Qualitative metabolome analysis of human cerebrospinal fluid by 13C-/12C-isotope dansylation labeling combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry

Day-night fluctuations in choroid plexus transcriptomics and cerebrospinal fluid metabolomics

The cerebrospinal fluid (CSF) provides mechanical protection for the brain and serves as a brain dispersion route for nutrients, hormones, and metabolic waste. The CSF secretion rate is elevated in the dark phase in both humans and rats, which could support the CSF flow along the paravascular spaces that may be implicated in waste clearance. The similar diurnal CSF dynamics pattern observed in the day-active human and the nocturnal rat suggests a circadian regulation of this physiological variable, rather than sleep itself. To obtain a catalog of potential molecular drivers that could provide the day-night-associated modulation of the CSF secretion rate, we determined the diurnal fluctuation in the rat choroid plexus transcriptomic profile with RNA-seq and in the CSF metabolomics with ultraperformance liquid chromatography combined with mass spectrometry. We detected significant fluctuation of 19 CSF metabolites and differential expression of 2,778 choroid plexus genes between the light and the dark phase, the latter of which encompassed circadian rhythm-related genes and several choroid plexus transport mechanisms. The fluctuating components were organized with joint pathway analysis, of which several pathways demonstrated diurnal regulation. Our results illustrate substantial transcriptional and metabolic light-dark phase-mediated changes taking place in the rat choroid plexus and its encircling CSF. The combined data provide directions toward future identification of the molecular pathways governing the fluctuation of this physiological process and could potentially be harnessed to modulate the CSF dynamics in pathology.

Hyphenation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with separation methods: The art of compromises and the possible - A review

This review provides an overview of the online hyphenation of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) with separation methods to date. The online coupling between separation techniques (gas and liquid chromatography, capillary electrophoresis) and FT-ICR MS essentially raises questions of compromise and is not look as straightforward as hyphenation with other analyzers (QTOF-MS for instance). FT-ICR MS requires time to reach its highest resolving power and accuracy in mass measurement capabilities whereas chromatographic and electrophoretic peaks are transient. In many applications, the strengths and the weaknesses of each technique are balanced by their hyphenation. Untargeted "Omics" (e.g. proteomics, metabolomics, petroleomics, …) is one of the main areas of application for FT-ICR MS hyphenated to online separation techniques because of the complexity of the sample. FT-ICR MS achieves the required high mass measurement accuracy to determine accurate molecular formulae and resolution for isobar distinction. Meanwhile separation techniques highlight isomers and reduce the ion suppression effects extending the dynamic range. Even if the implementation of FT-ICR MS hyphenated with online separation methods is a little trickier (the art of compromise), this review shows that it provides unparalleled results to the scientific community (the art of the possible), along with raising the issue of its future in the field with the relentless technological progress.

Determination of trace concentrations of simple phenols in ambient PM samples

Phenols are hazardous, but yet ubiquitous in the environment, including in atmospheric aerosols due to combustion emissions. There, phenols are subjected to secondary transformations, producing even more toxic nitrophenolic air pollutants. However, primary simple phenols, i.e. those containing only hydroxyl, methyl and methoxy substituents are not easy to detect. Trace concentrations, semi-volatile character and poorly ionizable functional groups prevent us from their determination by the most common analytical techniques, such as gas and liquid chromatography with mass spectrometric detection (GC/LC-MS). Here, we present a new derivatization method for MS/MS detection with positive ion electrospray ionization (+ESI-MS/MS) of simple phenols in atmospheric particulate matter (PM) extracts. The method is sensitive, selective, and robust, and requires no sample concentration step, which is critical due to the volatile character of the target analytes. After derivatization with dansyl chloride, phenol, catechol, cresols and guaiacol were detected in urban PM samples from Ljubljana, Slovenia. This method finally enables to study the abundance of primary phenols in atmospheric PM from different sources, which will improve understanding of secondary aerosol (trans)formation pathways and allow for more targeted mitigation strategies in respect to airborne phenolic pollutants.

NMR based CSF metabolomics in tuberculous meningitis: correlation with clinical and MRI findings

We report the potential role of ¹H Nuclear Magnetic Resonance (NMR) based metabolomics in tuberculous meningitis (TBM). We also correlate the significant metabolites with clinical-radiological parameters. Forty-three patients with TBM were included, and their severity of meningitis was graded as stages I to III, and patients with positive Mycobacterium tuberculosis or its nucleic acid was considered as definite TBM. ¹H NMR-based metabolomic study was performed on (CSF) samples, and the significant metabolites compared to healthy controls were identified. Outcome at three months was defined as death, poor and good based on the modified Rankin Scale. These metabolites were compared between definite and probable groups of TBM, and also correlated with MRI findings. About 11 metabolites were found to be significant for distinguishing TBM from the controls. In TBM, lactate, glutamate, alanine, arginine, 2-hydroxyisobutyrate, formate, and cis-aconitate were upregulated, and glucose, fructose, glutamine, and myo-inositol were downregulated compared to the controls. For differentiating TBM from the controls, the AUC of the ROC curve generated using these significant metabolites was 0.99, with a 95% confidence interval from 0.96 to 1, demonstrating that these metabolites were able to classify cases with good sensitivity and specificity. Lactate concentration in CSF correlated with hemoglobin, CSF glucose, and infarction. The outcome did not correlate with metabolomics parameters. NMR-based CSF metabolomics have a potential role in differentiating TBM from the controls.

Novel plasma metabolite markers of attention-deficit/hyperactivity disorder identified using high-performance chemical isotope labelling-based liquid chromatography-mass spectrometry

OBJECTIVES

Metabolites are the intermediate and final products of biological processes and ultimately reflect the responses of these processes to genetic regulation and environmental perturbations, including those involved in attention deficit/hyperactivity disorder (ADHD).

METHODS

We identified a quantitative profile of plasma metabolites in 58 ADHD patients (mean age 9.0 years, 77.6% males) and 38 healthy control subjects (mean age 10.2 years, 55.3% males) using the high-performance chemical isotope labelling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Using a volcano plot and orthogonal projections to latent structure-discriminant analysis (OPLS-DA), we determined nine metabolites with differentially expressed levels in ADHD plasma samples.

RESULTS

Compared to the control group, the plasma levels of guanosine, O-phosphoethanolamine, phenyl-leucine, hypoxanthine, 4-aminohippuric acid, 5-hydroxylysine, and L-cystine appeared increased in the ADHD patients, whilegentisic acid and tryptophyl-phenylalanine were down-regulated in the patients with ADHD. We found that the plasma levels of all nine metabolites were able to discriminate the ADHD group from the control group. Levels of O-phosphoethanolamine, 4-aminohippuric acid, 5-hydroxylysine, L-cystine, tryptophyl-phenylalanine, and gentisic acid were significantly correlated with clinical ADHD symptoms.

CONCLUSIONS

This study is the first to use the CIL-based LC-MS to profile ADHD plasma metabolites, and we identified nine novel metabolite biomarkers of ADHD.

Chemical Isotope Labeling LC-MS for Metabolomics

Due to the great diversity of chemical and physical properties of metabolites as well as a wide range of concentrations of metabolites present in metabolomic samples, performing comprehensive and quantitative metabolome analysis is a major analytical challenge. Conventional approach of combining various techniques and methods with each detecting a fraction of the metabolome can lead to the increase in overall metabolomic coverage. However, this approach requires extensive investment in equipment and analytical expertise with still relatively low coverage and low sample throughput. Chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) offers an alternative means of increasing metabolomic coverage while maintaining high quantification precision and accuracy. This chapter describes the CIL LC-MS method and its key features for metabolomic analysis.

Targeting amine- and phenol-containing metabolites in urine by dansylation isotope labeling and liquid chromatography mass spectrometry for evaluation of bladder cancer biomarkers

Metabolomics is considered an effective approach for understanding metabolic responses in complex biological systems. Accordingly, it has attracted increasing attention for biomarker discovery, especially in cancer. In this study, we used a non-invasive method to evaluate four urine metabolite biomarker candidates—o-phosphoethanolamine, 3-amio-2-piperidone, uridine and 5-hydroxyindoleactic acid—for their potential as bladder cancer diagnostic biomarkers. To analyze these targeted amine- and phenol-containing metabolites, we used differential ¹²C₂-/¹³C₂-dansylation labeling coupled with liquid chromatography/tandem mass spectrometry, which has previously been demonstrated to exhibit high sensitivity and reproducibility. Specifically, we used ultra-performance liquid chromatography (UPLC) coupled with high-resolution Fourier transform ion-cyclotron resonance MS system (LC-FT/MS) and an ion trap MS with MRM function (LC-HCT/MS) for targeted quantification. The urinary metabolites of interest were well separated and quantified using this approach. To apply this approach to clinical urine specimens, we spiked samples with ¹³C₂-dansylatedsynthetic compounds, which served as standards for targeted quantification of ¹²C₂-dansylated urinary endogenous metabolites using LC-FT/MS as well as LC-HCT/MS with MRM mode. These analyses revealed significant differences in two of the four metabolites of interest—o-phosphoethanolamine and uridine—between bladder cancer and non-cancer groups. O-phosphoethanolamine was the most promising single biomarker, with an area-under-the-curve (AUC) value of 0.709 for bladder cancer diagnosis. Diagnostic performance was improved by combining uridine and o-phosphoe-thanolamine in a marker panel, yielding an AUC value of 0.726. This study confirmed discovery-phase features of the urine metabolome of bladder cancer patients and verified their importance for further study.

Comprehensive urinary metabolomic characterization of a genetically induced mouse model of prostatic inflammation

Dysfunction of the lower urinary tract commonly afflicts the middle-aged and aging male population. The etiology of lower urinary tract symptoms (LUTS) is multifactorial. Benign prostate hyperplasia, fibrosis, smooth muscle contractility, and inflammation likely contribute. Here we aim to characterize the urinary metabolomic profile associated with prostatic inflammation, which could inform future personalized diagnosis or treatment, as well as mechanistic research. Quantitative urinary metabolomics was conducted to examine molecular changes following induction of inflammation via conditional Interleukin-1β expression in prostate epithelia using a novel transgenic mouse strain. To advance method development for urinary metabolomics, we also compared different urine normalization methods and found that normalizing urine samples based on osmolality prior to LC-MS most completely separated urinary metabolite profiles of mice with and without prostate inflammation via principal component analysis. Global metabolomics was combined with advanced machine learning feature selection and classification for data analysis. Key dysregulated metabolites and pathways were identified and were relevant to prostatic inflammation, some of which overlapped with our previous study of human LUTS patients. A binary classification model was established via the support vector machine algorithm to accurately differentiate control and inflammation groups, with an area-under-the-curve value of the receiver operating characteristic of 0.81, sensitivity of 0.974 and specificity of 0.995, respectively. This study generated molecular profiles of non-bacterial prostatic inflammation, which could assist future efforts to stratify LUTS patients and develop new therapies.

Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease

Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer's disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.

High-resolution metabolomics determines the mode of onset of type 2 diabetes in a 3-year prospective cohort study

Type 2 diabetes mellitus (DM) is a progressive disease and the rate of progression from non-diabetes to DM varies considerably between individuals, ranging from a few months to many years. It is important to understand the mechanisms underlying the progression of diabetes. In the present study, a high-resolution metabolomics (HRM) analysis was performed to detect potential biomarkers and pathways regulating the mode of onset by comparing subjects who developed and did not develop type 2 DM at the second year in a 3-year prospective cohort study. Metabolic profiles correlated with progression to DM were examined. The subjects (n=98) were classified into four groups: Control (did not develop DM for 3 years), DM (diagnosed with DM at the start of the study), DM onset at the third year and DM onset at the second year. The focus was on the comparison of serum samples of the DM groups with onset at the second and third year from the first year, where these two groups had not developed DM, yet. Analyses involved sample examination using liquid chromatography-mass spectrometry-based HRM and multivariate statistical analysis of the data. Metabolic differences were identified across all analyses with the affected pathways involved in metabolism associated with steroid biosynthesis and bile acid biosynthesis. In the first year, higher levels of cholesterol {mass-to charge ratio (m/z) 369.35, (M+H-H2O)+}, 25-hydroxycholesterol [m/z 403.36, (M+H)+], 3α,7α-dihydroxy-5β-cholestane [m/z 443.33, (M+K)+], 4α-methylzymosterol-4-carboxylate [m/z 425.34, (M+H‑H2O)+], and lower levels of 24,25-dihydrolanosterol [m/z 429.40, (M+H)+] were evident in the group with DM onset at the second year compared with those in the group with DM onset at the third year. These results, with a focus on the cholesterol biosynthesis pathway, point to important aspects in the development of DM and may aid in the development of more effective means of treatment and prevention.

The Impact of GFP Reporter Gene Transduction and Expression on Metabolomics of Placental Mesenchymal Stem Cells Determined by UHPLC-Q/TOF-MS

Introduction

Green fluorescent protein (GFP) is widely used as a reporter gene in regenerative medicine research to label and track stem cells. Here, we examined whether expressing GFP gene may impact the metabolism of human placental mesenchymal stem cells (hPMSCs).

Methods

The GFP gene was transduced into hPMSCs using lentiviral-based infection to establish GFP⁺hPMSCs. A sensitive ¹³C/¹²C-dansyl labeling LC-MS method targeting the amine/phenol submetabolome was used for in-depth cell metabolome profiling.

Results

A total of 1151 peak pairs or metabolites were detected from 12 LC-MS runs. Principal component analysis and partial least squares discriminant analysis showed poor separation, and the volcano plots demonstrated that most of the metabolites were not significantly changed when hPMSCs were tagged with GFP. Overall, 739 metabolites were positively or putatively identified. Only 11 metabolites showed significant changes. Metabolic pathway analyses indicated that three of the identified metabolites were involved in nine pathways. However, these metabolites are unlikely to have a large impact on the metabolic pathways due to their nonessential roles and limited hits in pathway analysis.

Conclusion

This study indicated that the expression of ectopic GFP reporter gene did not significantly alter the metabolomics pathways covered by the amine/phenol submetabolome.

Liquid Chromatography Mass Spectrometry-Based Metabolite Pathway Analyses of Myeloma and Non-Hodgkin's Lymphoma Patients

Objective

This study attempted to identify altered metabolism and pathways related to non-Hodgkin’s lymphoma (NHL) and myeloma patients.

Materials and Methods

In this retrospective study, we collected plasma samples from 11 patients-6 healthy controls with no evidence of any blood cancers and 5 patients with either multiple myeloma (n=3) or NHL (n=2) during the preliminary study period. Samples were analyzed using quadrupole time-of-flight liquid chromatography mass spectrometry (LC-MS). Significant features generated after statistical analyses were used for metabolomics and pathway analysis.

Results

Data after false discovery rate (FDR) adjustment at q=0.05 of features showed 136 for positive and 350 significant features for negative ionization mode in NHL patients as well as 262 for positive and 98 features for negative ionization mode in myeloma patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis determined that pathways such as steroid hormone biosynthesis, ABC transporters, and arginine and proline metabolism were affected in NHL patients. In myeloma patients, pyrimidine metabolism, carbon metabolism, and bile secretion pathways were potentially affected by the disease.

Conclusion

The results have shown tremendous differences in the metabolites of healthy individuals compared to myeloma and lymphoma patients. Validation through quantitative metabolomics is encouraged, especially for the metabolites with significantly expression in blood cancer patients.

Urinary profiling of cis-diol-containing metabolites in rats with bisphenol A exposure by liquid chromatography-mass spectrometry and isotope labeling

A rapid UPLC-MS method combined with isotope labeling technology was developed for the detection ofcis-diolmetabolites in rat urine after bisphenol A exposure.

Relative quantification of amine-containing metabolites using isobaric N,N-dimethyl leucine (DiLeu) reagents via LC-ESI-MS/MS and CE-ESI-MS/MS

Tandem mass spectrometry (MS/MS)-based relative quantification by isobaric labeling is a useful technique to compare different metabolic expression levels in biological systems. For the first time, we have labeled primary and secondary amine-containing small molecules using 4-plex isobaric N,N-dimethyl leucine (DiLeu) to perform relative quantification. Good labeling efficiency and quantification accuracy were demonstrated with a mixture of 12 metabolite standards including amino acids and small molecule neurotransmitters. Labeling amine-containing metabolites with DiLeu reagents also enabled the separation of polar metabolites by nanoRPLC and improved the detection sensitivity by CE-ESI-MS. The 4-plex DiLeu labeling technique combined with LC-MS/MS and CE-MS/MS platforms were applied to profile and quantify amine-containing metabolites in mouse urine. The variability of concentrations of identified metabolites in urine samples from different mouse individuals was illustrated by the ratios of reporter ion intensities acquired from online data-dependent analysis.

DnsID in MyCompoundID for rapid identification of dansylated amine- and phenol-containing metabolites in LC-MS-based metabolomics

High-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is an enabling technology based on rational design of labeling reagents to target a class of metabolites sharing the same functional group (e.g., all the amine-containing metabolites or the amine submetabolome) to provide concomitant improvements in metabolite separation, detection, and quantification. However, identification of labeled metabolites remains to be an analytical challenge. In this work, we describe a library of labeled standards and a search method for metabolite identification in CIL LC-MS. The current library consists of 273 unique metabolites, mainly amines and phenols that are individually labeled by dansylation (Dns). Some of them produced more than one Dns-derivative (isomers or multiple labeled products), resulting in a total of 315 dansyl compounds in the library. These metabolites cover 42 metabolic pathways, allowing the possibility of probing their changes in metabolomics studies. Each labeled metabolite contains three searchable parameters: molecular ion mass, MS/MS spectrum, and retention time (RT). To overcome RT variations caused by experimental conditions used, we have developed a calibration method to normalize RTs of labeled metabolites using a mixture of RT calibrants. A search program, DnsID, has been developed in www.MyCompoundID.org for automated identification of dansyl labeled metabolites in a sample based on matching one or more of the three parameters with those of the library standards. Using human urine as an example, we illustrate the workflow and analytical performance of this method for metabolite identification. This freely accessible resource is expandable by adding more amine and phenol standards in the future. In addition, the same strategy should be applicable for developing other labeled standards libraries to cover different classes of metabolites for comprehensive metabolomics using CIL LC-MS.

A hypothetical astrocyte-microglia lactate shuttle derived from a 1H NMR metabolomics analysis of cerebrospinal fluid from a cohort of South African children with tuberculous meningitis

Tuberculosis meningitis (TBM) is the most severe form of extra-pulmonary tuberculosis and is particularly intense in small children; there is no universally accepted algorithm for the diagnosis and substantiation of TB infection, which can lead to delayed intervention, a high risk factor for morbidity and mortality. In this study a proton magnetic resonance (¹H NMR)-based metabolomics analysis and several chemometric methods were applied to data generated from lumber cerebrospinal fluid (CSF) samples from three experimental groups: (1) South African infants and children with confirmed TBM, (2) non-meningitis South African infants and children as controls, and (3) neurological controls from the Netherlands. A total of 16 NMR-derived CSF metabolites were identified, which clearly differentiated between the controls and TBM cases under investigation. The defining metabolites were the combination of perturbed glucose and highly elevated lactate, common to some other neurological disorders. The remaining 14 metabolites of the host's response to TBM were likewise mainly energy-associated indicators. We subsequently generated a hypothesis expressed as an "astrocyte-microglia lactate shuttle" (AMLS) based on the host's response, which emerged from the NMR-metabolomics information. Activation of microglia, as implied by the AMLS hypothesis, does not, however, present a uniform process and involves intricate interactions and feedback loops between the microglia, astrocytes and neurons that hamper attempts to construct basic and linear cascades of cause and effect; TBM involves a complex integration of the responses from the various cell types present within the CNS, with microglia and the astrocytes as main players.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.

Development of isotope labeling liquid chromatography mass spectrometry for mouse urine metabolomics: quantitative metabolomic study of transgenic mice related to Alzheimer's disease

Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC-MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for ¹²C-dansylation labeling that target amines and phenols. Another 10 μL of aliquot was taken from each sample to generate a pooled sample that was subjected to ¹³C-dansylation labeling. The ¹²C-labeled individual sample was mixed with an equal volume of the ¹³C-labeled pooled sample. The mixture was then analyzed by LC-MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC-MS runs was assessed, and the median relative standard deviation over 4 days was 5.0%. This workflow was then applied to a metabolomic biomarker discovery study using urine samples obtained from the TgCRND8 mouse model of early onset familial Alzheimer's disease (FAD) throughout the course of their pathological deposition of beta amyloid (Aβ). It was showed that there was a distinct metabolomic separation between the AD prone mice and the wild type (control) group. As early as 15-17 weeks of age (presymptomatic), metabolomic differences were observed between the two groups, and after the age of 25 weeks the metabolomic alterations became more pronounced. The metabolomic changes at different ages corroborated well with the phenotype changes in this transgenic mice model. Several useful candidate biomarkers including methionine, desaminotyrosine, taurine, N1-acetylspermidine, and 5-hydroxyindoleacetic acid were identified. Some of them were found in previous metabolomics studies in human cerebrospinal fluid or blood samples. This work illustrates the utility of this isotope labeling LC-MS method for biomarker discovery using mouse urine metabolomics.

Quantitative metabolomic profiling using dansylation isotope labeling and liquid chromatography mass spectrometry

Differential chemical isotopic labeling (CIL) LC-MS has been used for quantifying a targeted metabolite in biological samples with high precision and accuracy. Herein we describe a high-performance CIL LC-MS method for generating quantitative and comprehensive profiles of the metabolome for metabolomics applications. After mixing two comparative samples separately labeled by light or heavy isotopic tags through chemical reactions, the peak intensity ratio of the labeled analyte pair can provide relative or absolute quantitative information on the metabolites. We describe the use of (12)C2- and (13)C2-dansyl chloride (DnsCl) as the isotope reagents to profile the metabolites containing amine and phenolic hydroxyl functional groups by LC-MS. This method can be used to compare the relative concentration changes of hundreds or thousands of amine- and phenol-containing metabolites among many comparative samples and generate absolute concentration information on metabolites for which the standards are available. Combined with statistical analysis and metabolite identification tools, this method can be used to identify key metabolites involved in differentiating comparative samples such as disease cases vs. healthy controls.

Multiplexed analysis of cage and cage free chicken egg fatty acids using stable isotope labeling and mass spectrometry

Binary stable isotope labeling couple with LC-ESI-MS has been used as a powerful non-targeted approach for the relative quantification of lipids, amino acids, and many other important metabolite classes. A multiplexed approach using three or more isotopic labeling reagents greatly reduces analytical run-time while maintaining excellent sensitivity and reproducibility. Three isotopic cholamine labeling reagents have been developed to take advantage of the pre-ionized character of cholamine, for ESI, and the ease by which stable isotopes can be incorporated into the cholamine structure. These three cholamine labeling reagents have been used to relatively quantify three fatty acid samples simultaneously. The quantification resulted in the observation of 12 fatty acids that had an average absolute error of 0.9% and an average coefficient of variation of 6.1%. Caged versus cage-free isotope labeling experiments showed that cage-free eggs have an increased level of omega-3 fatty acids as compared to caged eggs. This multiplexed fatty acid analysis provides an inexpensive and expedited tool for broad-based lipid profiling that will further aid discoveries in the mechanisms of fatty acid action in cells.

Liquid chromatography/mass spectrometry methods for measuring dipeptide abundance in non-small-cell lung cancer

RATIONALE

Metabolomic profiling is a promising methodology of identifying candidate biomarkers for disease detection and monitoring. Although lung cancer is among the leading causes of cancer-related mortality worldwide, the lung tumor metabolome has not been fully characterized.

METHODS

We utilized a targeted metabolomic approach to analyze discrete groups of related metabolites. We adopted a dansyl [5-(dimethylamino)-1-naphthalene sulfonamide] derivatization with liquid chromatography/mass spectrometry (LC/MS) to analyze changes of metabolites from paired tumor and normal lung tissues. Identification of dansylated dipeptides was confirmed with synthetic standards. A systematic analysis of retention times was required to reliably identify isobaric dipeptides. We validated our findings in a separate sample cohort.

RESULTS

We produced a database of the LC retention times and MS/MS spectra of 361 dansyl dipeptides. Interpretation of the spectra is presented. Using this standard data, we identified a total of 279 dipeptides in lung tumor tissue. The abundance of 90 dipeptides was selectively increased in lung tumor tissue compared to normal tissue. In a second set of validation tissues, 12 dipeptides were selectively increased.

CONCLUSIONS

A systematic evaluation of certain metabolite classes in lung tumors may identify promising disease-specific metabolites. Our database of all possible dipeptides will facilitate ongoing translational applications of metabolomic profiling as it relates to lung cancer.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Relative quantification of biomarkers using mixed-isotope labeling coupled with MS

The identification and quantification of important biomarkers is a critical first step in the elucidation of biological systems. Biomarkers take many forms as cellular responses to stimuli and can be manifested during transcription, translation, and/or metabolic processing. Increasingly, researchers have relied upon mixed-isotope labeling (MIL) coupled with MS to perform relative quantification of biomarkers between two or more biological samples. MIL effectively tags biomarkers of interest for ease of identification and quantification within the mass spectrometer by using isotopic labels that introduce a heavy and light form of the tag. In addition to MIL coupled with MS, a number of other approaches have been used to quantify biomarkers including protein gel staining, enzymatic labeling, metabolic labeling, and several label-free approaches that generate quantitative data from the MS signal response. This review focuses on MIL techniques coupled with MS for the quantification of protein and small-molecule biomarkers.

Development of isotope labeling LC-MS for human salivary metabolomics and application to profiling metabolome changes associated with mild cognitive impairment

Saliva is a readily available biofluid that may contain metabolites of interest for diagnosis and prognosis of diseases. In this work, a differential (13)C/(12)C isotope dansylation labeling method, combined with liquid chromatography Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR-MS), is described for quantitative profiling of the human salivary metabolome. New strategies are presented to optimize the sample preparation and LC-MS detection processes. The strategies allow the use of as little of 5 μL of saliva sample as a starting material to determine the concentration changes of an average of 1058 ion pairs or putative metabolites in comparative saliva samples. The overall workflow consists of several steps including acetone-induced protein precipitation, (12)C-dansylation labeling of the metabolites, and LC-UV measurement of the total concentration of the labeled metabolites in individual saliva samples. A pooled sample was prepared from all the individual samples and labeled with (13)C-dansylation to serve as a reference. Using this metabolome profiling method, it was found that compatible metabolome results could be obtained after saliva samples were stored in tubes normally used for genetic material collection at room temperature, -20 °C freezer, and -80 °C freezer over a period of 1 month, suggesting that many saliva samples already collected in genomic studies could become a valuable resource for metabolomics studies, although the effect of much longer term of storage remains to be determined. Finally, the developed method was applied for analyzing the metabolome changes of two different groups: normal healthy older adults and comparable older adults with mild cognitive impairment (MCI). Top-ranked 18 metabolites successfully distinguished the two groups, among which seven metabolites were putatively identified while one metabolite, taurine, was definitively identified.

Isotopic labeling-assisted metabolomics using LC-MS

Metabolomics has emerged as the latest of the so-called “omics” disciplines and has great potential to provide deeper understanding of fundamental biochemical processes at the biological system level. Among recent technological developments, LC–HRMS enables determination of hundreds to thousands of metabolites over a wide range of concentrations and has developed into one of the most powerful techniques in non-targeted metabolomics. The analysis of mixtures of in-vivo-stable isotopic-labeled samples or reference substances with un-labeled samples leads to specific LC–MS data patterns which can be systematically exploited in practically all data-processing steps. This includes recognition of true metabolite-derived analytical features in highly complex LC–MS data and characterization of the global biochemical composition of biological samples. In addition, stable-isotopic labeling can be used for more accurate quantification (via internal standardization) and identification of compounds in different organisms.

Isotope enhanced approaches in metabolomics

The rapidly growing area of "metabolomics," in which a large number of metabolites from body fluids, cells or tissue are detected quantitatively, in a single step, promises immense potential for a number of disciplines including early disease diagnosis, therapy monitoring, systems biology, drug discovery and nutritional science. Because of its ability to detect a large number of metabolites in intact biological samples reproducibly and quantitatively, nuclear magnetic resonance (NMR) spectroscopy has emerged as one of the most powerful analytical techniques in metabolomics. NMR spectroscopy of biological samples with isotope labeling of metabolites using nuclei such as (2)H, (13)C, (15)N and (31)P, either in vivo or ex vivo, has dramatically improved our ability to identify low concentrated metabolites and trace important metabolic pathways. Considering the somewhat limited sensitivity and high complexity of NMR spectra of biological samples, efforts have been made to increase sensitivity and selectivity through isotope labeling methods, which pave novel avenues to unravel biological complexity and understand cellular functions in health and various disease conditions. This chapter describes current developments in isotope labeling of metabolites in vivo as well as ex vivo, and their potential metabolomics applications.

Advances in metabolite identification

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.