iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination

Efficient and accurate quantitation of metabolites from LC-MS data has become an important topic. Here we present an automated tool, called iMet-Q (intelligent Metabolomic Quantitation), for label-free metabolomics quantitation from high-throughput MS1 data. By performing peak detection and peak alignment, iMet-Q provides a summary of quantitation results and reports ion abundance at both replicate level and sample level. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification. An in-house standard mixture and a public Arabidopsis metabolome data set were analyzed by iMet-Q. Three public quantitation tools, including XCMS, MetAlign, and MZmine 2, were used for performance comparison. From the mixture data set, seven standard metabolites were detected by the four quantitation tools, for which iMet-Q had a smaller quantitation error of 12% in both profile and centroid data sets. Our tool also correctly determined the charge states of seven standard metabolites. By searching the mass values for those standard metabolites against Human Metabolome Database, we obtained a total of 183 metabolite candidates. With the isotope ratios calculated by iMet-Q, 49% (89 out of 183) metabolite candidates were filtered out. From the public Arabidopsis data set reported with two internal standards and 167 elucidated metabolites, iMet-Q detected all of the peaks corresponding to the internal standards and 167 metabolites. Meanwhile, our tool had small abundance variation (≤0.19) when quantifying the two internal standards and had higher abundance correlation (≥0.92) when quantifying the 167 metabolites. iMet-Q provides user-friendly interfaces and is publicly available for download at http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html.     

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.     

Metabolic profiling of serum using Ultra Performance Liquid Chromatography and the LTQ-Orbitrap mass spectrometry system

Advances in analytical instrumentation can provide significant advantages to the volume and quality of biological knowledge acquired in metabolomic investigations. The interfacing of sub-2mum liquid chromatography (UPLC ACQUITY((R))) and LTQ-Orbitrap mass spectrometry systems provides many theoretical advantages. The applicability of the interfaced systems was investigated using a simple 11-component metabolite mix and a complex mammalian biofluid, serum. Metabolites were detected in the metabolite mix with signals that were linear with their concentration over 2.5-3.5 orders of magnitude, with correlation coefficients greater than 0.993 and limits of detection less than 1mumolL(-1). Reproducibility of retention time (RSD<3%) and chromatographic peak area (RSD<15%) and a high mass accuracy (<2ppm) were observed for 14 QC serum samples interdispersed with other serum samples, analysed over a period of 40h. The evaluation of a single deconvolution software package (XCMS) was performed and showed that two parameters (snthresh and bw) provided significant changes to the number of peaks detected and the peak area reproducibility for the dataset used. The data were used to indicate possible biomarkers of pre-eclampsia and showed both the instruments and XCMS to be applicable to the reproducible and valid detection of disease biomarkers present in serum.     

Differential mobility analysis-mass spectrometry coupled to XCMS algorithm as a novel analytical platform for metabolic profiling

The development of additional analytical instruments is of great interest to expand metabolome coverage. Differential mobility analyzers (DMAs) are a type of ion mobility spectrometers that can be straightforwardly interfaced with commercial mass spectrometers. In this pilot study, we explored the capabilities of an ion mobility-mass spectrometry platform, based on interfacing a Differential Mobility Analyzer with a commercial quadrupole time of-flight mass spectrometer (DMA-QTOF), to phenotype the metabolic urinary fingerprint of a cohort of prostate cancer patients (n = 8) and a group of healthy counterparts (n = 20). The resolving power of the DMA and the QTOF was ∼55 and ∼6,500, respectively. The transmission efficiency of the DMA was 50%. We illustrate the benefits of incorporating the DMA through the separation of isobaric species according to their electrical mobility, which were not fully resolved by the high resolution QTOF. In addition, we show that the bidimensional electrical mobility-mass spectra obtained can be successfully processed with the XCMS routine, extending its potential to ion mobility-mass spectrometry-based platforms. Data mining with XCMS revealed seven features significantly down-regulated in cancer patients (P < 0.05). These peaks were the input of principal component analysis, showing a clear separation tendency from prostate cancer patients and healthy controls. NIST MS search algorithm was used to classify the samples according to their class, with a resulting 75% sensitivity and 80% specificity. We pursued further fragmentation experiments for structural elucidation of the most discriminant metabolites, thereby illustrating the full potential of this analytical platform for the task. In summary, DMA-MS/MS provides an additional level of separation as compared to traditional mass spectrometry-based methods, thereby increasing the array of multi-analytical platforms available to global metabolite profiling and metabolite identification.

     

Organic acid profile of isovaleric acidemia: a comprehensive metabolomics approach

Isovaleric acidemia (IVA, MIM 248600) can be a severe and potentially life-threatening disease in affected neonates, but with a positive prognosis on treatment for some phenotypes. This study presents the first application of metabolomics to evaluate the metabolite profiles derived from urine samples of untreated and treated IVA patients as well as of obligate heterozygotes. All IVA patients carried the same homozygous c.367 G > A nucleotide change in exon 4 of the IVD gene but manifested phenotypic diversity. Concurrent class analysis (CONCA) was used to compare all the metabolites from the original complete data set obtained from the three case and two control groups used in this investigation. This application of CONCA has not been reported previously, and is used here to compare four different modes of scaling of all metabolites. The variables important in discrimination from the CONCA thus enabled the recognition of different metabolic patterns encapsulated within the data sets that would not have been revealed by using only one mode of scaling. Application of multivariate and univariate analyses disclosed 11 important metabolites that distinguished untreated IVA from controls. These included well-established diagnostic biomarkers of IVA, endogenous detoxification markers, and 3-hydroxycaproic acid, an indicator of ketosis, but not reported previously for this disease. Nine metabolites were identified that reflected the effect of treatment of IVA. They included detoxification products and indicators related to the high carbohydrate and low protein diet which formed the hallmark of the treatment. This investigation also provides the first comparative metabolite profile for heterozygotes of this inherited metabolic disorder. The detection of informative metabolites in even very low concentrations in all three experimental groups highlights the potential advantage of the holistic mode of analysis of inherited metabolic diseases in a metabolomics investigation.     

Detection of doxorubicin and metabolites in cell extracts and in single cells by capillary electrophoresis with laser-induced fluorescence detection

Capillary electrophoresis with laser-induced fluorescence detection was used to separate and detect doxorubicin and at least five metabolites from NS-1 cells that were treated with 25 microM doxorubicin for 8 h. Using 10 mM borate, 10 mM sodium dodecyl sulfate (pH 9.3) as separation buffer, the 488-nm argon-ion laser line for fluorescence excitation, and a 635 +/- 27.5 nm bandpass filter for detection, the limit of detection (S/N=3) for doxorubicin is 61 +/- 13 zmol. This low limit of detection allows for the detection of a larger number of metabolites than previously reported. Two extraction procedures were performed: a bulk liquid-liquid extraction and an in-capillary single-cell lysis. While in the bulk liquid-liquid extraction procedure, recovery for doxorubicin range from 50 to 99%, in single cell analysis the recovery is expected to be complete. Furthermore performing lysis of a single cell inside the separation capillary prevents doxorubicin or metabolite loss or degradation during handling. Based on the bulk method the calculated metabolite abundance is in the sub-amol per cell range while it varies from 0.1 to 1.1 fmol per cell in single cell analysis confirming metabolite loss during handling. Each metabolite was found at a level less than 0.1% of the doxorubicin content in either method, suggesting a slow metabolism in the NS-1 cell system or effective removal of metabolites by the cell.     

Differential mobility analysis-mass spectrometry coupled to XCMS algorithm as a novel analytical platform for metabolic profiling

The development of additional analytical instruments is of great interest to expand metabolome coverage. Differential mobility analyzers (DMAs) are a type of ion mobility spectrometers that can be straightforwardly interfaced with commercial mass spectrometers. In this pilot study, we explored the capabilities of an ion mobility-mass spectrometry platform, based on interfacing a Differential Mobility Analyzer with a commercial quadrupole time of-flight mass spectrometer (DMA-QTOF), to phenotype the metabolic urinary fingerprint of a cohort of prostate cancer patients (n = 8) and a group of healthy counterparts (n = 20). The resolving power of the DMA and the QTOF was ～55 and ～6,500, respectively. The transmission efficiency of the DMA was 50%. We illustrate the benefits of incorporating the DMA through the separation of isobaric species according to their electrical mobility, which were not fully resolved by the high resolution QTOF. In addition, we show that the bidimensional electrical mobility-mass spectra obtained can be successfully processed with the XCMS routine, extending its potential to ion mobility-mass spectrometry-based platforms. Data mining with XCMS revealed seven features significantly down-regulated in cancer patients (P < 0.05). These peaks were the input of principal component analysis, showing a clear separation tendency from prostate cancer patients and healthy controls. NIST MS search algorithm was used to classify the samples according to their class, with a resulting 75% sensitivity and 80% specificity. We pursued further fragmentation experiments for structural elucidation of the most discriminant metabolites, thereby illustrating the full potential of this analytical platform for the task. In summary, DMA-MS/MS provides an additional level of separation as compared to traditional mass spectrometry-based methods, thereby increasing the array of multi-analytical platforms available to global metabolite profiling and metabolite identification.     

Mass spectrometry-based metabolomics application to identify quantitative resistance-related metabolites in barley against Fusarium head blight

Quantitative resistance is generally controlled by several genes. More than 100 resistance quantitative trait loci (QTLs) have been identified in wheat and barley against Fusarium head blight (FHB), caused by Gibberella zeae (anamorph: Fusarium graminearum), implying the possible occurrence of several resistance mechanisms. The objective of this study was to apply metabolomics to identify the metabolites in barley that are related to resistance against FHB. Barley genotypes, Chevron and Stander, were inoculated with mock or pathogen during the anthesis stage. The disease severity was assessed as the proportion of spikelets diseased. The genotype Chevron (0.33) was found to have a higher level of quantitative resistance than Stander (0.88). Spikelet samples were harvested at 48 h post-inoculation; metabolites were extracted and analysed using an LC-ESI-LTQ-Orbitrap (Thermo Fisher, Waltham, MA, USA). The output was imported to an XCMS 1.12.1 platform, the peaks were deconvoluted and the adducts were sieved. Of the 1826 peaks retained, a t-test identified 496 metabolites with significant treatment effects. Among these, 194 were resistance-related (RR) constitutive metabolites, whose abundance was higher in resistant mock-inoculated than in susceptible mock-inoculated genotypes. Fifty metabolites were assigned putative names on the basis of accurate mass, fragmentation pattern and number of carbons in the formula. The RR metabolites mainly belonged to phenylpropanoid, flavonoid, fatty acid and terpenoid metabolic pathways. Selected RR metabolites were assayed in vitro for antifungal activity on the basis of fungal biomass production. The application of these RR metabolites as potential biomarkers for screening and the potential of mass spectrometry-based metabolomics for the identification of gene functions are discussed.     

Chemical Communication between the Endophytic Fungus Paraconiothyrium Variabile and the Phytopathogen Fusarium oxysporum

Paraconiothyrium variabile, one of the specific endophytic fungi isolated from the host plant Cephalotaxus harringtonia, possesses the faculty to inhibit the growth of common phytopathogens, thus suggesting a role in its host protection. A strong antagonism between the endophyte P. variabile and Fusarium oxysporum was observed and studied using optic and electronic microscopies. A disorganization of the mycelium of F. oxysporum was thus noticed. Interestingly, the biological effect of the main secondary metabolites isolated from P. variabile against F. oxysporum did not account for this strong antagonism. However, a metabolomic approach of pure fungal strains and confrontation zones using the data analysis tool XCMS were analyzed and pointed out a competition-induced metabolite production by the endophyte in the presence of the phytopathogen. Subsequent MS/MS fragmentations permitted to identify one of the induced metabolites as 13-oxo-9,11-octadecadienoic acid and highlighted a negative modulation of the biosynthesis of beauvericin, one of the most potent mycotoxin of F. oxysporum, during the competition with the endophyte.     

In situ metabolomic mass spectrometry imaging: recent advances and difficulties

MS imaging (MSI) is a remarkable new technology that enables us to determine the distribution of biological molecules present in tissue sections by direct ionization and detection. This technique is now widely used for in situ imaging of endogenous or exogenous molecules such as proteins, lipids, drugs and their metabolites, and it is a potential tool for pathological analysis and the investigation of disease mechanisms. MSI is also thought to be a technique that could be used for biomarker discovery with spatial information. The application of MSI to the study of endogenous metabolites has received considerable attention because metabolites are the result of the interactions of a system's genome with its environment and a total set of these metabolites more closely represents the phenotype of an organism under a given set of conditions. Recent studies have suggested the importance of in situ metabolite imaging in biological discovery and biomedical applications, but several issues regarding the technical application limits of MSI still remained to be resolved. In this review, we describe the capabilities of the latest MSI techniques for the imaging of endogenous metabolites in biological samples, and also discuss the technical problems and new challenges that need to be addressed for effective and widespread application of MSI in both preclinical and clinical settings.     

The potential of metabolite profiling as a selection tool for genotype discrimination in Populus

Differences between wild-type Populus tremulaxalba and two transgenic lines with modified lignin monomer composition, were interrogated using metabolic profiling. Analysis of metabolite abundance data by GC-MS, coupled with principal components analysis (PCA), successfully differentiated between lines that had distinct phenotypes, whether samples were taken from the cambial zone or non-lignifying suspension tissue cultures. Interestingly, the GC-MS analysis detected relatively few phenolic metabolites in cambial extracts, although a single metabolite associated with the differentiation between lines was directly related to the phenylpropanoid pathway or other down-stream aspects of lignin biosynthesis. In fact, carbohydrates, which have only an indirect relationship with the modified lignin monomer composition, featured strongly in the line-differentiating aspects of the statistical analysis. Traditional HPLC analysis was employed to verify the GC-MS data. These findings demonstrate that metabolic traits can be dissected reliably and accurately by metabolomic analyses, enabling the discrimination of individual genotypes of the same tree species that exhibit marked differences in industrially relevant wood traits. Furthermore, this validates the potential of using metabolite profiling techniques for marker generation in the context of plant/tree breeding for industrial applications.     

Phenotype differentiation of three E. coli strains by GC-FID and GC-MS based metabolomics

Two mutants of E. coli with deletion of sdhAB and ackA-pta genes respectively and their wild-type strains were subjected to gas chromatography-flame-ionization detection (GC-FID) and gas chromatography-mass spectrometry (GC-MS) metabolomics analysis. Intracellular metabolites of the three strains were profiled by GC-FID firstly. Methodological evaluation of the employed platform indicated that the limit of detection ranges were from 0.2 to 12.5ng for some representative metabolites and the corresponding recoveries were varied from 68.7 to 122.7%. Secondly, multivariable data analysis was applied to the acquired data sets. As expected, the three phenotypes could be easily differentiated, and the perturbed metabolite pools in the genetically modified strains were screened. Lastly, the metabolites playing key roles in the differentiation were further identified by GC-MS. It was confirmed that succinic acid and aspartic acid were similarly affected in the modified strains. But proline content was altered contrarily. Additionally, deletion of sdhAB gene also affected the growth property of relevant mutant greatly. The potential mechanism was postulated accordingly.     

Combination of 'omics' data to investigate the mechanism(s) of hydrazine-induced hepatotoxicity in Rats and to identify potential biomarkers

To gain novel insight into the molecular mechanisms underlying hydrazine-induced hepatotoxicity, mRNAs, proteins and endogenous metabolites were identified that were altered in rats treated with hydrazine compared with untreated controls. These changes were resolved in a combined genomics, proteomics and metabonomics study. Sprague-Dawley rats were assigned to three treatment groups with 10 animals per group and given a single oral dose of vehicle, 30 or 90 mg kg^-1 hydrazine, respectively. RNA was extracted from rat liver 48 h post-dosing and transcribed into cDNA. The abundance of mRNA was investigated on cDNA microarrays containing 699 rat-specific genes involved in toxic responses. In addition, proteins from rat liver samples (48 and 120/168 h post-dosing) were resolved by two-dimensional differential gel electrophoresis and proteins with changed expression levels after hydrazine treatment were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting. To elucidate how regulation was reflected in biochemical pathways, endogenous metabolites were measured in serum samples collected 48 h post-dosing by 600-MHz ¹H-NMR. In summary, a single dose of hydrazine caused gene, protein and metabolite changes, which can be related to glucose metabolism, lipid metabolism and oxidative stress. These findings support known effects of hydrazine toxicity and provide potential new biomarkers of hydrazine-induced toxicity.     

Combination of ‘omics’ data to investigate the mechanism(s) of hydrazine-induced hepatotoxicity in Rats and to identify potential biomarkers

To gain novel insight into the molecular mechanisms underlying hydrazine-induced hepatotoxicity, mRNAs, proteins and endogenous metabolites were identified that were altered in rats treated with hydrazine compared with untreated controls. These changes were resolved in a combined genomics, proteomics and metabonomics study. Sprague–Dawley rats were assigned to three treatment groups with 10 animals per group and given a single oral dose of vehicle, 30 or 90 mg?kg^?1 hydrazine, respectively. RNA was extracted from rat liver 48 h post-dosing and transcribed into cDNA. The abundance of mRNA was investigated on cDNA microarrays containing 699 rat-specific genes involved in toxic responses. In addition, proteins from rat liver samples (48 and 120/168 h post-dosing) were resolved by two-dimensional differential gel electrophoresis and proteins with changed expression levels after hydrazine treatment were identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting. To elucidate how regulation was reflected in biochemical pathways, endogenous metabolites were measured in serum samples collected 48 h post-dosing by 600-MHz ¹H-NMR. In summary, a single dose of hydrazine caused gene, protein and metabolite changes, which can be related to glucose metabolism, lipid metabolism and oxidative stress. These findings support known effects of hydrazine toxicity and provide potential new biomarkers of hydrazine-induced toxicity.     

Plasma metabolite profiles: effects of dietary phospholipids in a migratory passerine (Zonotrichia leucophrys gambelii)

Plasma metabolites, including triglycerides, beta -hydroxybutyrate, and glycerol, can be used to estimate mass change in birds. Although dietary fatty acids can be ingested and absorbed as phospholipids, they have been largely overlooked as a potential indicator of mass change. The plasma ratio of triglyceride to phospholipid could also provide insight into diet quality because a high ratio in food items indicates high relative energy content. Variability in dietary phospholipid content and triglyceride : phospholipid may also affect the relationships between metabolites and mass change. We fed Gambel's white-crowned sparrows (Zonotrichia leucophrys gambelii) diets differing in phospholipid content and triglyceride : phospholipid and measured metabolites during mass loss and mass gain. Plasma phospholipids were higher and triglyceride : phospholipid was lower in birds fed a diet higher in phospholipid content and lower in triglyceride : phospholipid. Contrary to our expectations, plasma phospholipids were negatively related to mass change. Plasma triglyceride levels were positively related to mass change and unaffected by diet. The relationships between mass change and both plasma beta -hydroxybutyrate and glycerol were affected by diet. Plasma triglyceride appears to be the most reliable metabolite predicting body mass changes, but inclusion of plasma phospholipids and triglyceride : phospholipid into metabolite profiles may provide additional information on diet quality.     

Analysis of tamoxifen and its metabolites in human plasma by gas chromatography-mass spectrometry (GC-MS) using selected ion monitoring (SIM)

A method for the analysis of tamoxifen and its metabolites in plasma from tamoxifen treated breast cancer patients, by capillary GC-MS using selected ion monitoring has been developed. Metabolite extraction was carried out on a Sep-pak C18 cartridge and metabolite purification by selective ion exchange chromatographic steps. Satisfactory recovery of radioactive standards through the extraction and purification steps was obtained. The method was shown to be accurate and precise with precision coefficient of variation values ranging from 4.3-11% for tamoxifen and its metabolites. Tamoxifen, 4-hydroxytamoxifen, metabolite Y and N-desmethyltamoxifen were identified with certainty in patient plasma on the basis of GC relative retention times and mass spectral comparison with authentic standards; because of their low abundance in plasma cis-metabolite E and 3,4-dihydroxytamoxifen could only be tentatively identified but identical GC behaviour and a satisfactory comparison of the abundance of key fragment ions was achieved. The tamoxifen and metabolite concentration ranges (ng X ml-1) in the group of patients who received 40 or 80 ng tamoxifen for 14 days were tamoxifen, 307-745; N-desmethyltamoxifen, 185-491; 4-hydroxytamoxifen, 1.4-2.5; 3,4-dihydroxytamoxifen, 0.7-2.0; metabolite Y, 19.0-112; and metabolite E1, 0.9-2.0.     

A comparison of direct infusion MS and GC-MS for metabolic footprinting of yeast mutants

Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.