Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry Research Articles

Segment Scan Mass Spectral Acquisition for Increasing the Metabolite Detectability in Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry Metabolome Analysis.

We report a segmented spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for improving the metabolite detection efficiency. In this method, the full m/z range is divided into multiple segments with the scanning of each segment to produce multiple narrow-range spectra during the LC data acquisition. These segmented spectra are separately processed to extract the peak pair information with each peak pair arising from a differentially labeled metabolite in the analysis of a mixture of 13C and 12C reagent-labeled samples. The sublists of peak pairs are merged to form the final peak pair list from the LC-MS run. Various experimental conditions, including automatic gain control (AGC) values, mass resolutions, segment m/z widths, number of segments, and total data acquisition time in the LC run, were examined to arrive at an optimal setting in the segment scan for increasing the number of detectable metabolites while maintaining the same analysis time as in the full scan. The optimal method used a segment width of 120 m/z with 60k resolution for a 16 min CIL LC-MS run. Using dansyl-labeled human urine samples as an example, we demonstrated that this method could detect 5867 peak pairs or metabolites (not features), compared to 3765 peak pairs detectable in a full scan, representing a 56% gain. Out of 5867 peak pairs, 5575 (95.0%) could be identified or mass-matched. The relative quantification accuracy was slightly reduced (81% peak pairs were within ±25% of the expected peak ratio of 1.0 in full, compared to 87% in the full scan) due to the inclusion of more low-abundance peak pairs in the segment scan. The peak ratio measurement precision was not significantly affected by the segment scan. We also showed the increase of the peak pair number detectable from 3843 in the full scan to 7273 (89% gain) using the Orbitrap operated at 120k resolution with a 60 m/z segment width when multiple repeat sample injections were used. Thus, segment scan Orbitrap MS is an enabling method for detecting coeluting metabolites in CIL LC-MS for increasing the metabolomic coverage.

Metabolic profile of irradiated whole blood by chemical isotope-labeling liquid chromatography-mass spectrometry

Irradiated blood is a new type of blood product used to prevent transfusion-associated graft-versus-host disease. However, the effects of irradiation on the metabolism of plasma, red blood cells (RBCs), and peripheral blood mononuclear cells (PBMCs) are largely unknown. We developed a workflow for testing metabolic changes in whole blood to determine the impact of irradiation by chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC–MS). Blood parameters, PBMC proliferation and apoptosis were examined before and after irradiation. Next, the amine/phenol metabolites in the blood components were assayed by 12C- and13C-dansylation labeling LC–MS. We identified 1654, 1730, and 1666 peak pairs in plasma, RBCs, and PBMCs, respectively. We screened out 367, 177, and 219 significant metabolites in plasma, RBCs, and PBMCs, respectively, by principle component analyses, volcano plots, and Venn plots. Metabolic pathway analyses showed that irradiation modulated taurine and hypotaurine metabolism in plasma and purine metabolism in RBCs and PBMCs. Changes in potential biomarkers, including an increase in hypoxanthine level and a decrease in adenine level, may be related to the dysfunction of DNA synthesis in PBMCs. The decreased AMP level in RBCs may interfere with RBC storage lesions. Our research provides a more comprehensive perspective on blood metabolism associated with irradiation.

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.

High Coverage Profiling of Carboxylated Metabolites in HepG2 Cells Using Secondary Amine-Assisted Ultrahigh-Performance Liquid Chromatography Coupled to High-Resolution Mass Spectrometry.

Carboxylic metabolites are an important class of metabolites, which widely exist in mammals with various types. Chemical isotope labeling liquid chromatography-mass spectrometry (CIL-LC-MS) has been widely used for the detection of carboxylated metabolites. However, high coverage analysis of carboxylated metabolites in biological samples is still challenging due to improper reactivity and selectivity of labeling reagents to carboxylated metabolites. In this study, we used N-methylphenylethylamine (MPEA) to label various types of carboxylated metabolites including short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs), long-chain fatty acids (LCFAs), polycarboxylic acids (polyCAs), amino acids (AAs), and aromatic acids. Additionally, metabolites containing other functional groups, such as phenol, sulfhydryl, and phosphate groups, could not be labeled under the conditions of MPEA labeling. After MPEA labeling, the detection sensitivity of carboxylic acids was increased by 1-2 orders of magnitude, and their chromatographic retention on a reversed-phase (RP) column was enhanced (RT > 3 min). Under optimized labeling conditions, we used MPEA and d3-N-methylphenylethylamine (d3-MPEA) for high coverage screening of carboxylated metabolites in HepG2 cells by ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS). As a result, a total of 403 potential carboxylated metabolites were obtained of which 68 were confirmed based on our established in-house chemically labeled metabolite database (CLMD). SCFAs, MCFAs, LCFAs, polyCAs, AAs, and aromatic acids were all detected in HepG2 cell extracts. Due to the successful identification of AAs, the current method increased the coverage of carboxylated metabolites compared with our previous work. Moreover, 133 and 109 carboxylated metabolites with changed contents were obtained in HepG2 cells incubated with curcumin and R-3-hydroxybutyric acid, respectively. In general, our established method realized high coverage analysis of carboxylated metabolites in HepG2 cells.

High tolerance to instrument drifts by differential chemical isotope labeling LC-MS: A case study of the effect of LC leak in long-term sample runs on quantitative metabolome analysis.

Metabolomics study of a biological system often involves the analysis of many comparative samples over a period of several days or weeks. This process of long-term sample runs can encounter unexpected instrument drifts such as small leaks in liquid chromatography-mass spectrometry (LC-MS), degradation of column performance, and MS signal intensity change. A robust analytical method should ideally tolerate these instrumental drifts as much as possible. In this work, we report a case study to demonstrate the high tolerance of differential chemical isotope labeling (CIL) LC-MS method for quantitative metabolome analysis. In a study of using a rat model to examine the metabolome changes during rheumatoid arthritis (RA) disease development and treatment, over 468 samples were analyzed over a period of 15 days in three batches. During the sample runs, a small leak in LC was discovered after a batch of analyses was completed. Reanalysis of these samples was not an option as sample amounts were limited. To overcome the problem caused by the small leak, we applied a method of retention time correction to the LC-MS data to align peak pairs from different runs with different degrees of leak, followed by peak ratio calculation and analysis. Herein, we illustrate that using 12 C-/13 C-peak pair intensity values in CIL LC-MS as a measurement of concentration changes in different samples could tolerate the signal drifts, while using the absolute intensity values (ie, 12 C-peak as in conventional LC-MS) was not as reliable. We hope that the case study illustrated and the method of overcoming the small-leak-caused signal drifts can be helpful to others who may encounter this kind of situation in long-term sample runs.

Metabolomic Coverage of Chemical-Group-Submetabolome Analysis: Group Classification and Four-Channel Chemical Isotope Labeling LC-MS

Chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) is a powerful technique for in-depth metabolome analysis with high quantification accuracy. Unlike conventional LC-MS, it analyzes chemical-group-based submetabolomes and uses the combined results to represent the whole metabolome. Due to analysis time and cost constraint, not all submetabolomes can be profiled and thus knowledge of chemical group classification is important in guiding submetabolome selection. Herein we report a study of determining the distribution of functional groups of compounds in a database and then examine how well we can experimentally analyze the major chemical groups in two representative samples (i.e., human plasma and yeast). We developed a computer algorithm to classify chemical structures according to their functional groups. After removing lipids which are targeted molecules in lipidomic analysis, inorganic species and other molecules that are unique to drug, food, plant, and environmental origins, five groups (i.e., amine, phenol, hydroxyl, carboxyl, and carbonyl) are found to be the dominant classes. In the databases of MCID (2683 filtered metabolites), HMDB (5506), KEGG (11598), YMDB (1107), and ECMDB (1462), 94.7%, 85.7%, 86.4%, 85.7%, and 95.8% of the filtered metabolites belong to one or more of the five groups, respectively. These groups can be analyzed in four-channel CIL LC-MS where hydroxyls (H), amines and phenols (A), carboxyls (C), and carbonyls or ketones/aldehydes (K) are separately profiled as individual channels using dansyl and DmPA labeling reagents. A total of 7431 peak pairs were detected with 6109 unique-mass pairs from plasma, while 5629 pairs with 4955 unique-mass pairs were detected in yeast. Compared to group distributions of database compounds, hydroxyl-containing metabolites were severely underdetected, which might indicate that the current method is less than optimal for analyzing this group of metabolites. As a result, the overall experimental coverage is likely significantly lower than the database-derived coverage. In short, this study has shown that high metabolome coverage is theoretically attainable by analyzing only the H, A, C, and K submetabolomes and the group classification information should be helpful in guiding future analytical method development and choices of submetabolomes to be analyzed.

Development of chemical isotope labeling LC-MS for tissue metabolomics and its application for brain and liver metabolome profiling in Alzheimer's disease mouse model

Tissue metabolomics can play an important role in biological studies and biomarker discovery. However, high-coverage metabolome analysis of tissue samples remains a challenge. In this work, we report an analytical method for in-depth tissue metabolome profiling with highly accurate metabolite quantification. This method is based on tissue homogenization with an extraction solvent mixture of methanol, dichloromethane and water, high-performance differential chemical isotope labeling (CIL) of metabolite extracts, followed by high-resolution liquid chromatography mass spectrometry (LC-MS) detection of labeled metabolites. The method development was initially carried out using chicken liver tissue. To demonstrate the analytical performance and potential applications of this approach in real world tissue metabolomics, we examined changes in the amine/phenol submetabolome of liver and brain tissues from an Alzheimer's disease (AD) mouse model. A total of 2319 and 1769 peak pairs or amine-/phenol-containing metabolites were commonly detected in 80% of the liver samples (n = 22) and 80% of the brain samples (n = 22), respectively. In liver samples, 89 metabolites were positively identified using labeled standard library and 1063 peak pairs were putatively matched to metabolome databases, while 78 were positively identified and 753 were putatively matched in brain samples. Using multivariate and univariate analyses to study these metabolites, we observed significant metabolome differences between AD transgenic mice and wild-type mice in both liver and brain tissues, with several metabolite biomarker candidates having good discriminative power. We envisage that the CIL LC-MS method reported herein can be used in various application areas requiring in-depth analysis of tissue metabolomes.

Development of a simple and efficient method of harvesting and lysing adherent mammalian cells for chemical isotope labeling LC-MS-based cellular metabolomics

Chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) has a potential to become a very powerful platform for comprehensive and quantitative analysis of metabolites in cellular metabolomics. While CIL LC-MS with rationally designed reagents for labeling can provide much enhanced detection and quantification of metabolites, pre-analytical process such as cell harvest and cell lysis is also a critical step in the overall workflow for metabolomic analysis. In this study, we examined various combinations of cell harvest and cell lysis methods in order to develop a simple and efficient method for harvesting and lysing adherent mammalian cells tailored to CIL LC-MS-based cellular metabolomics. We evaluated and compared two cell harvest methods (physical scraping and trypsinization) and two cell lysis methods (glass-bead-assisted lysis and freeze-thaw-cycle lysis). We used two types of commonly used mammalian cells, HeLa and MCF-7 cells, to cross-validate the findings. LC-UV quantification of the labeled metabolites after dansylation of cell extracts was used to compare the overall cell harvest and cell lysis efficiencies among different combination methods. It was found that scraping combined with freeze-thaw-cycle lysis gave the highest total metabolite concentration. We also performed multivariate and univariate analyses of the amine/phenol submetabolome data generated from different preparations to investigate the impact of cell harvest and lysis methods on cellular metabolome profiles. Comparing to scraping, trypsinization caused more significant metabolome changes likely due to metabolite leakage and metabolite level changes. The cellular metabolomes obtained from the two lysis methods were found to be similar; however, freeze-thaw-cycle lysis gave a higher lysis efficacy, compared to the glass bead method. We concluded that the combination of scraping and freeze-thaw-cycle was optimal for harvesting and lysing adherent mammalian cells for CIL LC-MS metabolomics.

Development of Chemical Isotope Labeling LC-MS for Milk Metabolomics: Comprehensive and Quantitative Profiling of the Amine/Phenol Submetabolome.

Milk is a complex sample containing a variety of proteins, lipids, and metabolites. Studying the milk metabolome represents an important application of metabolomics in the general area of nutritional research. However, comprehensive and quantitative analysis of milk metabolites is a challenging task due to the wide range of variations in chemical/physical properties and concentrations of these metabolites. We report an analytical workflow for in-depth profiling of the milk metabolome based on chemical isotope labeling (CIL) and liquid chromatography mass spectrometry (LC-MS) with a focus of using dansylation labeling to target the amine/phenol submetabolome. An optimal sample preparation method, including the use of methanol at a 3:1 ratio of solvent to milk for protein precipitation and dichloromethane for lipid removal, was developed to detect and quantify as many metabolites as possible. This workflow was found to be generally applicable to profile milk metabolomes of different species (cow, goat, and human) and types. Results from experimental replicate analysis (n = 5) of 1:1, 2:1, and 1:2 12C-/13C-labeled cow milk samples showed that 95.7%, 94.3%, and 93.2% of peak pairs, respectively, had ratio values within ±50% accuracy range and 90.7%, 92.6%, and 90.8% peak pairs had RSD values of less than 20%. In the metabolomic analysis of 36 samples from different categories of cow milk (brands, batches, and fat percentages) with experimental triplicates, a total of 7104 peak pairs or metabolites could be detected with an average of 4573 ± 505 (n = 108) pairs detected per LC-MS run. Among them, 3820 peak pairs were commonly detected in over 80% of the samples with 70 metabolites positively identified by mass and retention time matches to the dansyl standard library and 2988 pairs with their masses matched to the human metabolome libraries. This unprecedentedly high coverage of the amine/phenol submetabolome illustrates the complexity of the milk metabolome. Since milk and milk products are consumed in large quantities on a daily basis, the intake of these milk metabolites even at low concentrations can be cumulatively high. The high-coverage analysis of the milk metabolome using CIL LC-MS should be very useful in future research involving the study of the effects of these metabolites on human health. It should also be useful in the dairy industry in areas such as improving milk production, developing new processing technologies, developing improved nutritional products, quality control, and milk product authentication.

Chemical Isotope Labeling LC-MS for Monitoring Disease Progression and Treatment in Animal Models: Plasma Metabolomics Study of Osteoarthritis Rat Model

We report a chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) method generally applicable for tracking metabolomic changes from samples collected in an animal model for studying disease development and treatment. A rat model of surgically induced osteoarthritis (OA) was used as an example to illustrate the workflow and technical performance. Experimental duplicate analyses of 234 plasma samples were carried out using dansylation labeling LC-MS targeting the amine/phenol submetabolome. These samples composed of 39 groups (6 rats per group) were collected at multiple time points with sham operation, OA control group, and OA rats with treatment, separately, using glucosamine/Celecoxib and three traditional Chinese medicines (Epimedii folium, Chuanxiong Rhizoma and Bushen-Huoxue). In total, 3893 metabolites could be detected and 2923 of them were consistently detected in more than 50% of the runs. This high-coverage submetabolome dataset could be used to track OA progression and treatment. Many differentiating metabolites were found and 11 metabolites including 2-aminoadipic acid, saccharopine and GABA were selected as potential biomarkers of OA progression and OA treatment. This study illustrates that CIL LC-MS is a very useful technique for monitoring incremental metabolomic changes with high coverage and accuracy for studying disease progression and treatment in animal models.

Cerebrospinal Fluid Metabolomics After Natural Product Treatment in an Experimental Model of Cerebral Ischemia.

Cerebrospinal fluid (CSF) is an important biofluid for diagnosis of and research on neurological diseases. However, in-depth metabolomic profiling of CSF remains an analytical challenge due to the small volume of samples, particularly in small animal models. In this work, we report the application of a high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) workflow for CSF metabolomics in Gastrodia elata and Uncaria rhynchophylla water extract (GUW)-treated experimental cerebral ischemia model of rat. The GUW is a commonly used Traditional Chinese Medicine (TCM) for hypertension and brain disease. This study investigated the amine- and phenol-containing biomarkers in the CSF metabolome. After GUW treatment for 7 days, the neurological deficit score was significantly improved with infarct volume reduction, while the integrity of brain histological structure was preserved. Over 1957 metabolites were quantified in CSF by dansylation LC-MS. The analysis of this comprehensive list of metabolites suggests that metabolites associated with oxidative stress, inflammatory response, and excitotoxicity change during GUW-induced alleviation of ischemic injury. This work is significant in that (1) it shows CIL LC-MS can be used for in-depth profiling of the CSF metabolome in experimental ischemic stroke and (2) identifies several potential molecular targets (that might mediate the central nervous system) and associate with pharmacodynamic effects of some frequently used TCMs.

Metabolite Analysis and Histology on the Exact Same Tissue: Comprehensive Metabolomic Profiling and Metabolic Classification of Prostate Cancer.

We report a method of metabolomic profiling of intact tissue based on molecular preservation by extraction and fixation (mPREF) and high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). mPREF extracts metabolites by aqueous methanol from tissue biopsies without altering tissue architecture and thus conventional histology can be performed on the same tissue. In a proof-of-principle study, we applied dansylation LC-MS to profile the amine/phenol submetabolome of prostate needle biopsies from 25 patient samples derived from 16 subjects. 2900 metabolites were consistently detected in more than 50% of the samples. This unprecedented coverage allowed us to identify significant metabolites for differentiating tumor and normal tissues. The panel of significant metabolites was refined using 36 additional samples from 18 subjects. Receiver Operating Characteristic (ROC) analysis showed area-under-the-curve (AUC) of 0.896 with sensitivity of 84.6% and specificity of 83.3% using 7 metabolites. A blind study of 24 additional validation samples gave a specificity of 90.9% at the same sensitivity of 84.6%. The mPREF extraction can be readily implemented into the existing clinical workflow. Our method of combining mPREF with CIL LC-MS offers a powerful and convenient means of performing histopathology and discovering or detecting metabolite biomarkers in the same tissue biopsy.

Comprehensive and Quantitative Profiling of the Human Sweat Submetabolome Using High-Performance Chemical Isotope Labeling LC-MS.

Human sweat can be noninvasively collected and used as a media for diagnosis of certain diseases as well as for drug detection. However, because of very low concentrations of endogenous metabolites present in sweat, metabolomic analysis of sweat with high coverage is difficult, making it less widely used for metabolomics research. In this work, a high-performance method for profiling the human sweat submetabolome based on chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) is reported. Sweat was collected using a gauze sponge style patch, extracted from the gauze by centrifugation, and then derivatized using CIL. Differential (12)C- and (13)C-dansylation labeling was used to target the amine/phenol submetabolome. Because of large variations in the total amount of sweat metabolites in individual samples, sample amount normalization was first performed using liquid chromatography with UV detection (LC-UV) after dansylation. The (12)C-labeled individual sample was then mixed with an equal amount of (13)C-labeled pooled sample. The mixture was subjected to LC-MS analysis. Over 2707 unique metabolites were detected across 54 sweat samples collected from six individuals with an average of 2002 ± 165 metabolites detected per sample from a total of 108 LC-MS runs. Using a dansyl standard library, we were able to identify 83 metabolites with high confidence; many of them have never been reported to be present in sweat. Using accurate mass search against human metabolome libraries, we putatively identified an additional 2411 metabolites. Uni- and multivariate analyses of these metabolites showed significant differences in the sweat submetabolomes between male and female, as well as between early and late exercise. These results demonstrate that the CIL LC-MS method described can be used to profile the human sweat submetabolome with high metabolomic coverage and high quantification accuracy to reveal metabolic differences in different sweat samples, thereby allowing the use of sweat as another human biofluid for comprehensive and quantitative metabolomics research.

Development of chemical isotope labeling liquid chromatography mass spectrometry for silkworm hemolymph metabolomics

Silkworm (Bombyx mori) is a very useful target insect for evaluation of endocrine disruptor chemicals (EDCs) due to mature breeding techniques, complete endocrine system and broad basic knowledge on developmental biology. Comparative metabolomics of silkworms with and without EDC exposure offers another dimension of studying EDCs. In this work, we report a workflow on metabolomic profiling of silkworm hemolymph based on high-performance chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) and demonstrate its application in studying the metabolic changes associated with the pesticide dichlorodiphenyltrichloroethane (DDT) exposure in silkworm. Hemolymph samples were taken from mature silkworms after growing on diet that contained DDT at four different concentrations (1, 0.1, 0.01, 0.001 ppm) as well as on diet without DDT as controls. They were subjected to differential 12C-/13C-dansyl labeling of the amine/phenol submetabolome, LC-UV quantification of the total amount of labeled metabolites for sample normalization, and LC-MS detection and relative quantification of individual metabolites in comparative samples. The total concentration of labeled metabolites did not show any significant change between four DDT-treatment groups and one control group. Multivariate statistical analysis of the metabolome data set showed that there was a distinct metabolomic separation between the five groups. Out of the 2044 detected peak pairs, 338 and 1471 metabolites have been putatively identified against the HMDB database and the EML library, respectively. 65 metabolites were identified by the dansyl library searching based on the accurate mass and retention time. Among the 65 identified metabolites, 33 positive metabolites had changes of greater than 1.20-fold or less than 0.83-fold in one or more groups with p-value of smaller than 0.05. Several useful biomarkers including serine, methionine, tryptophan, asymmetric dimethylarginine, N-Methyl-D-aspartic and tyrosine were identified. The changes of these biomarkers were likely due to the disruption of the endocrine system of silkworm by DDT. This work illustrates that the method of CIL LC-MS is useful to generate quantitative submetabolome profiles from a small volume of silkworm hemolymph with much higher coverage than conventional LC-MS methods, thereby facilitating the discovery of potential metabolite biomarkers related to EDC or other chemical exposure.

Dansylation isotope labeling liquid chromatography mass spectrometry for parallel profiling of human urinary and fecal submetabolomes

Human urine and feces can be non-invasively collected for metabolomics-based disease biomarker discovery research. Because urinary and fecal metabolomes are thought to be different, analysis of both biospecimens may generate a more comprehensive metabolomic profile that can be better related to the health state of an individual. Herein we describe a method of using differential chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) for parallel metabolomic profiling of urine and feces. Dansylation labeling was used to quantify the amine/phenol submetabolome changes among different samples based on 12C-labeling of individual samples and 13C-labeling of a pooled urine or pooled feces and subsequent analysis of the 13C-/12C-labeled mixture by LC-MS. The pooled urine and pooled feces are further differentially labeled, mixed and then analyzed by LC-MS in order to relate the metabolite concentrations of the common metabolites found in both biospecimens. This method offers a means of direct comparison of urinary and fecal submetabolomes. We evaluated the analytical performance and demonstrated the utility of this method in the analysis of urine and feces collected daily from three healthy individuals for 7 days. On average, 2534 ± 113 (n = 126) peak pairs or metabolites could be detected from a urine sample, while 2507 ± 77 (n = 63) peak pairs were detected from a fecal sample. In total, 5372 unique peak pairs were detected from all the samples combined; 3089 and 3012 pairs were found in urine and feces, respectively. These results reveal that the urine and fecal metabolomes are very different, thereby justifying the consideration of using both biospecimens to increase the probability of finding specific biomarkers of diseases. Furthermore, the CIL LC-MS method described can be used to perform parallel quantitative analysis of urine and feces, resulting in more complete coverage of the human metabolome.

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.

Quantitative Metabolome Analysis Based on Chromatographic Peak Reconstruction in Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry.

Generating precise and accurate quantitative information on metabolomic changes in comparative samples is important for metabolomics research where technical variations in the metabolomic data should be minimized in order to reveal biological changes. We report a method and software program, IsoMS-Quant, for extracting quantitative information from a metabolomic data set generated by chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). Unlike previous work of relying on mass spectral peak ratio of the highest intensity peak pair to measure relative quantity difference of a differentially labeled metabolite, this new program reconstructs the chromatographic peaks of the light- and heavy-labeled metabolite pair and then calculates the ratio of their peak areas to represent the relative concentration difference in two comparative samples. Using chromatographic peaks to perform relative quantification is shown to be more precise and accurate. IsoMS-Quant is integrated with IsoMS for picking peak pairs and Zero-fill for retrieving missing peak pairs in the initial peak pairs table generated by IsoMS to form a complete tool for processing CIL LC-MS data. This program can be freely downloaded from the www.MyCompoundID.org web site for noncommercial use.