Metabolomics of lung cancer: Analytical platforms and their applications

Lung cancer is the leading type of cancer worldwide in terms of the number of new cases and is responsible for the largest number of deaths due to poor prognosis and difficult early detection. Due to its ability to detect numerous small molecular metabolites simultaneously, metabolomics has been widely used for the assessment of global metabolic changes in a living organism to discover candidate biomarkers for cancer diagnosis, investigate the development of cancer, and provide insights into the underlying pathophysiology. This review will mainly describe recent developments in lung cancer metabolomics in terms of early‐stage detection, biomarker discovery and mechanism exploration by using nuclear magnetic resonance, liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, and capillary electrophoresis–mass spectrometry in the last 10 years. The sample collection and metabolite extraction methods are also summarized.     

Recent advances in metabolomics in neurological disease, and future perspectives

Discovery of clinically relevant biomarkers for diseases has revealed metabolomics has potential advantages that classical diagnostic approaches do not. The great asset of metabolomics is that it enables assessment of global metabolic profiles of biofluids and discovery of biomarkers distinguishing disease status, with the possibility of enhancing clinical diagnostics. Most current clinical chemistry tests rely on old technology, and are neither sensitive nor specific for a particular disease. Clinical diagnosis of major neurological disorders, for example Alzheimer’s disease and Parkinson’s disease, on the basis of current clinical criteria is unsatisfactory. Emerging metabolomics is a powerful technique for discovering novel biomarkers and biochemical pathways to improve diagnosis, and for determination of prognosis and therapy. Identifying multiple novel biomarkers for neurological diseases has been greatly enhanced with recent advances in metabolomics that are more accurate than routine clinical practice. Cerebrospinal fluid (CSF), which is known to be a rich source of small-molecule biomarkers for neurological and neurodegenerative diseases, and is in close contact with diseased areas in neurological disorders, could potentially be used for disease diagnosis. Metabolomics will drive CSF analysis, facilitate and improve the development of disease treatment, and result in great benefits to public health in the long-term. This review covers different aspects of CSF metabolomics and discusses their significance in the postgenomic era, emphasizing the potential importance of endogenous small-molecule metabolites in this emerging field.     

NMR and pattern recognition methods in metabolomics: From data acquisition to biomarker discovery: A review

Metabolomics is the discipline where endogenous and exogenous metabolites are assessed, identified and quantified in different biological samples. Metabolites are crucial components of biological system and highly informative about its functional state, due to their closeness to functional endpoints and to the organism's phenotypes. Nuclear Magnetic Resonance (NMR) spectroscopy, next to Mass Spectrometry (MS), is one of the main metabolomics analytical platforms. The technological developments in the field of NMR spectroscopy have enabled the identification and quantitative measurement of the many metabolites in a single sample of biofluids in a non-targeted and non-destructive manner. Combination of NMR spectra of biofluids and pattern recognition methods has driven forward the application of metabolomics in the field of biomarker discovery. The importance of metabolomics in diagnostics, e.g. in identifying biomarkers or defining pathological status, has been growing exponentially as evidenced by the number of published papers. In this review, we describe the developments in data acquisition and multivariate analysis of NMR-based metabolomics data, with particular emphasis on the metabolomics of Cerebrospinal Fluid (CSF) and biomarker discovery in Multiple Sclerosis (MScl).     

Plasma fingerprinting with GC-MS in acute coronary syndrome

New biomarkers of cardiovascular disease are needed to augment the information obtained from traditional indicators and to illuminate disease mechanisms. One of the approaches used in metabolomics/metabonomics for that purpose is metabolic fingerprinting aiming to profile large numbers of chemically diverse metabolites in an essentially nonselective way. In this study, gas chromatography-mass spectrometry was employed to evaluate the major metabolic changes in low molecular weight plasma metabolites of patients with acute coronary syndrome (n = 9) and with stable atherosclerosis (n = 10) vs healthy subjects without significant differences in age and sex (n = 10). Reproducible differences between cases and controls were obtained with pattern recognition techniques, and metabolites accounting for higher weight in the classification have been identified through their mass spectra. On this basis, it seems inherently plausible that even a simple metabolite profile might be able to offer improved clinical diagnosis and prognosis, but in addition, specific markers are being identified.     

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.     

Serum metabolomics as a novel diagnostic approach for disease: a systematic review

Metabolomics is a promising "omics" field in systems biology; its objective is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could result in earlier intervention and provide valuable insights into the mechanisms of diseases. Because of the possible discovery of clinically relevant biomarkers, metabolomics has potential advantages that routine approaches to clinical diagnosis do not. Monitoring specific metabolite levels in serum, the most commonly used biofluid in metabolomics, has become an important way of detecting the early stages of a disease. Serum is a readily accessible and informative biofluid, making it ideal for early detection of a wide range of diseases, and analysis of serum has several advantages over analysis of other biofluids. Metabolite profiles of serum can be regarded as important indicators of physiological and pathological states and may aid understanding of the mechanism of disease occurrence and progression on the metabolic level, and provide information enabling identification of early and differential metabolic markers of disease. Analysis of these crucial metabolites in serum has become important in monitoring the state of biological organisms and is widely used for diagnosis of disease. Emerging metabolomics will drive serum analysis, facilitate and improve the development of disease treatments, and provide great benefits for public health in the long-term.     

Untargeted Tumor Metabolomics with Liquid Chromatography–Surface‐Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface‐enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC‐SERS approach has metabolite detection capabilities comparable to the state‐of‐art LC‐MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC‐SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC‐SERS technology for metabolomics‐based diagnosis and treatment of cancer.     

Ultra-performance liquid chromatography coupled to mass spectrometry as a sensitive and powerful technology for metabolomic studies

Metabolomics is the comprehensive assessment of endogenous metabolites of a biological system. These large-scale analyses of metabolites are intimately bound to advancements in ultra-performance liquid chromatography-electrospray (UPLC) technologies and have emerged in parallel with the development of novel mass analyzers and hyphenated techniques. Recently, the combination of UPLC with MS covers a number of polar metabolites, thus enlarging the number of detected analytes in the widely used separation sciences. This technology has rapidly been accepted by the analytical community and is being gradually applied to various fields such as metabolomics and traditional Chinese medicine (TCM). Given the power of the technology, metabolomics has become increasingly popular in drug development, molecular medicine, traditional medicine and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. Hyphenated UPLC/MS technique is becoming a useful tool in the study of body fluids, represents a promising hyphenated microseparation platform in metabolomics and has a strong potential to contribute to disease diagnosis. This review describes the applications of UPLC/MS in metabolomic research, and comparison role of HPLC/MS, NMR and GC/MS, highlights its advantages and limitations with certain characteristic examples in the life and TCM sciences.     

Untargeted Tumor Metabolomics with Liquid Chromatography–Surface-Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.     

Metabolomics: a revolution for novel cancer marker identification

The repertoire of small-molecular-weight substances present in cells, tissue and body fluids are known as the metabolites. The global analysis of metabolites, such as by high-resolution 1H nuclear magnetic resonance spectroscopy and mass spectrometry, is integral to the rapidly expanding field of metabolomics, which is making progress in various diseases. In the area of cancer and metabolic phenotype, the integrated analysis of metabolites may provide a powerful platform for detecting changes related to cancer diagnosis and discovering novel biomarkers. In this review, metabolomics including the technologies in metabolomics research and extracting information from metabolomics datasets are described. Then we discuss the challenges and opportunities in metabolomics for finding metabolic processes in cancer and discovering novel cancer biomarkers. Finally, we assess the clinical applicability of metabolomics.     

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.     

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)—obtained from joints affected in ReA—holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹H and ¹³C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹H and ¹H─¹³C NMR spectroscopy methods such as one-dimensional ¹H CPMG and two-dimensional J-resolved¹H NMR and homonuclear ¹H─¹H TOCSY and heteronuclear¹H─¹³C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹H NMR signals were identified and assigned to metabolites in the ¹H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.     

Modern analytical techniques in metabolomics analysis

Metabolomics is the comprehensive assessment of endogenous metabolites and attempts to systematically identify and quantify metabolites from a biological sample. Small-molecule metabolites have an important role in biological systems and represent attractive candidates to understand disease phenotypes. Metabolites represent a diverse group of low-molecular-weight structures including lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols and carbohydrates, which makes global analysis a difficult challenge. The recent rapid development of a range of analytical platforms, including GC, HPLC, UPLC, CE coupled to MS and NMR spectroscopy, could enable separation, detection, characterization and quantification of such metabolites and related metabolic pathways. Owing to the complexity of the metabolome and the diverse properties of metabolites, no single analytical platform can be applied to detect all metabolites in a biological sample. The combined use of modern instrumental analytical approaches has unravelled the ideal outcomes in metabolomics, and is beneficial to increase the coverage of detected metabolites that can not be achieved by single-analysis techniques. Integrated platforms have been frequently used to provide sensitive and reliable detection of thousands of metabolites in a biofluid sample. Continued development of these analytical platforms will accelerate widespread use and integration of metabolomics into systems biology. Here, the application of each hyphenated technique is discussed and its strengths and limitations are discussed with selected illustrative examples; furthermore, this review comprehensively highlights the role of integrated tools in metabolomic research.     

Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics

Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.     

Strategy for metabonomics research based on high-performance liquid chromatography and liquid chromatography coupled with tandem mass spectrometry

Metabonomics, the study of metabolites and their roles in various disease states, is a novel methodology arising from the post-genomics era. This methodology has been applied in many fields. Current metabonomic practice has relied on mass spectrometry (MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) to analyze metabolites. In this study, a strategy was developed for applying high-performance liquid chromatography (HPLC) and LC-MS-MS to metabonomics research. One of the key problems to be solved in this strategy is to match the peaks between the chromatograms. A peak alignment algorithm has been developed to match the chromatograms before the pattern recognition. As an application example, the strategy described above was applied to metabonomics research on liver diseases, and the false-positive result of live cancer diagnosis from the hepatocirrhosis and hepatitis diseases was effectively reduced to 7.40%. Based on the pattern recognition, several potential biomarkers were found and further identified by the following LC-MS-MS experiments. The structures of eight potential biomarkers were given for distinguishing the liver cancer from the hepatocirrhosis and hepatitis diseases.     

Application of NMR metabolomics to search for human disease biomarkers

Since antiquity, humans have used body fluids like saliva, urine and sweat for the diagnosis of diseases. The amount, color and smell of body fluids are still used in many traditional medical practices to evaluate an illness and make a diagnosis. The development and application of analytical methods for the detailed analysis of body fluids has led to the discovery of numerous disease biomarkers. Recently, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The goal of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. In this review we will discuss recent developments in sample preparation, experimental techniques, the identification and quantification of metabolites, and the chemometric tools used to search for biomarkers of human diseases using NMR.     

Metabonomics investigation of human urine after ingestion of green tea with gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry and (1)H NMR spectroscopy

A method using gas chromatography/mass spectrometry (GC/MS), liquid chromatography/mass spectrometry (LC/MS) and (1)H NMR with pattern recognition tools such as principle components analysis (PCA) was used to study the human urinary metabolic profiles after the intake of green tea. From the normalized peak areas obtained from GC/MS and LC/MS and peak heights from (1)H NMR, statistical analyses were used in the identification of potential biomarkers. Metabolic profiling by GC/MS provided a different set of quantitative signatures of metabolites that can be used to characterize the molecular changes in human urine samples. A comparison of normalized metabonomics data for selected metabolites in human urine samples in the presence of potential overlapping peaks after tea ingestion from LC/MS and (1)H NMR showed the reliability of the current approach and method of normalization. The close agreements of LC/MS with (1)H NMR data showed that the effects of ion suppression in LC/MS for early eluting metabolites were not significant. Concurrently, the specificity of detecting the stated metabolites by (1)H NMR and LC/MS was demonstrated. Our data showed that a number of metabolites involved in glucose metabolism, citric acid cycle and amino acid metabolism were affected immediately after the intake of green tea. The proposed approach provided a more comprehensive picture of the metabolic changes after intake of green tea in human urine. The multiple analytical approach together with pattern recognition tools is a useful platform to study metabolic profiles after ingestion of botanicals and medicinal plants.     

Bioanalytical methods for metabolomic profiling: Detection of head and neck cancer,including oral cancer

Metabolomics is an emerging field dealing with the measurement and interpretation of small molecular byproducts of biochemical processes, or metabolites, which can be used to generate profiles from biological samples. Promising for use in pathophysiology, metabolomic profiles give the immediate biological state of a sample. These profiles are altered in diseases and are detectable in biological samples, such as tissue, blood, urine, saliva, and others. Most remarkably, metabolic profiles usually are altered before symptoms appear in a patient. For this reason, metabolomics has potential as a reliable method for an early diagnosis of diseases through disease biomarker identification. This application is most prevalent in cancer, such as head and neck cancer (HNC). Metabolomic studies offer avenues to improve on current medical techniques through the application of mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and statistical analysis to determine better biomarkers than those currently known. In this review, we discuss the use of MS and NMR tools for detecting biomarkers in tissue and fluid samples, and the appropriateness of metabolomics in analyzing cancer. Advantages, disadvantages, and recent studies on metabolomic profiling techniques in HNC analysis are also discussed herein.     

Nuclear magnetic resonance-based metabolomics for prediction of gastric damage induced by indomethacin in rats

Non-steroidal anti-inflammatory drugs (NSAIDs) have side effects including gastric erosions, ulceration and bleeding. In this study, pattern recognition analysis of the ¹H-nuclear magnetic resonance (NMR) spectra of urine was performed to develop surrogate biomarkers related to the gastrointestinal (GI) damage induced by indomethacin in rats. Urine was collected for 5 h after oral administration of indomethacin (25 mg kg⁻¹) or co-administration with cimetidine (100 mg kg⁻¹), which protects against GI damage. The ¹H-NMR urine spectra were divided into spectral bins (0.04 ppm) for global profiling, and 36 endogenous metabolites were assigned for targeted profiling. The level of gastric damage in each animal was also determined. Indomethacin caused severe gastric damage; however, indomethacin administered with cimetidine did not. Simultaneously, the patterns of changes in their endogenous metabolites were different. Multivariate data analyses were carried out to recognize the spectral pattern of endogenous metabolites related to indomethacin using partial least square-discrimination analysis. In targeted profiling, a few endogenous metabolites, 2-oxoglutarate, acetate, taurine and hippurate, were selected as putative biomarkers for the gastric damage induced by indomethacin. These metabolites changed depending on the degree of GI damage, although the same dose of indomethacin (10 mg kg⁻¹) was administered to rats. The results of global and targeted profiling suggest that the gastric damage induced by NSAIDs can be screened in the preclinical stage of drug development using a NMR based metabolomics approach.