Relative Quantification Of Metabolites Research Articles

Local burn wound environment versus systemic response: Comparison of proteins and metabolites.

In this study, paired blood plasma (BP) and blister fluid (BF) samples from five paediatric burn patients were analysed using mass spectrometry to compare their protein and metabolite composition. The relative quantification of proteins was achieved through a label‐free data independent acquisition mode. The relative quantification of metabolites was achieved using a Shimadzu Smart Metabolite Database gas chromatography mass spectrometry (GCMS) targeted assay. In total, 562 proteins and 141 individual metabolites were identified in the samples. There was 81% similarity in the proteins present in the BP and BF, with 50 and 54 unique proteins found in each sample type respectively. BF contained keratinocyte proliferation‐related proteins and blood plasma contained abundant blood clotting proteins and apolipoproteins. BF contained more carbohydrates and less alpha‐hydroxy acid metabolites than the BP. In this study, there were unique proteins and metabolites in BF and BP which were reflective of the local wound environment and systemic environments respectively. The results from this study demonstrate that the biomolecule content of BF is mostly the same as blood, but it also contains information specific to the local wound environment.

Novel acquisition strategies for metabolomics using drift tube ion mobility-quadrupole resolved all ions time-of-flight mass spectrometry (IM-QRAI-TOFMS)

The focus of this work was the implementation of ion mobility (IM) and a prototype quadrupole driver within data independent acquisition (DIA) using a drift tube IM-QTOFMS aiming to improve the level of confidence in identity confirmation workflows for non-targeted metabolomics. In addition to conventional all ions (IM-AI) acquisition, quadrupole resolved all ions (IM-QRAI) acquisition allows a drift time-directed precursor ion isolation in DIA using sequential isolation of precursor ions using mass windows of up to 100 Da which can be rapidly ramped across single ion mobility transients (i.e., <100 ms) according to the arrival times of precursor ions. Both IM-AI and IM-QRAI approaches were used for identity confirmation and relative quantification of metabolites in cellular extracts of the cell factory host Pichia pastoris. Samples were spiked with a uniformly 13C-labeled (U13C) internal standard and LC with low-field drift tube IM separation was used in combination with IM-AI and IM-QRAI. Combining excellent hardware performance and correlation of IM arrival times of natural (natC) and U13C metabolites enabled alignment of signals in the arrival time domain (DTCCSN2 differences ≤0.3%), and, in the case of IM-QRAI operation, maintenance of quantitative signals in comparison to IM-AI. The combination of tailored IM-QRAI methods for precursor ion isolation and IM separation also minimized the occurrence of spectral interferences in complex DIA datasets. Combined use of the software tools MS-DIAL, MS-Finder and Skyline for peak picking, feature alignment, reconciliation of natC and U13C isotopologue pairs, deconvolution of fragment spectra from DIA data, identity confirmation (including DTCCSN2) and targeted re-extraction of datafiles were employed for the data processing workflow. Overall, the combined new acquisition and data processing approaches enabled 87 metabolites to be identified between Level 1 (identified by standard compound) and Level 3.2 (accurate mass spectrum and number of carbons confirmed). The developed methods constitute promising metabolomics discovery tools and can be used to elucidate the number of carbon atoms present in unknown metabolites in stable isotope-supported metabolomics.

Development of a relative quantification method for infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging of Arabidopsis seedlings.

Mass spectrometry imaging of young seedlings is an invaluable tool in understanding how mutations affect metabolite accumulation in plant development. However, due to numerous biological considerations, established methods for the relative quantification of analytes using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry imaging are not viable options. In this study, we report a method for the quantification of auxin-related compounds using stable-isotope-labelled (SIL) indole-3-acetic acid (IAA) doped into agarose substrate. Wild-type Arabidopsis thaliana seedlings, sur2 and wei8 tar2 loss-of-function mutants, and YUC1 gain-of-function line were grown for 3 days in the dark in standard growth medium. SIL-IAA was doped into a 1% low-melting-point agarose gel and seedlings were gently laid on top for IR-MALDESI imaging with Orbitrap mass spectrometry analysis. Relative quantification was performed post-acquisition by normalization of auxin-related compounds to SIL-IAA in the agarose. Amounts of auxin-related compounds were compared between genotypes to distinguish the effects of the mutations on the accumulation of indolic metabolites of interest. IAA added to agarose was found to remain stable, with repeatability and abundance features of IAA comparable with those of other compounds used in other methods for relative quantification in IR-MALDESI analyses. Indole-3-acetaldoxime was increased in sur2 mutants compared with wild-type and other mutants. Other auxin-related metabolites were either below the limits of quantification or successfully quantified but showing little difference among mutants. Agarose was shown to be an appropriate sampling surface for IR-MALDESI mass spectrometry imaging of Arabidopsis seedlings. SIL-IAA doping of agarose was demonstrated as a viable technique for relative quantification of metabolites in live seedlings or tissues with similar biological considerations.

Metabolomics Analysis of Opiate Abusers from Golestan Cohort Study (GCS)

Illicit use of opiate is a major public health threat across the world, and is associated with numerous potential adverse health outcomes such as cancers, infectious diseases and psychological disorders. Opium and their derivatives can modulate gene expression through epigenetic and possibly RNA modifications, ultimately impacts intracellular signaling cascades, and creates long‐lasting changes in the metabolome. Furthermore opiate can modify the output of urinary metabolites through many integrated neural and hormonal mechanisms within the periphery and central nervous system and kidneys.Metabolomics is a powerful technique that has been used in clinical and laboratory medicine for the discovery of potential diagnostic, prognostic and therapeutic biomarkers. The result of this study could lead to identification of significant altered biomarkers and can improve our knowledge of underling etiology and molecular pathways of opiate addiction and inform nutrition intervention.We performed 1H‐NMR metabolomics and liquid chromatography mass spectrometry (UPLC‐Q Exactive HF‐X MS) to analyze the urine metabolite profiling of 535 individuals from Golestan Cohort Study (GCS). The GCS is conducted in northeast of Iran to assess risk factors for upper gastrointestinal cancers for more than 50,000 volunteers who also were analyzed for opiate use and their complications.The metabolomics profiles of urine specimens from 443 (285 daily and 158 non‐daily) opiate users, stratified by route of use (oral and smoking) and tobacco use, have been compared with profiles obtained for 92 nonuser controls. After sample preparation, data was collected using a Bruker Avance III 700 MHz NMR Spectrometer. TopSpin and ACD Spectrus Processor softwares were used to data processing and NMR binning. Metabolites were identified by using Chenomx NMR Suite 8.3 Professional software.Metabolomics analysis revealed perturbations in level of hydroxyisovalerate, alanine, choline, citrate, creatinine, glucose, lactate, malonate, methylhydantoin, phenylalanine, phosphocholine, sucrose, tyrosine, acetylsalicylate and inositol in opiate abuser versus control groups.Metabolites are intrinsically involved in multiple metabolic pathways in vivo, so relative quantification of metabolites in body fluids such as urine can provide a profile of the metabolic state of an organism. Over the past decade, a large number of prospective and retrospective epidemiological studies have investigated the potential carcinogenic effect of opiate in humans. While data in favor of opiate's carcinogenicity is mounting, the potential mechanisms are largely unclear. The present study will explore opiate addiction and cancer risk using metabolomics, including exploring opiate effects on metabolic pathways and identification of potential markers of long term opiate abuse.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Improving accuracy of peak-pair intensity ratio measurement in differential chemical isotope labeling LC–MS for quantitative metabolomics

High-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC–MS) has become a powerful platform for quantitative metabolomic analysis. With differential isotope labeling of individual samples (e.g., 12C2-labeling) and a control sample (e.g., 13C2-labeling), the resulting mixtures (12C2-labeled sample and 13C2-labeled control) can be analyzed using LC–MS to detect peak pairs of labeled metabolites from which the peak-pair intensity ratios can be measured to provide the basis of relative quantification of metabolites in different samples. Depending on the mass difference of the light and heavy reagents used and the number of reagent tags attached to a metabolite, the mass difference within a peak pair can be as low as 2 Da. With 2-Da difference, the natural isotopologue of the light labeled molecular ion can overlap with that of the heavy labeled molecular ion of the same metabolite, causing potential interference in the measurement of the heavy peak intensity. In this work, we report our study of the natural isotope relative peak intensity of the most common elements in human endogenous metabolites, including carbon, hydrogen, oxygen, nitrogen, phosphor and sulfur, and their contributions to the intensity of the 2-Da-heavier labeled peak. We propose a simple data processing method to estimate and remove the natural isotope interference for improving the measurement accuracy of peak-pair intensity ratios.

Partially 13C-labeled mouse tissue as reference for LC-MS based untargeted metabolomics

The inclusion of stable isotope-labeled reference standards in the sample is an established method for the detection and relative quantification of metabolic features in untargeted metabolomics. In order to quantify as many metabolites as possible, the reference should ideally include the same metabolites in their stable isotope-labeled form as the sample under investigation. We present here an attempt to use partially 13C-labeled mouse material as internal standard for relative metabolite quantification of mouse and human samples in untargeted metabolomics.We fed mice for 14 days with a13C-labeled Ralstonia eutropha based diet. Tissue and blood amino acids from these mice showed 13C enrichment levels that ranged from 6% to 75%. We used MetExtract II software to automatically detect native and labeled peak pairs in an untargeted manner. In a dilution series and with the implementation of a correction factor, partially 13C-labeled mouse plasma resulted in accurate relative quantification of human plasma amino acids using liquid chromatography coupled to mass spectrometry, The coefficient of variation for the relative quantification is reduced from 27% without internal standard to 10% with inclusion of partially 13C-labeled internal standard.We anticipate the method to be of general use for the relative metabolite quantification of human specimens.

Chemical Isotope Labeling LC-MS for High Coverage and Quantitative Profiling of the Hydroxyl Submetabolome in Metabolomics.

A key step in metabolomics is to perform accurate relative quantification of the metabolomes in comparative samples with high coverage. Hydroxyl-containing metabolites are an important class of the metabolome with diverse structures and physical/chemical properties; however, many of them are difficult to detect with high sensitivity. We present a high-performance chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) technique for in-depth profiling of the hydroxyl submetabolome, which involves the use of acidic liquid-liquid extraction to enrich hydroxyl metabolites into ethyl acetate from an aqueous sample. After drying and then redissolving in acetonitrile, the metabolite extract is labeled using a base-activated 12C- or 13C-dansylation reaction. A fast step-gradient LC-UV method is used to determine the total concentration of labeled metabolites. On the basis of the concentration information, a 12C-labeled individual sample is mixed with an equal mole amount of a 13C-labeled pool or control for relative metabolite quantification. The 12C-/13C-labeled mixtures are individually analyzed by LC-MS, and the resultant peak pairs of labeled metabolites in MS are measured for relative quantification and metabolite identification. A standard library of 85 hydroxyl compounds containing MS, retention time, and MS/MS information was constructed for positive metabolite identification based on matches of two or all three of these parameters with those of an unknown. Using human urine as an example, we analyzed samples of 1:1 12C-/13C-labeled urine in triplicate with triplicate runs per sample and detected an average of 3759 ± 45 peak pairs or metabolites per run and 3538 ± 71 pairs per sample with 3093 pairs in common (n = 9). Out of the 3093 peak pairs, 2304 pairs (75%) could be positively or putatively identified based on metabolome database searches, including 20 pairs positively identified using the dansylated hydroxyl standards library. The majority of detected metabolites were those containing hydroxyl groups. This technique opens a new avenue for the detailed characterization of the hydroxyl submetabolome in metabolomics research.

13C Metabolomics: NMR and IROA for Unknown Identification

: BackgroundIsotopic Ratio Outlier Analysis (IROA) is an untargeted metabolomics method that uses stable isotopic labeling and LC-HRMS for identification and relative quantification of metabolites in a biological sample under varying experimental conditions.ObjectiveWe demonstrate a method using high-sensitivity 13C NMR to identify an unknown metabolite isolated from fractionated material from an IROA LC-HRMS experiment.MethodsIROA samples from the nematode Caenorhabditis elegans were fractionated using LC-HRMS using 5 repeated injections and collecting 30 sec fractions. These were concentrated and analyzed by 13C NMR.ResultsWe isotopically labeled samples of C. elegans and collected 2 adjacent LC fractions. By HRMS, one contained at least 2 known metabolites, phenylalanine and inosine, and the other contained tryptophan and an unknown feature with a monoisotopic mass of m/z 380.0742 [M+H]+. With NMR, we were able to easily verify the known compounds, and we then identified the spin system networks responsible for the unknown resonances. After searching the BMRB database and comparing the molecular formula from LC-HRMS, we determined that the fragments were a modified anthranilate and a glucose modified by a phosphate. We then performed quantum chemical NMR chemical shift calculations to determine the most likely isomer, which was 3’-O-phospho-β-D-glucopyranosyl-anthranilate. This compound had previously been found in the same organism, validating our approach.ConclusionWe were able to dereplicate previously known metabolites and identify a metabolite that was not in databases by matching resonances to NMR databases and using chemical shift calculations to determine the correct isomer. This approach is efficient and can be used to identify unknown compounds of interest using the same material used for IROA.

Matrix effect on chemical isotope labeling and its implication in metabolomic sample preparation for quantitative metabolomics

Matrix effect from various constituents in biological samples can reduce the accuracy of quantitative metabolomics. Differential chemical isotope labeling liquid chromatography mass spectrometry (MS) can overcome the matrix effect on MS detection based on measuring the intensity ratios of metabolite peak pairs detected in a mixture of a light-isotope labeled sample and a heavy-isotope labeled reference sample. However, the chemical labeling process itself may encounter matrix effect which can influence the overall quantitative results. In this work, we report the effects of salts and buffers commonly present in metabolomic samples on dansylation labeling. It is shown that high concentrations of NaCl and phosphate buffer (>50 mM) or PBS can reduce or enhance the labeling efficiencies of metabolites. By maintaining similar matrix contents in an individual sample versus a reference sample, relative quantification of metabolites can be performed without compromising the metabolomic profiling results. For samples containing varying amounts of high salts such as urine, we demonstrate that the matrix effect can be largely overcome by diluting the original sample before dansylation labeling (e.g., fourfold dilution for urine).

Development of versatile isotopic labeling reagents for profiling the amine submetabolome by liquid chromatography–mass spectrometry

Metabolomic profiling involves relative quantification of metabolites in comparative samples and identification of the significant metabolites that differentiate different groups (e.g., diseased vs. controls). Chemical isotope labeling (CIL) liquid chromatography–mass spectrometry (LC–MS) is an enabling technique that can provide improved metabolome coverage and metabolite quantification. However, chemical identification of labeled metabolites can still be a challenge. In this work, a new set of isotopic labeling reagents offering versatile properties to enhance both detection and identification are described. They were prepared by a glycine molecule (or its isotopic counterpart) and an aromatic acid with varying structures through a simple three-step synthesis route. In addition to relatively low costs of synthesizing the reagents, this reaction route allows adjusting reagent property in accordance with the desired application objective. To date, two isotopic reagents, 4-dimethylaminobenzoylamido acetic acid N-hydroxylsuccinimide ester (DBAA-NHS) and 4-methoxybenzoylamido acetic acid N-hydroxylsuccinimide ester (MBAA-NHS), for labeling the amine-containing metabolites (i.e., amine submetabolome) have been synthesized. The labeling conditions and the related LC–MS method have been optimized. We demonstrate that DBAA labeling can increase the metabolite detectability because of the presence of an electrospray ionization (ESI)-active dimethylaminobenzoyl group. On the other hand, MBAA labeled metabolites can be fragmented in MS/MS and pseudo MS3 experiments to provide structural information on metabolites of interest. Thus, these reagents can be tailored to quantitative profiling of the amine submetabolome as well as metabolite identification in metabolomics applications.

Metabolic characterization of triple negative breast cancer.

BackgroundThe aims of this study were to characterize the metabolite profiles of triple negative breast cancer (TNBC) and to investigate the metabolite profiles associated with human epidermal growth factor receptor-2/neu (HER-2) overexpression using ex vivo high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolic alterations caused by the different estrogen receptor (ER), progesterone receptor (PgR) and HER-2 receptor statuses were also examined. To investigate the metabolic differences between two distinct receptor groups, TNBC tumors were compared to tumors with ERpos/PgRpos/HER-2pos status which for the sake of simplicity is called triple positive breast cancer (TPBC).MethodsThe study included 75 breast cancer patients without known distant metastases. HR MAS MRS was performed for identification and quantification of the metabolite content in the tumors. Multivariate partial least squares discriminant analysis (PLS-DA) modeling and relative metabolite quantification were used to analyze the MR data.ResultsCholine levels were found to be higher in TNBC compared to TPBC tumors, possibly related to cell proliferation and oncogenic signaling. In addition, TNBC tumors contain a lower level of Glutamine and a higher level of Glutamate compared to TPBC tumors, which indicate an increase in glutaminolysis metabolism. The development of glutamine dependent cell growth or “Glutamine addiction” has been suggested as a new therapeutic target in cancer. Our results show that the metabolite profiles associated with HER-2 overexpression may affect the metabolic characterization of TNBC. High Glycine levels were found in HER-2pos tumors, which support Glycine as potential marker for tumor aggressiveness.ConclusionsMetabolic alterations caused by the individual and combined receptors involved in breast cancer progression can provide a better understanding of the biochemical changes underlying the different breast cancer subtypes. Studies are needed to validate the potential of metabolic markers as targets for personalized treatment of breast cancer subtypes.

Effects of sample injection amount and time-of-flight mass spectrometric detection dynamic range on metabolome analysis by high-performance chemical isotope labeling LC-MS.

In chemical isotope labeling (CIL) LC-MS, relative metabolite quantification is done by measuring the peak ratio of a (13)C2-/(12)C2-labeled peak pair for a given metabolite present in two comparative samples. The dynamic range of peak ratio measurement does not need to be very large, as only subtle changes of metabolite concentrations are encountered in most metabolomic studies where relative metabolome quantification of different groups of samples is performed. However, the absolute concentrations of different metabolites can be very different, requiring a technique to provide a wide detection dynamic range to allow the detection of as many peak pairs as possible. In this work, we demonstrated that controlling the sample injection amount into LC-MS was critical to achieve the optimal detectability while avoiding sample carry-over problem. In addition, the use of a high-dynamic-range TOF system increased the number of peak pairs detected, compared to a conventional TOF system. We also investigated the ionization and detection saturation factors limiting the dynamic range of detection. This article is part of a Special Issue entitled: Protein dynamics in health and disease. Guest Editors: Pierre Thibault and Anne-Claude Gingras.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research.

Many untargeted LC–ESI–HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly 13C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-13C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC–HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87–135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-13C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.

Isotopic Labeling of Metabolites in Drug Discovery Applications

Understanding the metabolic and pharmacokinetic fate of a drug in humans is a key factor in its development and registration, as well as in the elaboration of new therapeutic agents. To carry out these studies, stable isotope labeling techniques have been effectively used by drug metabolism scientists and toxicologists in order to gain better understanding of the drugs' disposition, bioavailability and toxicity in in vivo studies. Among the different analytical techniques used, mass spectrometry (MS) coupled to separation techniques has become the detection method of choice due to its high sensitivity and selectivity. In vitro quantification of metabolite levels in biofluids by MS is often difficult if a proper internal standard is not available due to the inherent problems associated with the technique (e.g. chromatographic coelutions, ion suppression, low reproducibility etc.). Stable isotope coding approaches alleviate these drawbacks and allow comparative drug metabolomics studies similarly to the differential proteomic techniques developed in the last decade. This review describes a selection of methodological improvements in the use of stable isotopes labeling in combination with MS to detect drug metabolites. In the first part of the paper, the application of labeled compounds to study the absorption, distribution, metabolism, excretion and toxicology of drugs (ADMET) in addition to the elucidation of metabolic pathways is presented. In the second part, recent developments of stable isotope coded tags for the in vitro relative metabolite quantification in biofluids are presented.

Reducing time and increasing sensitivity in sample preparation for adherent mammalian cell metabolomics.

A simple, fast, and reproducible sample preparation procedure was developed for relative quantification of metabolites in adherent mammalian cells using the clonal β-cell line INS-1 as a model sample. The method was developed by evaluating the effect of different sample preparation procedures on high performance liquid chromatography- mass spectrometry quantification of 27 metabolites involved in glycolysis and the tricarboxylic acid cycle on a directed basis as well as for all detectable chromatographic features on an undirected basis. We demonstrate that a rapid water rinse step prior to quenching of metabolism reduces components that suppress electrospray ionization thereby increasing signal for 26 of 27 targeted metabolites and increasing total number of detected features from 237 to 452 with no detectable change of metabolite content. A novel quenching technique is employed which involves addition of liquid nitrogen directly to the culture dish and allows for samples to be stored at -80 °C for at least 7 d before extraction. Separation of quenching and extraction steps provides the benefit of increased experimental convenience and sample stability while maintaining metabolite content similar to techniques that employ simultaneous quenching and extraction with cold organic solvent. The extraction solvent 9:1 methanol: chloroform was found to provide superior performance over acetonitrile, ethanol, and methanol with respect to metabolite recovery and extract stability. Maximal recovery was achieved using a single rapid (∼1 min) extraction step. The utility of this rapid preparation method (∼5 min) was demonstrated through precise metabolite measurements (11% average relative standard deviation without internal standards) associated with step changes in glucose concentration that evoke insulin secretion in the clonal β-cell line INS-1.

Dual Labeling of Metabolites for Metabolome Analysis (DLEMMA): A New Approach for the Identification and Relative Quantification of Metabolites by Means of Dual Isotope Labeling and Liquid Chromatography−Mass Spectrometry

Advanced metabolomics technologies are anticipated to permit the identification and quantification of metabolites at the whole-metabolome scale. Yet, most of the metabolites either remain unknown or cannot be identified unambiguously. Moreover, the present approaches suffer from inaccuracies in relative quantification because of sample preparation and matrix effects. Here we present Dual Labeling of Metabolites for Metabolome Analysis (DLEMMA) as a valuable tool, which with analogy to DNA array assays enables the identification and relative quantification of differential metabolites in a single sample. DLEMMA was demonstrated as an efficient method for reducing the number of possible chemical structures assigned that exhibit the same elemental composition. Its strength was exemplified by the discovery of 10 novel Tryptophan derivatives. Furthermore, employing DLEMMA by feeding two Phenylalanine-labeled precursors, we could detect differential metabolites between transgenic and control plants. The accuracy of relative quantification is also enhanced since DLEMMA provides identical matrixes for both samples, thus avoiding the effects of different complex biological matrixes on electrospray ionization. Hence, DLEMMA will complement and contribute to the advancement of metabolomics technologies and boost metabolic pathway discovery in diverse organisms.

Metabolite Quantification in Tumours by Magnetic Resonance Spectroscopy: Objectives, Results and Perspectives

Magnetic resonance spectroscopy (MRS) allows non invasive detection of tissue metabolism. Absolute or relative quantification of metabolites and chemical compounds detected by 1H and 31P MRS can today be performed in a number of pathological tissues including different types of cancer lesions, consequently improving accuracy in disease diagnosis and prognosis. Methods allowing quantification of tumour metabolites under in vivo MR spectral profiles have been progressively developed in the past and are now entering the clinical routine. Several methods of quantification have been proposed and validated in healthy tissues in a number of single- and multi-centre studies. Most of these approaches are not always directly applicable to cancer lesions because of their inherent heterogeneity or variability in water content, thus peak area ratios can, in some cases, represent better indicators of disease progression and response to therapy. An additional important limitation may derive from the excessive duration of some MRS measurement protocols, which would be added to a routine clinical MRI examination in the same session. We propose a critical overview of the principal methods currently applied for the quantification of metabolites and chemical compounds, mostly detected by 1H MRS in tumours.

Differential 12C-/13C-Isotope Dansylation Labeling and Fast Liquid Chromatography/Mass Spectrometry for Absolute and Relative Quantification of the Metabolome

We report a new quantitative metabolome profiling technique based on differential (12)C-/(13)C-isotope dansylation labeling of metabolites, fast liquid chromatography (LC) separation and electrospray ionization Fourier-transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) detection. An isotope reagent, (13)C-dansyl chloride, can be readily synthesized. This reagent, along with (12)C-dansyl chloride, provides a simple and robust means of labeling metabolites containing primary amine, secondary amine, or phenolic hydroxyl group(s). It is shown that dansylation labeling offers 1-3 orders of magnitude ESI signal enhancement over the underivatized counterparts. Dansylation alters the chromatographic behaviors of polar and ionic metabolites normally not retainable on a reversed phase (RP) column to an extent that they can be retained and separated by RPLC with high efficiency. There is no isotopic effect on RPLC separation of the differential isotope labeled metabolites, and (12)C-/(13)C-labeled isoforms of metabolites are coeluted and detected by MS for precise and accurate quantification and confident metabolite identification. It is demonstrated that, in the analysis of 20 amino acids, a linear response of over 2 orders of magnitude is achieved for relative metabolite quantification with an average relative standard deviation (RSD) of about 5.3% from replicate experiments. A dansylation standard compound library consisting of 121 known amines and phenols has been constructed and is proven to be useful for absolute metabolite quantification and MS-based metabolite identification in biological samples. As an example, the absolute concentrations of 93 metabolites, ranging from 30 nM to 2510 microM, can be determined from a pooled sample of human urines collected in 5 consecutive days labeled with (12)C-dansylation and spiked with the 121 (13)C-dansylated standards. Relative concentration variations of these metabolites in individual urine samples can also be monitored by mixing the (13)C-dansylated pooled urine sample with the (12)C-dansylated individual sample. With a 12 min fast LC separation combined with FTICR MS, 672 metabolites were detected in a human urine sample with each metabolite peak having a signal-to-noise ratio of greater than 20; the identities of most of the metabolites remain to be determined. This work illustrates that dansylation labeling and fast LC/FTICR MS can be a powerful technique for quantitative profiling of at least 672 metabolites in urine samples in 12 min.

KINETICS STUDY OF THE BIOTRANSFORMATION OF AN OLIGONUCLEOTIDE PRODRUG IN CELLS EXTRACT BY MATRIX-ASSISTED LASER DESORPTION/IONIZATION TIME-OF-FLIGHT MASS SPECTROMETRY

The fate of a dodecathymidine prodrug in cell extract was monitored by MALDI-TOF MS. This technique allows a facile identification and a relative quantification of metabolites produced. We showed that the relative peak intensities were similar to the relative metabolite proportions that permitted the determination of their half-lives. The oligonucleotide prodrug was fully metabolized to yield the T12 phosphorothioate likely through a carboxyesterase mediated mechanism.

Kinetics study of the biotransformation of an oligonucleotide prodrug in cells extract by matrix-assisted laser desorption–ionization time-of-flight mass spectrometry

The fate of a dodecathymidine prodrug in cell extract was monitored by MALDI-TOF MS. This technique allows a facile identification and a relative quantification of metabolites produced. We showed that the relative peak intensities were similar to the relative metabolite proportions that permitted the determination of their half-lives. We found a good fit between the calculated kinetics curves and the experimental points. The oligonucleotide prodrug was fully metabolized to yield the dodecathymidine phosphorothioate likely through a carboxyesterase mediated mechanism.