Chemical Isotope Labeling Liquid Chromatography Research Articles

Development of Chemical Isotope Labeling Liquid Chromatography Orbitrap Mass Spectrometry for Comprehensive Analysis of Dipeptides.

Dipeptides have recently attracted considerable attention due to their newly found biological functions and potential biomarkers of diseases. Global analysis of dipeptides (400 common dipeptides in total number) in samples of complex matrices would enable functional studies of dipeptides and biomarker discovery. In this work, we report a method for high-coverage detection and accurate relative quantification of dipeptides. This method is based on differential chemical isotope labeling (CIL) of dipeptides with dansylation and liquid chromatography Orbitrap tandem mass spectrometry (LC-Orbitrap-MS). An optimized LC gradient ensured the separation of dansyl-dipeptides, including positional isomers (e.g., leucine- and isoleucine-containing dipeptides). MS/MS collision energy in Orbitrap MS was optimized to provide characteristic fragment ion information to sequence dansyl-dipeptides. Using the optimized conditions, a CIL standard library consisting of retention time, MS, and MS/MS information of a whole set of 400 dansyl-dipeptides was constructed to facilitate rapid dipeptide identification. For qualitative analysis of dipeptides in real samples, IsoMS data processing software's parameters were tuned to improve the coverage of dipeptide annotation. Data-dependent acquisition was also carried out to improve the reliability of dipeptide identification. As examples of applications, we successfully identified a total of 321 dipeptides in rice wines and 105 dipeptides in human serum samples. For quantitative analysis, we demonstrated that the intensity ratios of the peak pairs from 96% of the dansyl-dipeptides detectable in a 1:1 mixture of 12C- and 13C-labeled rice wine samples were within ±20% of an expected value of 1.0. More than 90% of dipeptides were detected with a relative standard deviation of less than 10%, showing good performance of relative quantification.

MS-IDF: A Software Tool for Nontargeted Identification of Endogenous Metabolites after Chemical Isotope Labeling Based on a Narrow Mass Defect Filter.

Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is an emerging metabolomic strategy for the quantification and characterization of small molecular compounds in biological samples. However, its subsequent data analysis is not straightforward due to a large amount of data produced and interference of biological matrices. In order to improve the efficiency of searching and identification of target endogenous metabolites, a new software tool for nontargeted metabolomics data processing called MS-IDF was developed based on the principle of a narrow mass defect filter. The developed tool provided two function modules, including IsoFinder and MDFinder. The IsoFinder function module applied a conventional peak extraction method by using a fixed mass differences between the heavy and light labels and by the alignment of chromatographic retention time (RT). On the other hand, MDFinder was designed to incorporate the accurate mass defect differences between or among stable isotopes in the peak extraction process. By setting an appropriate filter interval, the target metabolites can be efficiently screened out while eliminating interference. Notably, the present results showed that the efficiency in compound identification using the new MDFinder module was nearly doubled as compared to the conventional IsoFinder method (an increase from 259 to 423 compounds). The Matlab codes of the developed MS-IDF software are available from github at https://github.com/jydong2018/MS_IDF. Based on the MS-IDF software tool, a novel and effective approach from nontargeted to targeted metabolomics research was developed and applied to the exploration of potential primary amine biomarkers in patients with schizophrenia. With this approach, potential biomarkers, including N,N-dimethylglycine, S-adenosine-l-methionine, dl-homocysteine, and spermidine, were discovered.

High-Coverage Metabolome Analysis Reveals Significant Diet Effects of Autoclaved and Irradiated Feed on Mouse Fecal and Urine Metabolomics.

Using metabolomics to study the relations of nutrition and health requires stringent control of the experimental conditions used in an animal model. This work investigates the diet effects of autoclaved and irradiated feed on mouse urine and fecal metabolomics. C57BL/6 mice are fed normal-irradiation sterilized diet (n = 9), autoclave sterilized diet (n = 9), and high-irradiation sterilized diet (n = 9) for 4 weeks. Differential chemical isotope labeling liquid chromatography mass spectrometry is used to quantify the metabolome variations of urine and feces collected at five time points. Significant differences are observed in urine or fecal metabolomes of mice fed autoclaved diet versus mice fed high-irradiation diet or fed normal-irradiation diet, while the differences are small between the mice fed normal-irradiation and high-irradiation diet. Correlation studies of metabolite changes of diet- and aging-related biomarkers indicate a large overlap of significantly affected metabolites by the two factors. Diet can be a confounding factor that needs to be carefully considered when a metabolomics study is designed and metabolomic results of a mouse model of nutritional or other biological study are interpreted. Using the same sterilized diet for a given metabolomics project is essential to control the diet effect.

Toxicity mechanisms of polystyrene microplastics in marine mussels revealed by high-coverage quantitative metabolomics using chemical isotope labeling liquid chromatography mass spectrometry

Marine microplastic has become an important environmental issue of global concern due to its wide distribution and harmful impacts. However, there is still insufficient information on the toxicity mechanism of microplastics to marine organisms. In this study, we developed and applied a high-coverage quantitative metabolomics technique to investigate the toxicity mechanisms of the polystyrene microspheres (micro-PS) on marine mussels (Mytilus coruscus). A total of 3599 metabolites were quantified, including 163 positively identified metabolites, 318 high-confident putatively identified metabolites, and 2602 mass-matched metabolites from the hemolymph of mussels. Metabolomics analysis indicated that micro-PS disrupted the amino acid metabolism, particularly phenylalanine metabolism, which may lead to oxidative stress and neurotoxicity. Micro-PS at environmentally relevant concentrations induced oxidative stress and immunotoxicity in mussels. After 7 days of recovery, along with the significant clearance of micro-PS by mussels, both metabolite levels and biochemical indicators generally returned to the same level as the control group. Overall, the results showed that microplastics at environmentally-relevant concentrations can cause toxic effects on mussels but these influences are reversible. We envisage the usages of high-coverage metabolomics for investigating the toxicity of various types of microplastics under many different conditions, including those relevant to the marine environment.

Plasma Carboxyl-Metabolome Is Associated with Average Daily Gain Divergence in Beef Steers.

Simple SummaryIn this study, we analyzed the plasma carboxyl-metabolome in beef steers with divergent average daily gain (ADG). Several short chain fatty acids were greater in beef steers with greater ADG. Conversely, several long chain fatty acids were greater in beef steers with lower ADG. Pathway analysis of the differential metabolites revealed alterations in abundance/activities of enzymes involved in fatty acid metabolism in the liver. The results of this study demonstrated that beef steers with divergent ADG had altered plasma carboxyl-metabolome, which is possibly caused by altered abundances and/or activities of enzymes involved in fatty acid oxidation and biosynthesis.We applied an untargeted metabolomics technique to analyze the plasma carboxyl-metabolome of beef steers with divergent average daily gain (ADG). Forty-eight newly weaned Angus crossbred beef steers were fed the same total mixed ration ad libitum for 42 days. On day 42, the steers were divided into two groups of lowest (LF: n = 8) and highest ADG (HF: n = 8), and blood samples were obtained from the two groups for plasma preparation. Relative quantification of carboxylic-acid-containing metabolites in the plasma samples was determined using a metabolomics technique based on chemical isotope labeling liquid chromatography mass spectrometry. Metabolites that differed (fold change (FC) ≥ 1.2 or ≤ 0.83 and FDR ≤ 0.05) between LF and HF were identified using a volcano plot. Metabolite set enrichment analysis (MSEA) of the differential metabolites was done to determine the metabolic pathways or enzymes that were potentially altered. In total, 328 metabolites were identified. Volcano plot analysis revealed 43 differentially abundant metabolites; several short chain fatty acids and ketone bodies had greater abundance in HF steers. Conversely, several long chain fatty acids were greater in LF steers. Five enzymatic pathways, such as fatty acyl CoA elongation and fatty-acid CoA ligase were altered based on MSEA. This study demonstrated that beef steers with divergent ADG had altered plasma carboxyl-metabolome, which is possibly caused by altered abundances and/or activities of enzymes involved in fatty acid oxidation and biosynthesis in the liver.

PSII-6 Comparative effects of two multi-species direct-fed microbial products on rumen fermentation, bacterial community and metabolome of beef steers

Abstract We examined the effects of two direct-fed microbials (DFM) containing multiple microbial species and their fermentation products on ruminal fermentation, bacterial community and metabolome of beef steers. Nine rumen-cannulated Holstein steers were assigned to 3 treatments arranged in a 3 × 3 Latin square design with three 21-d periods. Dietary treatments were (1) CON (basal diet without additive), (2) PROB (basal diet plus 19 g/d of Commence), and (3) SYNB (basal diet plus 28 g/d of RX3). Commence is a blend of active S. cerevisiae, Enterococcus lactis, Bacillus subtilis, Enterococcus faecium, and L. casei, and their fermentation products. RX3 is a blend of active S. cerevisiae and the fermentation products of S. cerevisiae, Enterococcus lactis, Bacillus licheniformis, and Bacillus subtilis. Daily dry matter intake was calculated. On d 21 of each period, ruminal contents were sampled at 3, 6, 12, and 18 h after feeding for analysis of VFA, bacterial community via 16S rRNA sequencing, and metabolome via chemical isotope labeling liquid chromatography mass spectrometry. There was no effect on DMI. Compared to CON, supplementation of either PROB or SYNB increased or tended to increase (P ≤ 0.10) total VFA, propionate, and valerate concentrations. Compared to CON, PROB reduced (P ≤ 0.05) the relative abundance of Prevotella 1 and Prevotellaceae UCG-001, but increased those of Rikenellaceae RC9, Succinivibrionaceae UCG-001, Succiniclasticum, Ruminococcaceae UCG-014, and Ruminococcaceae UCG-002, whereas SYNB decreased (P ≤ 0.05) the relative abundance of Prevotella 1 and Prevotellaceae UCG-001, and increased those of Prevotella 7, Succinivibrio, Succiniclasticum, and Ruminococcaceae UCG-014. Metabolome analysis revealed that 8 ruminal metabolites, including some amino acids, were increased (P ≤ 0.05) by PROB, whereas no differences were found for SYNB. This study demonstrated that supplemental PROB or SYNB altered the ruminal bacterial community and metabolome differently to achieve a similar ruminal fermentation pattern.

MSC-triggered metabolomic alterations in liver-resident immune cells isolated from CCl4-induced mouse ALI model

Clinical trials testing mesenchymal stem cell (MSC) as a cellular remedy for acute liver injury (ALI) are underway, but its underlying mechanism has not been thoroughly scrutinized. We highlight that the metabolomic profile of the liver-resident immune cells is significantly altered after MSC administration; its potential correlation with ALI remission is discussed in this study. C57BL/6 mice are randomly divided into three groups: the sham group, MSC-treated ALI group and PBS-treated ALI group; acute liver injury is induced by intraperitoneal injection of carbon tetrachloride. A high-performance chemical isotope labeling liquid chromatography–mass spectrometry (CIL LC–MS) is exploited to profile amine, phenol and carbonyl submetabolome of the liver-resident immune cells in different treatments. 4295 peak pairs are quantified and 2461 peak pairs are further identified in zero-reaction and one-reaction libraries. Clear separation of the three groups is observed in the global PCA and OPLS-DA analyses. We identified 256 metabolites to be candidate biomarkers for ALI-activated immunity and 114 metabolites to be candidate biomarkers for MSC-modulated immunity. Ariginine, aspartate and glutamate metabolism are most affected in both cases, with eight significantly regulated metabolites as joints (glutamic-gamma-semialdehyde, aspartate acid, glutamate acid, gamma-Aminobutyric acidorinithine, 2-keto-glutaramic acid, N-acetylornithine, citrulline and ornithine). These findings shed new light on the therapeutic benefit of immune modulation during ALI rescue. It needs to be further investigated whether exogenous supply of certain metabolites will exert a profound impact on the metabolic network, crosstalking with immune responses and modulating ALI prognosis.

Chemical isotope labeling liquid chromatography mass spectrometry for investigating acute dietary effects of cow milk consumption on human urine metabolome

Biomarker discovery has been increasingly important in the field of metabolomics for the detection and understanding of diseases. Of the many biofluids available for metabolomics, urine is a preferred option as it is non-invasive to collect and contains a wide range of metabolites reflective of the health status of the testing individual. However, urine also contains many exogenous metabolites which are introduced through various sources such as diet. This complicates the data interpretation when searching the metabolome for disease-related endogenous metabolites. Since diet is difficult to control, this work aims to study the acute effects of diet (particularly cow milk) consumption on the human urine amine/phenol submetabolome by utilizing differential chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS). LC-MS analysis of 62 urine samples collected before and after (1 hour and 2 hours) milk intake resulted in the detection of 4985 metabolites with an average of 3815 ± 206 (n = 62) detected per sample. The work aims to differentiate the exogenous “food” metabolites from the endogenous metabolite pool and to determine any dietary effects from milk intake on the human urine metabolome.

Metabolomics Analyses of Saliva Detect Novel Biomarkers of Alzheimer's Disease.

Using a non-invasive biofluid (saliva), we apply a powerful metabolomics workflow for unbiased biomarker discovery in Alzheimer's disease (AD). We profile and differentiate Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD groups. The workflow involves differential chemical isotope labeling liquid chromatography mass spectrometry using dansylation derivatization for in-depth profiling of the amine/phenol submetabolome. The total sample (N = 109) was divided in to the Discovery Phase (DP) (n = 82; 35 CN, 25 MCI, 22 AD) and a provisional Validation Phase (VP) (n = 27; 10 CN, 10 MCI, 7 AD). In DP we detected 6,230 metabolites. Pairwise analyses confirmed biomarkers for AD versus CN (63), AD versus MCI (47), and MCI versus CN (2). We then determined the top discriminating biomarkers and diagnostic panels. A 3-metabolite panel distinguished AD from CN and MCI (DP and VP: Area Under the Curve [AUC] = 1.000). The MCI and CN groups were best discriminated with a 2-metabolite panel (DP: AUC = 0.779; VP: AUC = 0.889). In addition, using positively confirmed metabolites, we were able to distinguish AD from CN and MCI with good diagnostic performance (AUC > 0.8). Saliva is a promising biofluid for both unbiased and targeted AD biomarker discovery and mechanism detection. Given its wide availability and convenient accessibility, saliva is a biofluid that can promote diversification of global AD biomarker research.

Overcoming Sample Matrix Effect in Quantitative Blood Metabolomics Using Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry.

Blood is widely used for discovery metabolomics to search for disease biomarkers. However, blood sample matrix can have a profound effect on metabolome analysis, which can impose an undesirable restriction on the type of blood collection tubes that can be used for blood metabolomics. We investigated the effect of blood sample matrix on metabolome analysis using a high-coverage and quantitative metabolome profiling technique based on differential chemical isotope labeling (CIL) LC-MS. We used 12C-/13C-dansylation LC-MS to perform relative quantification of the amine/phenol submetabolomes of four types of samples (i.e., serum, EDTA plasma, heparin plasma, and citrate plasma) collected from healthy individuals and compare their metabolomic results. From the analysis of 80 plasma and serum samples in experimental triplicate, we detected a total of 3651 metabolites with an average of 1818 metabolites per run (n = 240). The number of metabolites detected and the precision and accuracy of relative quantification were found to be independent of the sample type. Within each sample type, the metabolome data set could reveal biological variation (e.g., sex separation). Although the relative concentrations of some individual metabolites might be different in the four types of samples, for sex separation, all 66 significant metabolites with larger fold-changes (FC ≥ 2 and p < 0.05) found in at least one sample type could be found in the other types of samples with similar or somewhat reduced, but still significant, fold-changes. Our results indicate that CIL LC-MS could overcome the sample matrix effect, thereby greatly broadening the scope of blood metabolomics; any blood samples properly collected in routine clinical settings, including those in biobanks originally used for other purposes, can potentially be used for discovery metabolomics.

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.

High-Performance Chemical Isotope Labeling Liquid Chromatography–Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast

Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) for in-depth yeast metabolome profiling. Using (12)C- and (13)C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied (12)C- and (13)C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types.

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).

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.

Development of high-performance chemical isotope labeling LC-MS for profiling the human fecal metabolome.

Human fecal samples contain endogenous human metabolites, gut microbiota metabolites, and other compounds. Profiling the fecal metabolome can produce metabolic information that may be used not only for disease biomarker discovery, but also for providing an insight about the relationship of the gut microbiome and human health. In this work, we report a chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method for comprehensive and quantitative analysis of the amine- and phenol-containing metabolites in fecal samples. Differential (13)C2/(12)C2-dansyl labeling of the amines and phenols was used to improve LC separation efficiency and MS detection sensitivity. Water, methanol, and acetonitrile were examined as an extraction solvent, and a sequential water-acetonitrile extraction method was found to be optimal. A step-gradient LC-UV setup and a fast LC-MS method were evaluated for measuring the total concentration of dansyl labeled metabolites that could be used for normalizing the sample amounts of individual samples for quantitative metabolomics. Knowing the total concentration was also useful for optimizing the sample injection amount into LC-MS to maximize the number of metabolites detectable while avoiding sample overloading. For the first time, dansylation isotope labeling LC-MS was performed in a simple time-of-flight mass spectrometer, instead of high-end equipment, demonstrating the feasibility of using a low-cost instrument for chemical isotope labeling metabolomics. The developed method was applied for profiling the amine/phenol submetabolome of fecal samples collected from three families. An average of 1785 peak pairs or putative metabolites were found from a 30 min LC-MS run. From 243 LC-MS runs of all the fecal samples, a total of 6200 peak pairs were detected. Among them, 67 could be positively identified based on the mass and retention time match to a dansyl standard library, while 581 and 3197 peak pairs could be putatively identified based on mass match using MyCompoundID against a Human Metabolome Database and an Evidence-based Metabolome Library, respectively. This represents the most comprehensive profile of the amine/phenol submetabolome ever detected in human fecal samples. The quantitative metabolome profiles of individual samples were shown to be useful to separate different groups of samples, illustrating the possibility of using this method for fecal metabolomics studies.

Development of a universal metabolome-standard method for long-term LC-MS metabolome profiling and its application for bladder cancer urine-metabolite-biomarker discovery.

Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility. We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets. In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori. The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study. The resultant mixture is analyzed by LC-MS to provide relative quantification of the individual sample metabolome to UMS. UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies. In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer. UMS of human urine was prepared by (13)C2-dansyl labeling of a pooled sample from 20 healthy individuals. This method was first used to profile the discovery samples to generate a list of putative biomarkers potentially useful for bladder cancer detection and then used to analyze the verification samples about one year later. Within the discovery sample set, three-month technical reproducibility was examined using a quality control sample and found a mean CV of 13.9% and median CV of 9.4% for all the quantified metabolites. Statistical analysis of the urine metabolome data showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples. Receiver operating characteristic (ROC) test showed that the area under the curve (AUC) was 0.956 in the discovery data set and 0.935 in the verification data set. These results demonstrated the utility of the UMS method for long-term metabolomics and discovering potential metabolite biomarkers for diagnosis of bladder cancer.