Flow infusion electrospray ionisation mass spectrometry for high throughput,non-targeted metabolite fingerprinting: a review

Producing a comprehensive overview of the chemical content of biologically-derived material is a major challenge. Apart from ensuring adequate metabolome coverage and issues of instrument dynamic range, mass resolution and sensitivity, there are major technical difficulties associated with data pre-processing and signal identification when attempting large scale, high-throughput experimentation. To address these factors direct infusion or flow infusion electrospray mass spectrometry has been finding utility as a high throughput metabolite fingerprinting tool. With little sample pre-treatment, no chromatography and instrument cycle times of less than 5 min it is feasible to analyse more than 1,000 samples per week. Data pre-processing is limited to aligning extracted mass spectra and mass-intensity matrices are generally ready in a working day for a month’s worth of data mining and hypothesis generation. ESI-MS fingerprinting has remained rather qualitative by nature and as such ion suppression does not generally compromise data information content as originally suggested when the methodology was first introduced. This review will describe how the quality of data has improved through use of nano-flow infusion and mass-windowing approaches, particularly when using high resolution instruments. The increasingly wider availability of robust high accurate mass instruments actually promotes ESI-MS from a merely fingerprinting tool to the ranks of metabolite profiling and combined with MS/MS capabilities of hybrid instruments improved structural information is available concurrently. We summarise current applications in a wide range of fields where ESI-MS fingerprinting has proved to be an excellent tool for “first pass” metabolome analysis of complex biological samples. The final part of the review describes a typical workflow with reference to recently published data to emphasise key aspects of overall experimental design.

     

Automated work-flow for processing high-resolution direct infusion electrospray ionization mass spectral fingerprints

The use of mass spectrometry (MS) is pivotal in analyses of the metabolome and presents a major challenge for subsequent data processing. While the last few years have given new high performance instruments, there has not been a comparable development in data processing. In this paper we discuss an automated data processing pipeline to compare large numbers of fingerprint spectra from direct infusion experiments analyzed by high resolution MS. We describe some of the intriguing problems that have to be addressed, starting with the conversion and pre-processing of the raw data to the final data analysis. Illustrated on the direct infusion analysis (ESI-TOF-MS) of complex mixtures the method exploits the full quality of the high-resolution present in the mass spectra. Although the method is illustrated as a new library search method for high resolution MS, we demonstrate that the output of the preprocessing is applicable to cluster-, discriminant analysis, and related multivariate methods applied directly to mass spectra from direct infusion analysis of crude extracts. This is done to find the relationship between several terverticillate Penicillium species and identify the ions responsible for the segregation.     

Metabolite fingerprinting and profiling in plants using NMR

Although less sensitive than mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary technique for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. In one approach, metabolite fingerprinting, multivariate analysis of unassigned 1H NMR spectra is used to compare the overall metabolic composition of wild-type, mutant, and transgenic plant material, and to assess the impact of stress conditions on the plant metabolome. Metabolite fingerprinting by NMR is a fast, convenient, and effective tool for discriminating between groups of related samples and it identifies the most important regions of the spectrum for further analysis. In a second approach, metabolite profiling, the 1H NMR spectra of tissue extracts are assigned, a process that typically identifies 20-40 metabolites in an unfractionated extract. These profiles may also be used to compare groups of samples, and significant differences in metabolite concentrations provide the basis for hypotheses on the underlying causes for the observed segregation of the groups. Both approaches generate a metabolic phenotype for a plant, based on a system-wide but incomplete analysis of the plant metabolome. However, a review of the literature suggests that the emphasis so far has been on the accumulation of analytical data and sample classification, and that the potential of 1H NMR spectroscopy as a tool for probing the operation of metabolic networks, or as a functional genomics tool for identifying gene function, is largely untapped.     

Specialized natural product analysis and chemophenetics of some Turkish endemic Centaurea L. (Asteraceae) taxa by electrospray ionization mass spectrometry fingerprinting and liquid chromatography-tandem mass spectrometry

Specialized natural product analysis of six Turkish endemic and two narrowly distributed Centaurea L. taxa was performed via electrospray ionization mass spectrometry (ESI-MS) fingerprinting and liquid chromatography-tandem mass spectrometry (LC-MS/MS), which is an effective methodology that is widely used for fast screening of complex natural mixtures such as food extracts, but not has not been used as commonly for plant chemophenetics. This method is preferable when it is aimed to compare a large number of plant extracts for chemophenetic purposes and when it is difficult to provide equally good chromatographic separation in all of the extracts. ESI-MS shows the major compounds in fingerprinting extracts. LC-MS/MS provides identification according to fragmentation with the advantage of MS/MS, and validation can be performed in selected reaction monitoring (SRM) mode with simultaneous precursor and product ion scans. Herein, sixteen flavones, four flavonols, four flavanones, two lignans, three sesquiterpene lactones, and four phenolic acids, a total of thirty three substances, were identified tentatively or unambiguously from the extracts. It was concluded that ESI-MS fingerprinting is a suitable method for plant chemophenetics when coupled and validated with LC-MS/MS. Moreover, it was concluded that sesquiterpene lactones, lignans, and flavonoids are suitable for taxonomic purposes in Centaurea owing to species-specific metabolite profiles.     

Metabolic profiling by ion mobility mass spectrometry (IMMS)

Ion Mobility Mass Spectrometry (IMMS) was evaluated as an analytical method for metabolic profiling. The specific instrument used in these studies was a direct infusion (DI)-electrospray ionization (ESI)—ambient pressure ion mobility spectrometer (APIMS) coupled to a time-of-flight mass spectrometer (TOFMS). The addition of an ion mobility spectrometer to a mass spectrometer had several advantages over direct infusion electrospray mass spectrometry alone. This tandem instrument (ESI-IMMS) added a rapid separation step with high-resolution prior to mass spectrometric analysis of metabolite mixtures without extending sample preparation time or reducing the high through put potential of direct mass spectrometry. Further, IMMS also reduced the baseline noise common with ESI-MS analyses of complex samples and enabled rapid separation of isobaric metabolites. IMMS was used to analyze the metabolome of Escherichia coli (E. coli), containing a collection of extremely diverse chemical compounds including hydrophobic lipids, inorganic ions, volatile alcohols and ketones, amino and non-amino organic acids, and hydrophilic carbohydrates. IMMS data were collected as two-dimensional spectra showing both mobility and mass of each ion detected. Using direct infusion ESI-IMMS of a non-derivatized methanol extract of an E. coli culture, more than 500 features were detected, of which over 200 intracellular metabolites were tentatively assigned as E. coli metabolites. This analytical method also allowed simultaneous separation of isomeric metabolic features.     

The yeast metabolome addressed by electrospray ionization mass spectrometry: Initiation of a mass spectral library and its applications for metabolic footprinting by direct infusion mass spectrometry

Mass spectrometry (MS) has been a major driver for metabolomics, and gas chromatography (GC)-MS has been one of the primary techniques used for microbial metabolomics. The use of liquid chromatography (LC)-MS has however been limited, but electrospray ionization (ESI) is very well suited for ionization of microbial metabolites without any previous derivatization needed. To address the capabilities of ESI-MS in detecting the metabolome of Saccharomyces cerevisiae, the in silico metabolome of this organism was used as a template to present a theoretical metabolome. This showed that in combination with the specificity of MS up to 84% of the metabolites can be identified in a high mass accuracy ESI-spectrum. A total of 66 metabolites were systematically analyzed by positive and negative ESI-MS/MS with the aim of initiating a spectral library for ESI of microbial metabolites. This systematic analysis gave insight into the ionization and fragmentation characteristics of the different metabolites. With this insight, a small study of metabolic footprinting with ESI-MS demonstrated that biological information can be extracted from footprinting spectra. Statistical analysis of the footprinting data revealed discriminating ions, which could be assigned using the in silico metabolome. By this approach metabolic footprinting can advance from a classification method that is used to derive biological information based on guilt-by-association, to a tool for extraction of metabolic differences, which can guide new targeted biological experiments. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Dynamic mass spectrometry probe for electrospray ionization mass spectrometry monitoring of bioreactors for therapeutic cell manufacturing

Large-scale manufacturing of therapeutic cells requires bioreactor technologies with online feedback control enabled by monitoring of secreted biomolecular critical quality attributes (CQAs). Electrospray ionization mass spectrometry (ESI-MS) is a highly sensitive label-free method to detect and identify biomolecules, but requires extensive sample preparation before analysis, making online application of ESI-MS challenging. We present a microfabricated, monolithically integrated device capable of continuous sample collection, treatment, and direct infusion for ESI-MS detection of biomolecules in high-salt solutions. The dynamic mass spectrometry probe (DMSP) uses a microfluidic mass exchanger to rapidly condition samples for online MS analysis by removing interfering salts, while concurrently introducing MS signal enhancers to the sample for sensitive biomolecular detection. Exploiting this active conditioning capability increases MS signal intensity and signal-to-noise ratio. As a result, sensitivity for low-concentration biomolecules is significantly improved, and multiple proteins can be detected from chemically complex samples. Thus, the DMSP has significant potential to serve as an enabling portion of a novel analytical tool for discovery and monitoring of CQAs relevant to therapeutic cell manufacturing.     

Rapid protein identification using direct infusion nanoelectrospray ionization mass spectrometry

Current protein identification techniques are largely based on MALDI-TOF mass fingerprinting and LC-ESI MS/MS sequence tag analysis. Here we describe an improved method for rapid protein identification that uses direct infusion nanoelectrospray quadrupole time-of-flight (nanoESI QTOF) MS. Protein digests were analyzed without LC separation using nanoESI on a QSTAR XL MS/MS system in information dependent data acquisition mode. The protein identification conditions and parameters were extensively evaluated with in-solution and in-gel digested protein samples. Rapid identification of proteins was achieved and compared directly to the results obtained on the same samples using nanoflow HPLC-MS/MS on the QSTAR system. The increased throughput, reproducibility, the high data quality, and the ease of use make the direct infusion system an efficient and affordable technique for protein identification analysis.     

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.     

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

Background  

Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI).     

Comparative proteomics using 2-D gel electrophoresis and mass spectrometry as tools to dissect stimulons and regulons in bacteria with sequenced or partially sequenced genomes

We propose two-dimensional gel electrophoresis (2-DE) and mass spectrometry to define the protein components of regulons and stimulons in bacteria, including those organisms where genome sequencing is still in progress. The basic 2-DE protocol allows high resolution and reproducibility and enables the direct comparison of hundreds or even thousands of proteins simultaneously. To identify proteins that comprise stimulons and regulons, peptide mass fingerprint (PMF) with matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF-MS) analysis is the first option and, if results from this tool are insufficient, complementary data obtained with electrospray ionization tandem-MS (ESI-MS/MS) may permit successful protein identification. ESI-MS/MS and MALDI-TOF-MS provide complementary data sets, and so a more comprehensive coverage of a proteome can be obtained using both techniques with the same sample, especially when few sequenced proteins of a particular organism exist or genome sequencing is still in progress.     

Matrix-assisted laser desorption/ionization-tandem mass spectrometry with high resolution and sensitivity for identification and characterization of proteins

Although peptide mass fingerprinting is currently the method of choice to identify proteins, the number of proteins available in databases is increasing constantly, and hence, the advantage of having sequence data on a selected peptide, in order to increase the effectiveness of database searching, is more crucial. Until recently, the ability to identify proteins based on the peptide sequence was essentially limited to the use of electrospray ionization tandem mass spectrometry (MS) methods. The recent development of new instruments with matrix-assisted laser desorption/ionization (MALDI) sources and true tandem mass spectrometry (MS/MS) capabilities creates the capacity to obtain high quality tandem mass spectra of peptides. In this work, using the new high resolution tandem time of flight MALDI-(TOF/TOF) mass spectrometer from Applied Biosystems, examples of successful identification and characterization of bovine heart proteins (SWISS-PROT entries: P02192, Q9XSC6, P13620) separated by two-dimensional electrophoresis and blotted onto polyvinylidene difluoride membrane are described. Tryptic protein digests were analyzed by MALDI-TOF to identify peptide masses afterward used for MS/MS. Subsequent high energy MALDI-TOF/TOF collision-induced dissociation spectra were recorded on selected ions. All data, both MS and MS/MS, were recorded on the same instrument. Tandem mass spectra were submitted to database searching using MS-Tag or were manually de novo sequenced. An interesting modification of a tryptophan residue, a "double oxidation", came to light during these analyses.     

Detecting a difference – assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants

There is current debate on whether genetically-manipulated plants might contain unexpected, potentially undesirable, changes in overall metabolite composition relative to that of the progenitor genotype. However, appropriate analytical technology and acceptable metrics of compositional similarity require development, particularly to allow data integration from different laboratories and different harvests. For an initial comprehensive overview of compositional similarity, we explored the use of a rapid and relatively non-selective fingerprinting technique based on flow injection electrospray ionisation mass spectrometry (FIE-MS). Six conventionally-bred potato cultivars and six experimental bioengineered potato genotypes were produced in four field blocks during two growing seasons and analysed on two different analytical instruments (LCT, Micromass in 2001 and LTQ, Thermo Finnigan in 2003). Field effects and overall process variability was found to be negligible when compared to inherited genotype variance. The data derived separately for experiments using tubers from individual harvest years were compared to assess the generalisability of models for the comparison of GM and non-GM potato tubers under investigation. This procedure proved appropriate for not only rapid assessment of similarities between plant genotypes but also to predict the identity of metabolite signals that could explain differences between genotype classes irrespective of the instrument used for analysis. Importantly, despite differences in ionisation and data acquisition properties of the two instruments the generalisation of models could be confirmed after correlation analysis of explanatory variables correctly identified the molecular origin of differences between genotypes. We conclude that FIE-MS metabolomics fingerprinting technology coupled to machine learning data analysis has great potential as a robust tool for first-pass metabolic phenotyping and, therefore, initial assessments of compositional similarities prior to use of more targeted hyphenated gas or liquid chromatography-mass spectrometry techniques.     

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.     

MetaboSearch: tool for mass-based metabolite identification using multiple databases

Searching metabolites against databases according to their masses is often the first step in metabolite identification for a mass spectrometry-based untargeted metabolomics study. Major metabolite databases include Human Metabolome DataBase (HMDB), Madison Metabolomics Consortium Database (MMCD), Metlin, and LIPID MAPS. Since each one of these databases covers only a fraction of the metabolome, integration of the search results from these databases is expected to yield a more comprehensive coverage. However, the manual combination of multiple search results is generally difficult when identification of hundreds of metabolites is desired. We have implemented a web-based software tool that enables simultaneous mass-based search against the four major databases, and the integration of the results. In addition, more complete chemical identifier information for the metabolites is retrieved by cross-referencing multiple databases. The search results are merged based on IUPAC International Chemical Identifier (InChI) keys. Besides a simple list of m/z values, the software can accept the ion annotation information as input for enhanced metabolite identification. The performance of the software is demonstrated on mass spectrometry data acquired in both positive and negative ionization modes. Compared with search results from individual databases, MetaboSearch provides better coverage of the metabolome and more complete chemical identifier information. Availability: The software tool is available at http://omics.georgetown.edu/MetaboSearch.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.     

An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection

In any metabolomics experiment, robustness and reproducibility of data collection is of vital importance. These become more important in collaborative studies where data is to be collected on multiple instruments. With minimisation of variance in sample preparation and instrument performance it is possible to elucidate even subtle differences in metabolite fingerprints due to genotype or biological treatment. In this paper we report on an inter laboratory comparison of plant derived samples by [¹H]-NMR spectroscopy across five different sites and within those sites utilising instruments with different probes and magnetic field strengths of 9.4 T (400 MHz), 11.7 T (500 MHz) and 14.1 T (600 MHz). Whilst the focus of the study is on consistent data collection across laboratories, aspects of sample stability and the requirement for sample rotation within the NMR magnet are also discussed. Comparability of the datasets from participating laboratories was exceptionally good and the data were amenable to comparative analysis by multivariate statistics. Field strength differences can be adjusted for in the data pre-processing and multivariate analysis demonstrating that [¹H]-NMR fingerprinting is the ideal technique for large scale plant metabolomics data collection requiring the participation of multiple laboratories.     

A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography–mass spectrometry (GC–MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC–MS. In total, 235 GC–MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples (1) had the same or lower coefficients of variance among reproducibly detected metabolites, (2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and (3) increased the number of metabolite identifications. Overall, we observed no negative consequences of urease pre-treatment. In contrast, urease pre-treatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pre-treatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.     

Novel linear quadrupole ion trap/FT mass spectrometer: performance characterization and use in the comparative analysis of histone H3 post-translational modifications

We describe the design and performance of a prototype high performance hybrid mass spectrometer. This instrument consists of a linear quadrupole ion trap (QLT) coupled to a Fourier transform ion cyclotron resonance mass analyzer (FTMS). This configuration provides rapid and automated MS and MS/MS analyses, similar to the "data dependent scanning" found on standard 3-D Paul traps, but with substantially improved internal scan dynamic range, mass measurement accuracy, mass resolution, and detection limits. Sequence analysis of peptides at the zeptomole level is described. The recently released, commercial version of this instrument operates in the LC/MS mode (1 s/scan) with a mass resolution of 100 000 and is equipped with automatic gain control to provide mass measurement accuracy of 1-2 ppm without internal standard. Methodology is described that uses this instrument to compare the post-translational modifications present on histone H3 isolated from asynchronously growing cells and cells arrested in mitosis.