Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles

The non-supervised construction of a mass spectral and retention time index data base (MS/RI library) from a set of plant metabolic profiles covering major organs of potato (Solanum tuberosum), tobacco (Nicotiana tabaccum), and Arabidopsis thaliana, was demonstrated. Typically 300-500 mass spectral components with a signal to noise ratio > or =75 were obtained from GC/EI-time-of-flight (TOF)-MS metabolite profiles of methoxyaminated and trimethylsilylated extracts. Profiles from non-sample controls contained approximately 100 mass spectral components. A MS/RI library of 6205 mass spectral components was accumulated and applied to automated identification of the model compounds galactonic acid, a primary metabolite, and 3-caffeoylquinic acid, a secondary metabolite. Neither MS nor RI alone were sufficient for unequivocal identification of unknown mass spectral components. However library searches with single bait mass spectra of the respective reference substance allowed clear identification by mass spectral match and RI window. Moreover, the hit lists of mass spectral searches were demonstrated to comprise candidate components of highly similar chemical nature. The search for the model compound galactonic acid allowed identification of gluconic and gulonic acid among the top scoring mass spectral components. Equally successful was the exemplary search for 3-caffeoylquinic acid, which led to the identification of quinic acid and of the positional isomers, 4-caffeoylquinic acid, 5-caffeoylquinic acid among other still non-identified conjugates of caffeic and quinic acid. All identifications were verified by co-analysis of reference substances. Finally we applied hierarchical clustering to a complete set of pair-wise mass spectral comparisons of unknown components and reference substances with known chemical structure. We demonstrated that the resulting clustering tree depicted the chemical nature of the reference substances and that most of the nearest neighbours represented either identical components, as judged by co-elution, or conformational isomers exhibiting differential retention behaviour. Unknown components could be classified automatically by grouping with the respective branches and sub-branches of the clustering tree.     

微生物学研究中的软电离质谱技术

包括基质辅助激光解吸电离（ＭＡＬＤＩ）和电喷雾（ＥＳＩ）在内的软电离质谱是最近发展起来的质谱技术,由于这些电离方式对样品的破坏性小,质量测定范围大,分子量测定准确,样品纯度要求不高很适合分析成分复杂的微生物样品,ＭＡＬＩＤＩ－ＴＯＦ－ＭＳ结合高分辨率的二维ＳＤＳ－ＰＡＧＥ可以分析１０＾－１２摩尔水平的蛋白,是细菌蛋白质研究过程中必不可少的工具。最近的研究工作表明,通过ＭＡＩＤＩ－ＴＯＦ－ＭＳ或ＨＰ     

The identification of anaerobic bacteria using MALDI-TOF MS

Matrix Assisted Laser Desorption and Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has gained more and more popularity for the identification of bacteria. Several studies show that bacterial diagnosticis is being revolutionized by the application of MALDI-TOF MS. For anaerobic bacteria, MALDI-TOF MS has been used for the identification of Prevotella spp., Fusobacterium spp., Clostridium spp., Bacteroides spp. and Gram-positive anaerobic cocci. However, to identify bacteria reliably, an extensive database is essential. For routine identification of anaerobic bacteria available databases need to be optimised.     

A hemoglobin fragment found in cervicovaginal fluid from women in labor potentiates the action of agents that promote contraction of smooth muscle cells

We employed a proteomic approach to search for peptides that have a physiological role in labor. Cervicovaginal secretions were collected at term from women in labor and women at term not in labor. Samples were spotted onto weak cation exchange chips (WCX-2) and analyzed using Surface-Enhanced Laser Desorption Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS). Spectra were obtained for each sample and Biomarker Wizard analysis revealed 25 peaks that had significantly different peak intensity between the labor and non-laboring women. The sequences of five peaks that were significantly elevated in the labor cohort were determined using Protein Chip Interface Quadruple Time-of-Flight Mass Spectrometry (PCI-QTOF-MS). All of these peaks were identified as fragments of alpha or beta-hemoglobin (Hb). A 2.022 kDa fragment of alpha-Hb (amino acids 110-128, NH2-AAHLPAEFTPAVHASLDKF-COOH) was found to potentiate smooth muscle cell contraction in response to bradykinin, oxytocin and prostaglandin-F2alpha. This peptide may promote vasoconstriction and augment normal labor through enhancing the action of uterotonins.     

Mass spectrometry imaging: Towards a lipid microscope?

Biological imaging techniques are the most efficient way to locally measure the variation of different parameters on tissue sections. These analyses are gaining increasing interest since 20 years and allow observing extremely complex biological phenomena at lower and lower time and resolution scale. Nevertheless, most of them only target very few compounds of interest, which are chosen a priori, due to their low resolution power and sensitivity. New chemical imaging technique has to be introduced in order to overcome these limitations, leading to more informative and sensitive analyses for biologists and physicians.Two major mass spectrometry methods can be efficiently used to generate the distribution of biological compounds over a tissue section. Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry (MALDI-MS) needs the co-crystallization of the sample with a matrix before to be irradiated by a laser, whereas the analyte is directly desorbed by a primary ion bombardment for Secondary Ion Mass Spectrometry (SIMS) experiments. In both cases, energy used for desorption/ionization is locally deposited -some tens of microns for the laser and some hundreds of nanometers for the ion beam- meaning that small areas over the surface sample can be separately analyzed. Step by step analysis allows spectrum acquisitions over the tissue sections and the data are treated by modern informatics software in order to create ion density maps, i.e., the intensity plot of one specific ion versus the (x,y) position.Main advantages of SIMS and MALDI compared to other chemical imaging techniques lie in the simultaneous acquisition of a large number of biological compounds in mixture with an excellent sensitivity obtained by Time-of-Flight (ToF) mass analyzer. Moreover, data treatment is done a posteriori, due to the fact that no compound is selectively marked, and let us access to the localization of different lipid classes in only one complete acquisition.     

Potential of gas chromatography–atmospheric pressure chemical ionization–time-of-flight mass spectrometry for the determination of sterols in human plasma

The application of Gas Chromatography (GC)–Atmospheric Pressure Chemical Ionization (APCI)–Time-of-Flight Mass Spectrometry (TOF-MS) is presented for sterol analysis in human plasma. A commercial APCI interface was modified to ensure a well-defined humidity which is essential for controlled ionization. In the first step, optimization regarding flow rates of auxiliary gases was performed by using a mixture of model analytes. Secondly, the qualitative and quantitative analysis of sterols including oxysterols, cholesterol precursors, and plant sterols as trimethylsilyl-derivatives was successfully carried out. The characteristics of APCI together with the very good mass accuracy of TOF-MS data enable the reliable identification of relevant sterols in complex matrices. Linear calibration lines and plausible results for healthy volunteers and patients could be obtained whereas all mass signals were extracted with an extraction width of 20 ppm from the full mass data set. One advantage of high mass accuracy can be seen in the fact that from one recorded run any search for m/z can be performed.     

MMT--a pathway modeling tool for data from rapid sampling experiments

The identification of metabolic regulation is a major concern in metabolic engineering. Metabolic regulation phenomena depend on intracellular compounds such as enzymes, metabolites and cofactors. A complete understanding of metabolic regulation requires quantitative information about these compounds under in vivo conditions. This quantitative knowledge in combination with the known network of metabolic pathways allows the construction of mathematical models that describe the dynamic changes in metabolite concentrations over time. Rapid sampling combined with pulse experiments is a useful tool for the identification of metabolic regulation owing to the transient data they provide. Enzymatic tests in combination with ESI-LC-MS (Electrospray Ionization Liquid Chromatographic Tandem Mass Spectrometry) and HPLC measurements have been used to identify up to 30 metabolites and nucleotides from rapid sampling experiments. A metabolic modeling tool (MMT) that is built on a relational database was developed specifically for analysis of rapid sampling experiments. The tool allows to construct complex pathway models with information stored in the relational database. Parameter fitting and simulation algorithms for the resulting system of Ordinary Differential Equations (ODEs) are part of MMT. Additionally explicit sensitivity functions are calculated. The integration of all necessary algorithms in one tool allows fast model analysis and comparison. Complex models have been developed to describe the central metabolic pathways of Escherichia coli during a glucose pulse experiment.     

Protein identification with Liquid Chromatography and Matrix Enhanced Secondary Ion Mass Spectrometry (LC-ME-SIMS)

Secondary Ion Mass Spectrometry (SIMS) is a well established method for sensitive surface atomic and molecular analysis. Protein analysis with conventional SIMS has been attempted numerous times; however it delivers exclusively fragment peaks assigned to α-amino acids or immonium ions. In this paper we report experiments where direct sequence information could be measured thanks to a combination of HPLC separation with matrix enhanced SIMS (ME-SIMS) on tryptic digests of intact proteins. We employ peptide mass fingerprinting (PMF) and protein identification through the detection of HPLC-separated digests of Savinase (Sav.) and bovine serum albumin (BSA), followed by MASCOT search. This is the first time that the possibility of full protein identification using LC-ME-SIMS is demonstrated in a classic proteomics workflow and that a 69kDa protein is identified with SIMS. These results demonstrate both the relevance and the potential of LC-ME-SIMS in future high resolution proteomics studies.     

Possibility of Production of Amino Acids by Impact Reaction Using a Light-Gas Gun as a Simulation of Asteroid Impacts

In order to investigate impact production of carbonaceous products by asteroids on Titan and other satellites and planets, simulation experiments were carried out using a 2-stage light gas gun. A small polycarbonate or metal bullet with about 6.5 km/s was injected into a pressurized target chamber filled with 1 atm of nitrogen gas, to collide with a ice + iron target or an iron target or a ice + hexane + iron target. After the impact, black soot including fine particles was deposited on the chamber wall. The soot was carefully collected and analyzed by High Performance Liquid Chromatography (HPLC), Fourier Transform Infrared Spectroscopy (FT-IR), and Laser Desorption Time-of-Flight Mass Spectrometry (LD-ToF-MS). As a result of the HPLC analysis, about 0.04–8 pmol of glycine, and a lesser amount of alanine were found in the samples when the ice + hexane + iron target was used. In case of the ice + iron target and the iron target, less amino acids were produced. The identification of the amino acids was also supported by FTIR and LD-ToF-MS analysis.     

TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis

Background  

According to mRNA microarray, proteomics and other studies, biological abnormalities of eutopic endometrium (EM) are involved in the pathogenesis of endometriosis, but the relationship between mRNA and protein expression in EM is not clear. We tested for the first time the hypothesis that EM TRIzol extraction allows proteomic Surface Enhanced Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry (SELDI-TOF MS) analysis and that these proteomic data can be related to mRNA (microarray) data obtained from the same EM sample from women with and without endometriosis.     

TagFinder for the quantitative analysis of gas chromatography--mass spectrometry (GC-MS)-based metabolite profiling experiments

MOTIVATION: Typical GC-MS-based metabolite profiling experiments may comprise hundreds of chromatogram files, which each contain up to 1000 mass spectral tags (MSTs). MSTs are the characteristic patterns of approximately 25-250 fragment ions and respective isotopomers, which are generated after gas chromatography (GC) by electron impact ionization (EI) of the separated chemical molecules. These fragment ions are subsequently detected by time-of-flight (TOF) mass spectrometry (MS). MSTs of profiling experiments are typically reported as a list of ions, which are characterized by mass, chromatographic retention index (RI) or retention time (RT), and arbitrary abundance. The first two parameters allow the identification, the later the quantification of the represented chemical compounds. Many software tools have been reported for the pre-processing, the so-called curve resolution and deconvolution, of GC-(EI-TOF)-MS files. Pre-processing tools generate numerical data matrices, which contain all aligned MSTs and samples of an experiment. This process, however, is error prone mainly due to (i) the imprecise RI or RT alignment of MSTs and (ii) the high complexity of biological samples. This complexity causes co-elution of compounds and as a consequence non-selective, in other words impure MSTs. The selection and validation of optimal fragment ions for the specific and selective quantification of simultaneously eluting compounds is, therefore, mandatory. Currently validation is performed in most laboratories under human supervision. So far no software tool supports the non-targeted and user-independent quality assessment of the data matrices prior to statistical analysis. TagFinder may fill this gap. Strategy: TagFinder facilitates the analysis of all fragment ions, which are observed in GC-(EI-TOF)-MS profiling experiments. The non-targeted approach allows the discovery of novel and unexpected compounds. In addition, mass isotopomer resolution is maintained by TagFinder processing. This feature is essential for metabolic flux analyses and highly useful, but not required for metabolite profiling. Whenever possible, TagFinder gives precedence to chemical means of standardization, for example, the use of internal reference compounds for retention time calibration or quantitative standardization. In addition, external standardization is supported for both compound identification and calibration. The workflow of TagFinder comprises, (i) the import of fragment ion data, namely mass, time and arbitrary abundance (intensity), from a chromatography file interchange format or from peak lists provided by other chromatogram pre-processing software, (ii) the annotation of sample information and grouping of samples into classes, (iii) the RI calculation, (iv) the binning of observed fragment ions of equal mass from different chromatograms into RI windows, (v) the combination of these bins, so-called mass tags, into time groups of co-eluting fragment ions, (vi) the test of time groups for intensity correlated mass tags, (vii) the data matrix generation and (viii) the extraction of selective mass tags supported by compound identification. Thus, TagFinder supports both non-targeted fingerprinting analyses and metabolite targeted profiling. AVAILABILITY: Exemplary TagFinder workspaces and test data sets are made available upon request to the contact authors. TagFinder is made freely available for academic use from http://www-en.mpimp-golm.mpg.de/03-research/researchGroups/01-dept1/Root_Metabolism/smp/TagFinder/index.html.     

Towards high-throughput metabolomics using ultrahigh-field Fourier transform ion cyclotron resonance mass spectrometry

With unmatched mass resolution, mass accuracy, and exceptional detection sensitivity, Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) has the potential to be a powerful new technique for high-throughput metabolomic analysis. In this study, we examine the properties of an ultrahigh-field 12-Tesla (12T) FTICR-MS for the identification and absolute quantitation of human plasma metabolites, and for the untargeted metabolic fingerprinting of inbred-strain mouse serum by direct infusion (DI). Using internal mass calibration (mass error ≤1 ppm), we determined the rational elemental compositions (incorporating unlimited C, H, N and O, and a maximum of two S, three P, two Na, and one K per formula) of approximately 250 out of 570 metabolite features detected in a 3-min infusion analysis of aqueous extract of human plasma, and were able to identify more than 100 metabolites. Using isotopically-labeled internal standards, we were able to obtain excellent calibration curves for the absolute quantitation of choline with sub-pmol sensitivity, using 500 times less sample than previous LC/MS analyses. Under optimized serum dilution conditions, chemical compounds spiked into mouse serum as metabolite mimics showed a linear response over a 600-fold concentration range. DI/FTICR-MS analysis of serum from 26 mice from 2 inbred strains, with and without acute trichloroethylene (TCE) treatment, gave a relative standard deviation (RSD) of 4.5%. Finally, we extended this method to the metabolomic fingerprinting of serum samples from 49 mice from 5 inbred strains involved in an acute alcohol toxicity study, using both positive and negative electrospray ionization (ESI). Using these samples, we demonstrated the utility of this method for high-throughput metabolomics, with more than 400 metabolites profiled in only 24 h. Our experiments demonstrate that DI/FTICR-MS is well-suited for high-throughput metabolomic analysis. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

Background  

Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.     

Decision tree supported substructure prediction of metabolites from GC-MS profiles

Gas chromatography coupled to mass spectrometry (GC-MS) is one of the most widespread routine technologies applied to the large scale screening and discovery of novel metabolic biomarkers. However, currently the majority of mass spectral tags (MSTs) remains unidentified due to the lack of authenticated pure reference substances required for compound identification by GC-MS. Here, we accessed the information on reference compounds stored in the Golm Metabolome Database (GMD) to apply supervised machine learning approaches to the classification and identification of unidentified MSTs without relying on library searches. Non-annotated MSTs with mass spectral and retention index (RI) information together with data of already identified metabolites and reference substances have been archived in the GMD. Structural feature extraction was applied to sub-divide the metabolite space contained in the GMD and to define the prediction target classes. Decision tree (DT)-based prediction of the most frequent substructures based on mass spectral features and RI information is demonstrated to result in highly sensitive and specific detections of sub-structures contained in the compounds. The underlying set of DTs can be inspected by the user and are made available for batch processing via SOAP (Simple Object Access Protocol)-based web services. The GMD mass spectral library with the integrated DTs is freely accessible for non-commercial use at . All matching and structure search functionalities are available as SOAP-based web services. A XML + HTTP interface, which follows Representational State Transfer (REST) principles, facilitates read-only access to data base entities.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.     

Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

     

Identification of modified nucleosides in intact transfer ribonucleic acid by pyrolysis-electron impact-collisional activation mass spectrometry.

A novel mass spectrometric method has been developed for the detection and identification of dihydrouridine, ribothymidine, 4-thiouridine, and 7-methylguanosine in Escherichia coli tRNAs. The method utilizes (a) Pyrolysis-Electron Impact-Mass Spectrometry (PYEIMS), a procedure which releases the purine and pyrimidine bases from the intact, underivatized tRNA molecule. The mass spectrum exhibits intense peaks for the bases deriving from the common nucleosides in tRNA as well as peaks of much lower intensity at mass values expected for the bases from modified components known to be present in the tRNA; and, (b) Collisional Activation Mass Spectrometry (CAMS), a technique which permits the isolation of a single ion species from a complex mass spectrum. Subsequent fragmentation of that species yields a characteristic collisional activation spectrum. Such analyses of the ion species that were presumed to originate from H2Urd, rThd, 4SUrd, and 7MeGuo in the tRNA were used to define the structure and, thus, the identity of each component. Attributes of the PYEICAMS technique are that (a) precise structural elucidation of minor nucleosides present in tRNAs at the 1 - 4% level is obtained; (b) the high order of sensitivity allows the analysis to be done on microgram amounts of tRNA; and (c) there is no requirement for enzymatic or chemical hydrolysis of the tRNA or for subsequent chromatographic separation methods.     

HPLC fingerprinting and LC-TOF-MS analysis of the extract of Pseudostellaria heterophylla (Miq.) Pax root

High-performance liquid chromatographic (HPLC) was developed for fingerprint analysis of Pseudostellaria heterophylla (Miq.) Pax. Liquid chromatography-electrospray ionization-time-of-flight mass spectrometry (LC-TOF-MS) technique was first employed to identify the components of the fingerprint. Twelve major peaks in chromatographic fingerprint were analyzed by on-line LC-TOF-MS analysis; one cyclic peptide was unequivocally identified and five cyclic peptides were tentatively assigned based on their MS data. These cyclic peptides served as the marker peaks in the HPLC fingerprints. The chromatographic fingerprints have been analyzed by similarity index calculations and hierarchical clustering analysis (HCA). The result showed that the HPLC fingerprints could be used to determine the optimal harvest time for P. heterophylla (Miq.) Pax and to authenticate the species of the herb.     

Croton lobatus, an African medicinal plant: spectroscopic and chemical elucidation of its many constituents

The chemical constituents of the African medicinal plant Croton lobatus were elucidated and characterised using 1D and 2D-NMR analysis and the application of the technique of High Resolution Electron Ionization Mass Spectrometry (HREIMS) and High Resolution Mass Spectrometry (HRMS). The novel triglyceride lobaceride or 3-((6Z,9Z)dodeca-6,9-dienoyloxy)-2-octanoyloxypropyl (6Z,9Z)dodeca-6,9-dienoate, along with ten compounds were isolated from the stems and leaves of Croton lobatus.