased paraquat toxicity, while its inhibition with 6-aminonicotinamide inhibited paraquat-induced oxidative stress and cell death. These results suggest that paraquat “hijacks” the PPP to increase NADPH reducing equivalents and stimulate paraquat redox cycling, oxidative stress, and cell death. Our study clearly demonstrates that alterations in energy metabolism, which are specific for distinct mitochondiral/environmental toxins, are not bystanders to energy failure but also contribute significant to cell death progression.

Epidemiology of lung cancer: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines

BACKGROUND

Ever since a lung cancer epidemic emerged in the mid-1900 s, the epidemiology of lung cancer has been intensively investigated to characterize its causes and patterns of occurrence. This report summarizes the key findings of this research.

METHODS

A detailed literature search provided the basis for a narrative review, identifying and summarizing key reports on population patterns and factors that affect lung cancer risk.

RESULTS

Established environmental risk factors for lung cancer include smoking cigarettes and other tobacco products and exposure to secondhand tobacco smoke, occupational lung carcinogens, radiation, and indoor and outdoor air pollution. Cigarette smoking is the predominant cause of lung cancer and the leading worldwide cause of cancer death. Smoking prevalence in developing nations has increased, starting new lung cancer epidemics in these nations. A positive family history and acquired lung disease are examples of host factors that are clinically useful risk indicators. Risk prediction models based on lung cancer risk factors have been developed, but further refinement is needed to provide clinically useful risk stratification. Promising biomarkers of lung cancer risk and early detection have been identified, but none are ready for broad clinical application.

CONCLUSIONS

Almost all lung cancer deaths are caused by cigarette smoking, underscoring the need for ongoing efforts at tobacco control throughout the world. Further research is needed into the reasons underlying lung cancer disparities, the causes of lung cancer in never smokers, the potential role of HIV in lung carcinogenesis, and the development of biomarkers.

Sensitive ionization of non-volatile analytes using protein solutions as spray liquid in desorption electrospray ionization mass spectrometry

RATIONALE

Desorption electrospray ionization (DESI) is the most popular ambient ionization technique for direct analysis of complex samples without sample pretreatment. However, for many applications, especially for trace analysis, it is of interest to improve the sensitivity of DESI-mass spectrometry (MS).

METHODS

In traditional DESI-MS, a mixture of methanol/water/acetic acid is usually used to generate the primary ions. In this article, dilute protein solutions were electrosprayed in the DESI method to create multiply charged primary ions for the desorption ionization of trace analytes on various surfaces (e.g., filter paper, glass, Al-foil) without any sample pretreatment. The analyte ions were then detected and structurally characterized using a LTQ XL mass spectrometer.

RESULTS

Compared with the methanol/water/acetic acid (49:49:2, v/v/v) solution, protein solutions significantly increased the signal levels of non-volatile compounds such as benzoic acid, TNT, o-toluidine, peptide and insulin in either positive or negative ion detection mode. For all the analytes tested, the limits of detection (LODs) were reduced to about half of the original values which were obtained using traditional DESI. The results showed that the signal enhancement is highly correlated with the molecular weight of the proteins and the selected solid surfaces.

CONCLUSIONS

The proposed DESI method is a universal strategy for rapid and sensitive detection of trace amounts of strongly bound and/or non-volatile analytes, including explosives, peptides, and proteins. The results indicate that the sensitivity of DESI can be further improved by selecting larger proteins and appropriate solid surfaces.

Systematic applications of metabolomics in metabolic engineering

The goals of metabolic engineering are well-served by the biological information provided by metabolomics: information on how the cell is currently using its biochemical resources is perhaps one of the best ways to inform strategies to engineer a cell to produce a target compound. Using the analysis of extracellular or intracellular levels of the target compound (or a few closely related molecules) to drive metabolic engineering is quite common. However, there is surprisingly little systematic use of metabolomics datasets, which simultaneously measure hundreds of metabolites rather than just a few, for that same purpose. Here, we review the most common systematic approaches to integrating metabolite data with metabolic engineering, with emphasis on existing efforts to use whole-metabolome datasets. We then review some of the most common approaches for computational modeling of cell-wide metabolism, including constraint-based models, and discuss current computational approaches that explicitly use metabolomics data. We conclude with discussion of the broader potential of computational approaches that systematically use metabolomics data to drive metabolic engineering.

Biofluid metabonomics using (1)H NMR spectroscopy: the road to biomarker discovery in gastroenterology and hepatology

Metabolic profiling or 'metabonomics' is an investigatory method that allows metabolic changes associated with the presence of an underlying pathological process to be investigated. Various biofluids can be utilized in the process but urine, serum and fecal extract are most pertinent to the investigation of gastrointestinal and hepatological disease. Nuclear magnetic resonance spectroscopy-based metabonomic research has the potential to generate novel noninvasive diagnostic tests, based on biomarkers of disease, which are simple and cost effective yet retain high sensitivity and specificity characteristics. The process involves a number of key steps, including sample collection, data acquisition, chemometric techniques and, finally, validation. This technique-driven review aims to demystify the metabonomics pathway, while also illustrating the potential of this technique with recent examples of its application in hepato-gastroenterological disease.

Liquid-phase microextraction and desorption electrospray ionization mass spectrometry for identification and quantification of basic drugs in human urine

Hollow fibre liquid-phase microextraction (LPME) and desorption electrospray ionization mass spectrometry (DESI-MS) were evaluated for the identification and quantification of basic drugs in human urine samples. The selective extraction capabilities of three-phase LPME provided a significant reduction in the matrix effects otherwise observed in direct DESI-MS analysis of urine samples. Aqueous LPME extracts (in 10 mM HCl) were deposited on porous Teflon, dried at room temperature, and the dried spots were then analyzed directly with DESI-MS in full scan mode. Pethidine, diphenhydramine, nortriptyline, and methadone were used as model compounds for identification, and their limits of identification were determined to be 100, 25, 100, and 30 ng/mL, respectively. In a reliability test with 19 spiked urine samples, 100% of the positive samples containing the model drugs in concentrations at or above the limit of identification were identified. Diphenhydramine was used as a model compound for quantitative analysis with diphenhydramine-d(5) as an internal standard. The calibration curve was linear in the range 50-2000 ng/mL (R(2) = 0.992) with a limit of quantification at approximately 140 ng/mL. The intra- and inter-day relative standard deviations were <9.5%. In a reliability test with six spiked urine samples, deviations between the measured and the true values for diphenhydramine were in the range 0.2-22.9%.

Foodomics: MS-based strategies in modern food science and nutrition

Modern research in food science and nutrition is moving from classical methodologies to advanced analytical strategies in which MS-based techniques play a crucial role. In this context, Foodomics has been recently defined as a new discipline that studies food and nutrition domains through the application of advanced omics technologies in which MS techniques are considered indispensable. Applications of Foodomics include the genomic, transcriptomic, proteomic, and/or metabolomic study of foods for compound profiling, authenticity, and/or biomarker-detection related to food quality or safety; the development of new transgenic foods, food contaminants, and whole toxicity studies; new investigations on food bioactivity, food effects on human health, etc. This review work does not intend to provide an exhaustive revision of the many works published so far on food analysis using MS techniques. The aim of the present work is to provide an overview of the different MS-based strategies that have been (or can be) applied in the new field of Foodomics, discussing their advantages and drawbacks. Besides, some ideas about the foreseen development and applications of MS-techniques in this new discipline are also provided.

Liquid chromatography-mass spectrometry based global metabolite profiling: a review

Untargeted, global metabolite profiling (often described as metabonomics or metabolomics) represents an expanding research topic and is, potentially, a major pillar for systems biology studies. To obtain holistic metabolic profiles from complex samples, such as biological fluids or tissue extracts, requires powerful, high resolution and information-rich analytical methods and for this spectroscopic technologies are generally used. Mass spectrometry, coupled to liquid chromatography (LC-MS), is increasingly being used for such investigations as a result of the significant advances in both technologies over the past decade. Here we try to critically review the topic of LC-MS-based global metabolic profiling and describe and compare the results offered by different analytical strategies and technologies. This review highlights the current challenges, limitations and opportunities of the current methodology.

Development of stereotactic mass spectrometry for brain tumor surgery

BACKGROUND

Surgery remains the first and most important treatment modality for the majority of solid tumors. Across a range of brain tumor types and grades, postoperative residual tumor has a great impact on prognosis. The principal challenge and objective of neurosurgical intervention is therefore to maximize tumor resection while minimizing the potential for neurological deficit by preserving critical tissue.

OBJECTIVE

To introduce the integration of desorption electrospray ionization mass spectrometry into surgery for in vivo molecular tissue characterization and intraoperative definition of tumor boundaries without systemic injection of contrast agents.

METHODS

Using a frameless stereotactic sampling approach and by integrating a 3-dimensional navigation system with an ultrasonic surgical probe, we obtained image-registered surgical specimens. The samples were analyzed with ambient desorption/ionization mass spectrometry and validated against standard histopathology. This new approach will enable neurosurgeons to detect tumor infiltration of the normal brain intraoperatively with mass spectrometry and to obtain spatially resolved molecular tissue characterization without any exogenous agent and with high sensitivity and specificity.

RESULTS

Proof of concept is presented in using mass spectrometry intraoperatively for real-time measurement of molecular structure and using that tissue characterization method to detect tumor boundaries. Multiple sampling sites within the tumor mass were defined for a patient with a recurrent left frontal oligodendroglioma, World Health Organization grade II with chromosome 1p/19q codeletion, and mass spectrometry data indicated a correlation between lipid constitution and tumor cell prevalence.

CONCLUSION

The mass spectrometry measurements reflect a complex molecular structure and are integrated with frameless stereotaxy and imaging, providing 3-dimensional molecular imaging without systemic injection of any agents, which can be implemented for surgical margins delineation of any organ and with a rapidity that allows real-time analysis.

Sinapine detection in radish taproot using surface desorption atmospheric pressure chemical ionization mass spectrometry

Plant research and natural product detection are of sustainable interests. Benefited by direct detection with no sample preparation, sinapine, a bioactive chemical usually found in various seeds of Brassica plants, has been unambiguously detected in radish taproot (Raphanus sativus) tissue using a liquid-assisted surface desorption atmospheric pressure chemical ionization mass spectrometry (DAPCI-MS). A methanol aqueous solution (1:1) was nebulized by a nitrogen sheath gas toward the corona discharge, resulting in charged ambient small droplets, which affected the radish tissue for desorption/ionization of analytes on the tissue surface. Thus, sinapine was directly detected and identified by tandem DAPCI-MS experiments without sample pretreatment. The typical relative standard deviation (RSD) of this method for sinapine detection was 5-8% for six measurements (S/N=3). The dynamic response range was 10(-12)-10(-7) g/cm2 for sinapine on the radish skin surface. The discovery of sinapine in radish taproot was validated by using HPLC-UV methods. The data demonstrated that DAPCI assisted by solvent enhanced the overall efficiency of the desorption/ionization process, enabling sensitive detection of bioactive compounds in plant tissue.

Identification and quantification of metabolites in (1)H NMR spectra by Bayesian model selection

MOTIVATION

Nuclear magnetic resonance (NMR) spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, accurate interpretation of the spectra in terms of identities and abundances of metabolites can be challenging, in particular in crowded regions with heavy peak overlap. Although a number of computational approaches for this task have recently been proposed, they are not entirely satisfactory in either accuracy or extent of automation.

RESULTS

We introduce a probabilistic approach Bayesian Quantification (BQuant), for fully automated database-based identification and quantification of metabolites in local regions of (1)H NMR spectra. The approach represents the spectra as mixtures of reference profiles from a database, and infers the identities and the abundances of metabolites by Bayesian model selection. We show using a simulated dataset, a spike-in experiment and a metabolomic investigation of plasma samples that BQuant outperforms the available automated alternatives in accuracy for both identification and quantification.

AVAILABILITY

The R package BQuant is available at: http://www.stat.purdue.edu/~ovitek/BQuant-Web/.

Principal component directed partial least squares analysis for combining nuclear magnetic resonance and mass spectrometry data in metabolomics: application to the detection of breast cancer

Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) are the two most commonly used analytical tools in metabolomics, and their complementary nature makes the combination particularly attractive. A combined analytical approach can improve the potential for providing reliable methods to detect metabolic profile alterations in biofluids or tissues caused by disease, toxicity, etc. In this paper, (1)H NMR spectroscopy and direct analysis in real time (DART)-MS were used for the metabolomics analysis of serum samples from breast cancer patients and healthy controls. Principal component analysis (PCA) of the NMR data showed that the first principal component (PC1) scores could be used to separate cancer from normal samples. However, no such obvious clustering could be observed in the PCA score plot of DART-MS data, even though DART-MS can provide a rich and informative metabolic profile. Using a modified multivariate statistical approach, the DART-MS data were then reevaluated by orthogonal signal correction (OSC) pretreated partial least squares (PLS), in which the Y matrix in the regression was set to the PC1 score values from the NMR data analysis. This approach, and a similar one using the first latent variable from PLS-DA of the NMR data resulted in a significant improvement of the separation between the disease samples and normals, and a metabolic profile related to breast cancer could be extracted from DART-MS. The new approach allows the disease classification to be expressed on a continuum as opposed to a binary scale and thus better represents the disease and healthy classifications. An improved metabolic profile obtained by combining MS and NMR by this approach may be useful to achieve more accurate disease detection and gain more insight regarding disease mechanisms and biology.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC-MS system

Lung cancer is one of the most common and lethal cancers in the world. In this study, a home-devised hydrophilic interaction chromatography/RPLC-MS (HILIC/RPLC-MS) system was developed to study the urinary metabonomics of lung cancer patients. This system combined the orthogonal selectivity of HILIC and RPLC and could chromatographically reveal more comprehensive information of the urinary metabolites. Within a total analysis time of 50 min, we detected 577 polar metabolite ions on the first HILIC column and 261 apolar ones on the second RPLC column. In addition, an orthogonal signal correction partial least-squares discriminant analysis model was constructed to characterize differences between health and lung cancer cases. Eleven potential biomarkers, ten from HILIC column and one from the second RP column, were identified and all of these biomarkers were found upregulated in lung cancer patients. Overall, the results indicated that the developed HILIC/RPLC-MS system is a promising tool for metabonomic studies in revealing more information of highly complex samples.

On the use of electrospray ionization and desorption electrospray ionization mass spectrometry for bulk and surface polymer analysis

Electrospray ionization (ESI) and desorption electrospray ionization (DESI) mass spectrometry were for the first time compared to investigate degradation products resulting from radiolysis of polyurethane (PUR) irradiated using accelerated electrons. First, DESI optimization was performed to obtain the best results in terms of sensitivity for surface polymer analysis. Then, a comparison was performed between DESI-MS and ESI-MS characterization of the degradation products of irradiated PUR with different doses up to 10 MGy. Similar results were obtained despite a lower sensitivity of DESI. Finally, DESI results obtained by direct surface analysis of irradiated PUR films as a function of depth were compared to bulk analysis results obtained by ESI. This comparison shows that degradation products of PUR are very comparable at the surface or within the polymer, confirming the radio-oxidation homogeneity.

Ambient ionization mass spectrometry: current understanding of mechanistic theory; analytical performance and application areas

Ambient ionization mass spectrometry allows the rapid analysis of samples or objects in their native state in the open environment with no prior preparation. Over the past six years, the ability of these techniques to provide selective analyte desorption and ionization, in combination with mass spectrometry (MS), has provided a growing number of powerful analytical alternatives across broad application areas, both quantitative and qualitative in nature, including pharmaceutical analysis, process chemistry, biological imaging, in vivo analysis, proteomics, metabolomics, forensics, and explosives detection. With the emergence of new ambient ionization methods, and the complementary nature of existing desorption and/or ionization techniques, additional hyphenated methods have been devised, which pushes the total number of documented methods to almost thirty. To cover all current ambient ionization techniques in detail would be too complex and detract from the main objective of this review. Rather, an overview of the field of ambient ionization MS will be given, followed by broad classification to allow detailed discussion of theory and common mechanistic factors underpinning a number of key techniques. Consideration will be given to experimental design, ease of implementation and analytical performance, detailing subsequent impact on a number of application areas, both established and emerging.

NMR metabolomics and drug discovery

NMR is an integral component of the drug discovery process with applications in lead discovery, validation, and optimization. NMR is routinely used for fragment-based ligand affinity screens, high-resolution protein structure determination, and rapid protein-ligand co-structure modeling. Because of this inherent versatility, NMR is currently making significant contributions in the burgeoning area of metabolomics, where NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

1H NMR metabolomics study of age profiling in children

Metabolic profiling of urine provides a fingerprint of personalized endogenous metabolite markers that correlate to a number of factors such as gender, disease, diet, toxicity, medication, and age. It is important to study these factors individually, if possible to unravel their unique contributions. In this study, age-related metabolic changes in children of age 12 years and below were analyzed by (1)H NMR spectroscopy of urine. The effect of age on the urinary metabolite profile was observed as a distinct age-dependent clustering even from the unsupervised principal component analysis. Further analysis, using partial least squares with orthogonal signal correction regression with respect to age, resulted in the identification of an age-related metabolic profile. Metabolites that correlated with age included creatinine, creatine, glycine, betaine/TMAO, citrate, succinate, and acetone. Although creatinine increased with age, all the other metabolites decreased. These results may be potentially useful in assessing the biological age (as opposed to chronological) of young humans as well as in providing a deeper understanding of the confounding factors in the application of metabolomics.

Advances in Nuclear Magnetic Resonance for Drug Discovery

BACKGROUND: Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. OBJECTIVE/METHOD: A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR. CONCLUSION: NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets.

Mass spectrometric imaging of lipids using desorption electrospray ionization

Desorption electrospray ionization (DESI), a relatively new ambient ionization technique used in mass spectrometry (MS), allows for the direct analysis of samples such as thin tissue sections, to be conducted outside of vacuum in the ambient environment and often without sample preparation. DESI-MS has been used in order to systematically characterize phospholipids, which are abundant species in biological tissue samples. Lipids play important biological roles and differences in lipid compositions have been seen in diseases such as cancer and Alzheimer's disease. Imaging of thin tissue sections exploits the ability of DESI-MS to study these lipids directly in the biological matrix. In imaging MS (IMS), a mass spectrum is recorded at each pixel while moving the surface containing the sample so that the entire sample area is covered. The information in these mass spectra can be combined to create a 2D chemical image of the sample, combining information on spatial distribution with information on chemical identity from the characteristic ions in the mass spectra. DESI-MS has been used to image a variety of tissue samples including human liver adenocarcinoma, rat brain, human breast tissue and canine abdominal tumor tissue. Comparisons between diseased and normal tissue are made in these studies.

Direct analysis of pharmaceutical formulations from non-bonded reversed-phase thin-layer chromatography plates by desorption electrospray ionisation ion mobility mass spectrometry

The direct analysis of pharmaceutical formulations and active ingredients from non-bonded reversed-phase thin layer chromatography (RP-TLC) plates by desorption electrospray ionisation (DESI) combined with ion mobility mass spectrometry (IM-MS) is reported. The analysis of formulations containing analgesic (paracetamol), decongestant (ephedrine), opiate (codeine) and stimulant (caffeine) active pharmaceutical ingredients is described, with and without chromatographic development to separate the active ingredients from the excipient formulation. Selectivity was enhanced by combining ion mobility and mass spectrometry to characterise the desorbed gas-phase analyte ions on the basis of mass-to-charge ratio (m/z) and gas-phase ion mobility (drift time). The solvent composition of the DESI spray using a step gradient was varied to optimise the desorption of active pharmaceutical ingredients from the RP-TLC plates. The combined RP-TLC/DESI-IM-MS approach has potential as a rapid and selective technique for pharmaceutical analysis by orthogonal gas-phase electrophoretic and mass-to-charge separation.