High-performance Chemical Isotope Labeling Research Articles

Salivary metabolomic identification of biomarker candidates for oral melanoma and oral squamous cell carcinoma in dogs.

Oral melanoma (OM) and oral squamous cell carcinoma (OSCC) are frequently diagnosed in dogs, presenting a challenge in distinguishing them from benign oral tumors (BN). Salivary metabolomic biomarkers offer a practical solution because of saliva's direct contact with tumors and the noninvasive nature of collection. Assess the diversity and abundance of the salivary metabolome in dogs with BN, OM, and OSCC using amine/phenol submetabolome analysis and high-performance chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS). Study included 11 BN, 24 OM, 10 OSCC, and 20 healthy control dogs. Case-control cross-sectional study was conducted to assess salivary submetabolic profiles in dogs with BN, OM, and OSCC and healthy dogs. Samples were labeled with 12C-dansyl chloride and analyzed using CIL LC-MS targeted to amine- and phenol-containing metabolites for amine/phenol submetabolome analysis. Distinct clusters and significant differences in metabolite concentrations were observed among the oral cancer, BN, and control groups. A total of 154 and 66 metabolites showed significantly altered concentrations, particularly in OM and OSCC, respectively, when compared with BN (Padj < .05). Potential metabolic biomarkers were identified for each cancer, including decreased concentrations of seryl-arginine and sarcosine in OSCC. Moreover, high-confidence putative metabolites were identified, including an increase in tryptophyl-threonine and a decrease in 1,2-dihydroxynapthalene-6-sulfonic acid and hydroxyprolyl-hydroxyproline for OM. We identified high coverage of the amine/phenol submetabolome, including seryl-arginine, and sarcosine, in OSCC. Our findings emphasize the potential of these biomarkers for distinguishing between oral OSCC and BN in dogs.

Research on sweat metabolomics of athlete's fatigue induced by high intensity interval training.

Objective: Sweat is an important specimen of human metabolism, which can simply and non-invasively monitor the metabolic state of the body, and its metabolites can be used as biomarkers for disease diagnosis, while the changes of sweat metabolites before and after exercise-induced fatigue are still unclear. Methods: In this experiment, high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) was used to metabolomic 28 sweat samples before and after exercise-induced fatigue of 14 long-distance runners, also IsoMS PRO and SPSS22.0 software were used to analyze the metabolite changes and differential metabolic pathways. Results: A total of 446 metabolites with high confidence were identified, and the sweat metabolome group before and after high-intensity interval exercise-induced fatigue was obvious, among which the upregulated differential metabolites mainly included hypoxanthine, pyruvate, several amino acids, etc., while the downregulated differential metabolites mainly included amino acid derivatives, vitamin B6, theophylline, etc. Conclusion: The change of hypoxanthine concentration in sweat can be used as a good biomarker for the diagnosis of exercise-induced fatigue, while the change of pyruvate content in sweat can be used as a discriminant index for the energy metabolism mode of the body before and after exercise. The main metabolic pathways involved in differential metabolites produced before and after HIIT exercise-induced fatigue are purine metabolism and amino acid metabolism.

Transcriptional and metabolomic investigation of the stress response in snow crab during simulated transport condition (Chionoecetes opilio)

The molecular mechanisms underlying the stress response are poorly described in crustaceans. This includes the snow crab (Chionoecetes opilio), a commercially important stenotherm species distributed throughout the northern hemisphere. A better understanding of the stress response in C. opilio is desperately needed for commercial and conservation purposes. The purpose of this study was to investigate the transcriptional and metabolomic response of C. opilio exposed to stressors. Crabs were randomly assigned to 24 or 72 h treatment groups where they were exposed to conditions simulating live transport (handling and air exposure). A control group was kept in cold (2 °C) and well‑oxygenated saltwater. The hepatopancreas of the crabs was sampled to perform RNA-sequencing and high-performance chemical isotope labeling metabolomics. Differential gene expression analyses showed that classic crustaceans' stress markers, such as crustacean hyperglycemic hormones and heat shock proteins, were overexpressed in response to stressors. Tyrosine decarboxylase was also up-regulated in stressed crabs, suggesting an implication of the catecholamines tyramine and octopamine in the stress response. Deregulated metabolites revealed that low oxygen was an important trigger in the stress response as intermediate metabolites of the tricarboxylic acid cycle (TCA) accumulated. Lactate, which accumulated unevenly between crabs could potentially be used to predict mortality. This study provides new information on how stressors affect crustaceans and provides a basis for the development of stress markers in C. opilio.

Distinctive Metabolomics Patterns Associated With Insulin Resistance and Type 2 Diabetes Mellitus

Obesity is associated with an increased risk of insulin resistance (IR) and type 2 diabetes mellitus (T2DM) which is a multi-factorial disease associated with a dysregulated metabolism and can be prevented in pre-diabetic individuals with impaired glucose tolerance. A metabolomic approach emphasizing metabolic pathways is critical to our understanding of this heterogeneous disease. This study aimed to characterize the serum metabolomic fingerprint and multi-metabolite signatures associated with IR and T2DM. Here, we have used untargeted high-performance chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) to identify candidate biomarkers of IR and T2DM in sera from 30 adults of normal weight, 26 obese adults, and 16 adults newly diagnosed with T2DM. Among the 3633 peak pairs detected, 62% were either identified or matched. A group of 78 metabolites were up-regulated and 111 metabolites were down-regulated comparing obese to lean group while 459 metabolites were up-regulated and 166 metabolites were down-regulated comparing T2DM to obese groups. Several metabolites were identified as IR potential biomarkers, including amino acids (Asn, Gln, and His), methionine (Met) sulfoxide, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, serotonin, L-2-amino-3-oxobutanoic acid, and 4,6-dihydroxyquinoline. T2DM was associated with dysregulation of 42 metabolites, including amino acids, amino acids metabolites, and dipeptides. In conclusion, these pilot data have identified IR and T2DM metabolomics panels as potential novel biomarkers of IR and identified metabolites associated with T2DM, with possible diagnostic and therapeutic applications. Further studies to confirm these associations in prospective cohorts are warranted.

L-Cystine is associated with the dysconnectivity of the default-mode network and salience network in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Distributed dysconnectivity within both the default-mode network (DMN) and the salience network (SN) has been observed in ADHD. L-cystine may serve as a neuroprotective molecule and signaling pathway, as well as a biomarker of ADHD. The purpose of this study was to explore whether differential brain network connectivity is associated with peripheral L-cystine levels in ADHD patients. We recruited a total of 31 drug-naïve patients with ADHD (mean age: 10.4 years) and 29 healthy controls (mean age: 10.3 years) that underwent resting state functional magnetic resonance imaging scans. Functional connectomes were generated for each subject, and we examined the cross-sectional group difference in functional connectivity (FC) within and between DMN and SN. L-cystine plasma levels were determined using high-performance chemical isotope labeling (CIL)-based liquid chromatography-mass spectrometry (LC-MS). Compared to the control group, the ADHD group showed decreased FC of dorsal DMN (p = 0.031), as well as decreased FC of precuneus-post SN (p = 0.006) and ventral DMN-post SN (p = 0.001). The plasma L-cystine levels of the ADHD group were significantly higher than in the control group (p = 0.002). Furthermore, L-cystine levels were negatively correlated with FC of precuneus-post SN (r = −0.404, p = 0.045) and ventral DMN-post SN (r = −0.540, p = 0.007). The findings suggest that decreased synergies of DMN and SN may serve as neurobiomarkers for ADHD, while L-cystine may be involved in the pathophysiology of network dysconnectivity. Future studies on the molecular mechanism of the cystine-glutamate system in brain network connectivity are warranted.

OP0304 METABOLOMICS PROFILING OF HUMAN SERUM FOR DISCOVERING BIOMARKERS TO DIAGNOSE PSORIATIC ARTHRITIS AND ANKYLOSING SPONDYLITIS WITH HIGH SPECIFICITY

Background:High-specificity biomarkers that can differentiate PsA and AS patients from healthy people, as well as RA patients, would be extremely helpful for early diagnosis and treatment of these two diseases. Based on the hypothesis that each disease state may cause specific changes to the metabolome, metabolomics is becoming a powerful tool for biomarker discovery. In this work, we applied a high-performance chemical isotope labeling (CIL) LC-MS platform to search for biomarker candidates of PsA and AS in human serum samples.Objectives:We aimed to identify metabolite biomarkers with high specificity for PsA and AS.Methods:Serum samples were collected from 331 subjects, including 100 healthy controls, 48 PsA patients, 52 AS patients and 131 RA patients. The average age of each group was: 52.6 (control), 50.7 (PsA), 51.8 (AS) and 53.1 (RA) years. After pre-treatment, each sample was incubated with12C-dansyl chloride, which can label the amine/phenol-containing metabolites. The reference sample for relative quantification was prepared by mixing all individual samples and then labeled by13C-dansyl chloride. With this normalization, the individual samples and the reference sample were mixed at an equal amount. Finally, we used an LC-QTOF-MS platform to analyze the mixtures and measure the12C/13C peak pairs.Results:We detected 1,149 peak pairs commonly existing in the serum samples. Using our dansyl-library of 700 dansyl-labeled standards and a prediction library, which contains the predicted retention times and mass values of 3,431 dansylated human metabolites, we identified 134 and 141 peak pairs, respectively. The relative concentrations are calculated from the intensity ratios of12C/13C peak pairs. We first visualized the entire amine/phenol-submetabolome for all phenotypes using the partial least squares discriminant analysis (PLS-DA). We found that the most significant between-group separation was between healthy controls and all the patients. No significant sex or age effect was observed. Furthermore, among the three diseases, PsA and AS samples were closely clustering, while the RA group was well separated from them. Therefore, we chose a two-step diagnosis approach that first differentiates PsA patients from controls/RA patients and then filters out the AS patients wrongly classified as PsA in the first step. The same strategy was conducted for AS. Stipulating a fold change larger than 1.5 with the false discovery rate lower than 5%, we found 74 metabolites having significantly higher or lower concentrations in the PsA group compared to the control or the RA group. We selected two of these significant metabolites to build a classification model based on the linear support vector machine (SVM) method, and the area-under-the-curve (AUC) value of the resulting receiver operating characteristic (ROC) curve was 0.929 (95% confidence interval: 0.899-0.956). Similarly, 37 metabolites could differentiate AS samples from RAs and controls. A proposed diagnostic panel containing four metabolites demonstrated an AUC value of 0.890 (0.843-0.934). For the last step, distinguishing between PsA and AS, there were 15 significantly increased metabolites and 9 lowered ones. The biomarker panel consisting of the top three metabolites also achieved good discriminatory power with AUC = 0.827 (0.717-0.919).Conclusion:Isotope-labeling-LC-MS-based metabolomics has revealed biomarker candidates that can specifically differentiate PsA or AS patients from control populations.Disclosure of Interests:Wei Han: None declared, Xiaohang Wang: None declared, Liang Li: None declared, Stephanie Wichuk: None declared, Edna Hutchings: None declared, Rana Dadashova: None declared, Joel Paschke: None declared, Walter P Maksymowych Grant/research support from: Received research and/or educational grants from Abbvie, Novartis, Pfizer, UCB, Consultant of: WPM is Chief Medical Officer of CARE Arthritis Limited, has received consultant/participated in advisory boards for Abbvie, Boehringer, Celgene, Eli-Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Speakers bureau: Received speaker fees from Abbvie, Janssen, Novartis, Pfizer, UCB.

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.

Controlling Preanalytical Process in High-Coverage Quantitative Metabolomics: Spot-Sample Collection for Mouse Urine and Fecal Metabolome Profiling.

Compared to conventional MS and NMR techniques, high-performance chemical isotope labeling (CIL) LC-MS provides accurate relative quantification of many more metabolites in biological samples. However, to apply this technique for urine and fecal metabolomics studies of animal models, the entire workflow, including the preanalytical process, needs to be strictly controlled to avoid or minimize quantitative errors. In this study, we report our investigation of the effects of mouse urine and fecal sample collection methods on CIL LC-MS metabolome analysis. Metabolic-cage collection and spot-sample collection of urine and feces were compared in a mouse model of CCl4-induced liver disease. 13C-/12C-dansylation LC-MS was used for quantitative profiling of the amine-/phenol-submetabolome changes. A total of 5026, 4963, 4238, and 4600 peak pairs or metabolites were detected from spot urine, spot feces, cage-collected urine, and cage-collected feces, respectively. It was found that samples collected using metabolic cages, widely used in low coverage metabolomics, could be contaminated with food as well as cross-specimen (urine in feces or feces in urine) to the extent that metabolomic comparison of different groups of mice could be seriously compromised in high-coverage metabolomics. In contrast, spot urine and spot feces could be collected without contamination and should be used in CIL LC-MS metabolomics.

Bretschneider solution-induced alterations in the urine metabolome in cardiac surgery patients

The development of Bretschneider’s histidine-tryptophan-ketoglutarate (HTK) cardioplegia solution represented a major advancement in cardiac surgery, offering significant myocardial protection. However, metabolic changes induced by this additive in the whole body have not been systematically investigated. Using an untargeted mass spectrometry-based method to deeply explore the urine metabolome, we sought to provide a holistic and systematic view of metabolic perturbations occurred in patients receiving HTK. Prospective urine samples were collected from 100 patients who had undergone cardiac surgery, and metabolomic changes were profiled using a high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method. A total of 14,642 peak pairs or metabolites were quantified using differential 13C-/12C-dansyl labeling LC-MS, which targets the amine/phenol submetabolome from urine specimens. We identified 223 metabolites that showed significant concentration change (fold change > 5) and assembled several potential metabolic pathway maps derived from these dysregulated metabolites. Our data indicated upregulated histidine metabolism with subsequently increased glutamine/glutamate metabolism, altered purine and pyrimidine metabolism, and enhanced vitamin B6 metabolism in patients receiving HTK. Our findings provide solid evidence that HTK solution causes significant perturbations in several metabolic pathways and establish a basis for further study of key mechanisms underlying its organ-protective or potential harmful effects.

Dansylhydrazine Isotope Labeling LC-MS for Comprehensive Carboxylic Acid Submetabolome Profiling.

High-performance chemical isotope labeling (CIL) LC-MS is an important tool for profiling chemical-group-based submetabolomes using different labeling chemistries for quantitative metabolomics. Metabolites containing carboxylic acid groups are a class of molecules playing diverse and significant roles in many metabolic pathways. We report a relatively simple and convenient method for analyzing the carboxyl submetabolome with high coverage. Dansylhydrazine (DnsHz) labeling of the carboxylic acid group in metabolites is used to improve both separation and ionization in LC-MS. Using differential 12C- and 13C-DnsHz reagents for labeling individual samples and a reference sample (e.g., a pooled sample), accurate relative quantification of individual metabolites among comparative samples can be achieved. DnsHz labeling of carboxylic acids could be carried out at room temperature in water-containing solution in 2 h. A labeled-carboxyl-standard library consisting of 193 endogenous human metabolites was constructed for metabolite identification, which covers more than 55 pathways. To demonstrate the performance of this method, analysis of triplicate human urine samples with duplicate injections ( n = 6) was carried out. A total of 2266 peak pairs or metabolites were commonly detected in all six runs; among them, 81 peak pairs were positively identified and 517 and 1445 peak pairs were matched in the zero- and one-reaction MyCompoundID metabolome libraries, respectively, on the basis of accurate mass search. The possibility of using this method for comprehensive profiling of the carboxyl submetabolome in real world samples was demonstrated in metabolome comparison of 30 urine samples of female and male individuals as well as in absolute quantification of two urinary acids.

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.

Applying quantitative metabolomics based on chemical isotope labeling LC-MS for detecting potential milk adulterant in human milk

There is an increasing demand for donor human milk to feed infants for various reasons including that a mother may be unable to provide sufficient amounts of milk for their child or the milk is considered unsafe for the baby. Selling and buying human milk via the Internet has gained popularity. However, there is a risk of human milk sold containing other adulterants such as animal or plant milk. Analytical tools for rapid detection of adulterants in human milk are needed. We report a quantitative metabolomics method for detecting potential milk adulterants (soy, almond, cow, goat and infant formula milk) in human milk. It is based on the use of a high-performance chemical isotope labeling (CIL) LC-MS platform to profile the metabolome of an unknown milk sample, followed by multivariate or univariate comparison of the resultant metabolomic profile with that of human milk to determine the differences. Using dansylation LC-MS to profile the amine/phenol submetabolome, we could detect an average of 4129 ± 297 (n = 9) soy metabolites, 3080 ± 470 (n = 9) almond metabolites, 4256 ± 136 (n = 18) cow metabolites, 4318 ± 198 (n = 9) goat metabolites, 4444 ± 563 (n = 9) infant formula metabolites, and 4020 ± 375 (n = 30) human metabolites. This high level of coverage allowed us to readily differentiate the six different types of samples. From the analysis of binary mixtures of human milk containing 5, 10, 25, 50 and 75% other type of milk, we demonstrated that this method could be used to detect the presence of as low as 5% adulterant in human milk. We envisage that this method could be applied to detect contaminant or adulterant in other types of food or drinks.

Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells.

In cellular metabolomics, it is desirable to carry out metabolomic profiling using a small number of cells in order to save time and cost. In some applications (e.g., working with circulating tumor cells in blood), only a limited number of cells are available for analysis. In this report, we describe a method based on high-performance chemical isotope labeling (CIL) nanoflow liquid chromatography mass spectrometry (nanoLC-MS) for high-coverage metabolomic analysis of small numbers of cells (i.e., ≤10000 cells). As an example, 12C-/13C-dansyl labeling of the metabolites in lysates of 100, 1000, and 10000 MCF-7 breast cancer cells was carried out using a new labeling protocol tailored to handle small amounts of metabolites. Chemical-vapor-assisted ionization in a captivespray interface was optimized for improving metabolite ionization and increasing robustness of nanoLC-MS. Compared to microflow LC-MS, the nanoflow system provided much improved metabolite detectability with a significantly reduced sample amount required for analysis. Experimental duplicate analyses of biological triplicates resulted in the detection of 1620 ± 148, 2091 ± 89 and 2402 ± 80 (n = 6) peak pairs or metabolites in the amine/phenol submetabolome from the 12C-/13C-dansyl labeled lysates of 100, 1000, and 10000 cells, respectively. About 63-69% of these peak pairs could be either identified using dansyl labeled standard library or mass-matched to chemical structures in human metabolome databases. We envisage the routine applications of this method for high-coverage quantitative cellular metabolomics using a starting material of 10000 cells. Even for analyzing 100 or 1000 cells, although the metabolomic coverage is reduced from the maximal coverage, this method can still detect thousands of metabolites, allowing the analysis of a large fraction of the metabolome and focused analysis of the detectable metabolites.

Development of High-Performance Chemical Isotope Labeling LC-MS for Profiling the Carbonyl Submetabolome.

Metabolites containing a carbonyl group represent several important classes of molecules including various forms of ketones and aldehydes such as steroids and sugars. We report a high-performance chemical isotope labeling (CIL) LC-MS method for profiling the carbonyl submetabolome with high coverage and high accuracy and precision of relative quantification. This method is based on the use of dansylhydrazine (DnsHz) labeling of carbonyl metabolites to change their chemical and physical properties to such an extent that the labeled metabolites can be efficiently separated by reversed phase LC and ionized by electrospray ionization MS. In the analysis of six standards representing different carbonyl classes, acetaldehyde could be ionized only after labeling and MS signals were significantly increased for other 5 standards with an enhancement factor ranging from ∼15-fold for androsterone to ∼940-fold for 2-butanone. Differential 12C- and 13C-DnsHz labeling was developed for quantifying metabolic differences in comparative samples where individual samples were separately labeled with 12C-labeling and spiked with a 13C-labeled pooled sample, followed by LC-MS analysis, peak pair picking, and peak intensity ratio measurement. In the replicate analysis of a 1:1 12C-/13C-labeled human urine mixture (n = 6), an average of 2030 ± 39 pairs per run were detected with 1737 pairs in common, indicating the possibility of detecting a large number of carbonyl metabolites as well as high reproducibility of peak pair detection. The average RSD of the peak pair ratios was 7.6%, and 95.6% of the pairs had a RSD value of less than 20%, demonstrating high precision for peak ratio measurement. In addition, the ratios of most peak pairs were close to the expected value of 1.0 (e.g., 95.5% of them had ratios of between 0.67 and 1.5), showing the high accuracy of the method. For metabolite identification, a library of DnsHz-labeled standards was constructed, including 78 carbonyl metabolites with each containing MS, retention time (RT), and MS/MS information. This library and an online search program for labeled carbonyl metabolite identification based on MS, RT, and MS/MS matches have been implemented in a freely available Website, www.mycompoundid.org . Using this library, out of the 1737 peak pairs detected in urine, 33 metabolites were positively identified. In addition, 1333 peak pairs could be matched to the metabolome databases with most of them belonging to the carbonyl metabolites. These results show that 12C-/13C-DnsHz labeling LC-MS is a useful tool for profiling the carbonyl submetabolome of complex samples with high coverage.

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.

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.

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.

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.