Annotating unknown components from GC/EI-MS-based metabolite profiling experiments using GC/APCI(+)-QTOFMS

GC/EI-MS-based metabolite profiling of derivatized polar fractions of crude plant extracts typically reveals several hundred components. Thereof, only up to one half can be identified using mass spectral and retention index libraries, the rest remains unknown. In the present work, the utility of GC/APCI(+)-QTOFMS for the annotation of unknown components was explored. Hence, EI and APCI(+) mass spectra of ~100 known components were extracted from GC/EI-QMS and GC/APCI(+)-QTOFMS profiles obtained from a methoximated and trimethylsilylated root extract of Arabidopsis thaliana. Based on this reference set, adduct and fragment ion formation under APCI(+) conditions was examined and the calculation of elemental compositions evaluated. During these studies, most of the components formed dominating protonated molecular ions. Despite the high mass accuracy (|Δm| ≤ 3 mDa) and isotopic pattern accuracy (mSigma ≤ 30) the determination of a component’s unique native elemental composition requires additional information, namely the number of trimethylsilyl and methoxime moieties as well as the analysis of corresponding collision-induced dissociation (CID) mass spectra. After all, the reference set was used to develop a strategy for the pairwise assignment of EI and APCI(+) mass spectra. Proceeding from these findings, the annotation of unidentified components detected by GC/EI-QMS using GC/APCI(+)-QTOFMS and corresponding deuterated derivatization reagents was attempted. For a total of 25 unknown components, pairs of EI and APCI(+) mass spectra were compiled and elemental compositions determined. Integrative interpretation of EI and CID mass spectra resulted in 14 structural hypotheses, of which seven were confirmed using authenticated standards.     

Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics

Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. In spectral library searching, a spectral library is first meticulously compiled from a large collection of previously observed peptide MS/MS spectra that are conclusively assigned to their corresponding amino acid sequence. An unknown spectrum is then identified by comparing it to all the candidates in the spectral library for the most similar match. This review discusses the basic principles of spectral library building and searching, describes its advantages and limitations, and provides a primer for researchers interested in adopting this new approach in their data analysis. It will also discuss the future outlook on the evolution and utility of spectral libraries in the field of proteomics.     

Optimization of mass spectral features in MALDI-TOF MS profiling of Acinetobacter species

The influence of the matrix solution, sample form and deposition technique on the quality MALDI-TOF mass spectra was examined and assessed with the aim to improve MALDI-TOF MS performance for the identification of microorganisms and to enable automatic spectra acquisition. It was observed that the use of matrix compounds ferulic and sinapinic acid may result in improved mass spectral features, in terms of signal resolution and S/N ratio, as compared to alpha-cyano-4-hydroxycinnamic acid, which was, on the other hand, found to be the only matrix compound that enabled fully automatic mass spectra acquisition. The robustness of the whole sample preparation procedure was then assessed on a set of 25 strains of four Acinetobacter species. Results showed reproducible detection of subtle mass spectral differences between strains belonging to the same species, although they do not confirm the possibility of reliable strain typing.     

Comparison of a preliminary procedure for the general unknown screening of drugs and toxic compounds using a quadrupole-linear ion-trap mass spectrometer with a liquid chromatography-mass spectrometry reference technique

Liquid chromatography-tandem mass spectrometry (LC-MS-MS) might be a complement to GC-MS and HPLC-diode array detection for the general unknown screening (GUS) of drugs and toxic compounds, particularly when using information- or data-dependent acquisition (IDA or DDA), an auto-adaptive MS-MS product-ion scan mode where, at each unit time, the m/z ratios above a given intensity threshold are selected for fragmentation. A new quadrupole-linear ion-trap mass spectrometer (LC-QqQlinear ion-trap) was evaluated for GUS using IDA. For the first detection step (so-called "survey scan") the single quadrupole "enhanced" MS mode (EMS), where ions are accumulated then filtered in the Q3-linear ion-trap, was used. The so-called "enhanced" parent ion scan mode (EPI) used at two alternated fragmentation energies gave the best signal intensity and the best mass spectral information when adding mass spectra obtained in low and high fragmentation conditions, respectively, both in the positive (+20 and +50 eV) and negative (-15 and -40 eV) modes. Solid-phase extracts of serum spiked with eight test compounds (chosen for their retention times distributed along the 30-min long chromatogram and for ionising in both the positive and negative modes) were analysed in parallel with this LC-MS-MS technique and with a reference LC-MS method run on a single-quadrupole instrument where low and high in-source fragmentation conditions in the positive and the negative ion modes are alternated. A C(18), 5 microm (150 x 1 mm I.D.) column and a gradient elution of acetonitrile in pH 3, 2 mM ammonium formate, were used for both. Higher signal-to-noise ratios were obtained with the LC-QqQlinear ion-trap instrument than with the reference technique, resulting in mass spectra devoid of contaminant ions and at least as informative as the reconstructed single-MS spectra. After optimisation of the IDA intensity threshold for the detection of tiny chromatographic peaks in noise, five out of the eight compounds (milrinone, lorazepam, fluometuron, piretanide and warfarin) could be unambiguously identified at the concentration of 0.1 mg/l in serum, in the positive or negative modes, or in both, versus only two by LC-MS. All of them could be identified at 1 mg/l by both techniques. These preliminary results show that the sensitivity and mass structural information brought by this new LC-QqQlinear ion-trap instrument may help design an efficient toxicological GUS procedure.     

In silico fragmentation for computer assisted identification of metabolite mass spectra

Background  

Mass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.     

A new scoring function for top-down spectral deconvolution

Background

Top-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation.

Results

In this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution.

Conclusions

L-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1140) contains supplementary material, which is available to authorized users.     

The spectral networks paradigm in high throughput mass spectrometry

High-throughput proteomics is made possible by a combination of modern mass spectrometry instruments capable of generating many millions of tandem mass (MS(2)) spectra on a daily basis and the increasingly sophisticated associated software for their automated identification. Despite the growing accumulation of collections of identified spectra and the regular generation of MS(2) data from related peptides, the mainstream approach for peptide identification is still the nearly two decades old approach of matching one MS(2) spectrum at a time against a database of protein sequences. Moreover, database search tools overwhelmingly continue to require that users guess in advance a small set of 4-6 post-translational modifications that may be present in their data in order to avoid incurring substantial false positive and negative rates. The spectral networks paradigm for analysis of MS(2) spectra differs from the mainstream database search paradigm in three fundamental ways. First, spectral networks are based on matching spectra against other spectra instead of against protein sequences. Second, spectral networks find spectra from related peptides even before considering their possible identifications. Third, spectral networks determine consensus identifications from sets of spectra from related peptides instead of separately attempting to identify one spectrum at a time. Even though spectral networks algorithms are still in their infancy, they have already delivered the longest and most accurate de novo sequences to date, revealed a new route for the discovery of unexpected post-translational modifications and highly-modified peptides, enabled automated sequencing of cyclic non-ribosomal peptides with unknown amino acids and are now defining a novel approach for mapping the entire molecular output of biological systems that is suitable for analysis with tandem mass spectrometry. Here we review the current state of spectral networks algorithms and discuss possible future directions for automated interpretation of spectra from any class of molecules.     

Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra

Abstract An artificial neural network was trained to distinguish between three putatively novel species of Streptomyces using normalised, scaled pyrolysis mass spectra from three representative strains of each of the taxa, each sampled in triplicate. Once trained, the artificial neural network was challenged with spectral data from the original organisms, the 'training set', from additional members of the putative novel taxa and from over a hundred strains representing six other actinomycete genera. All of the streptomycetes were correctly identified but many of the other actinomycetes were mis-identified. A modified network topology was developed to recognise the mass spectral patterns of the non-streptomycete strains. The resultant neural network correctly identified the streptomycetes, whereas all of the remaining actinomycetes were recognised as unknown organisms. The improved artificial neural network provides a rapid, reliable and cost-effective method of identifying members of the three target streptomycete taxa.     

Characterization and mass spectral analysis of some grayanotoxin derivatives

The mass spectra of grayanotoxin III and some of its mono-, di, and tri-ester derivatives are discussed. Some of the physiological responses of this compound class are also presented. The extraction of this material from a natural source is discussed as well as the schemes for ester derivatization and spectral characterization of all compounds studied. High resolution mass spectral analysis and comparison of the mass spectra of similar compounds were used to define possible fragmentation mechanisms.     

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.     

Infrared spectroscopy of exfoliated cervical cell specimens. Proceed with caution

OBJECTIVE: To determine whether diagnostic information may be recovered from the infrared spectra of exfoliated cell specimens by using a novel spectral feature extraction method, in conjunction with linear and quadratic discriminant analysis, for spectral classification. STUDY DESIGN: Over 800 infrared spectra were included in the study, with corresponding clinical diagnoses based upon cytology and, when available, histology reports. Three sets of classification trials were carried out with the aim of distinguishing the spectra corresponding to normal specimens from CIN 1, 2 and 3. For each of these three cases, the procedure was to: (1) develop a set of provisional classification models using only a "training" subset of the spectra, and (2) test each provisional model by its ability to correctly predict the diagnoses on the basis of the remaining spectra. RESULTS: For optimal classification trials, training set classification accuracies were 68% for normal/CIN 1, 73% for normal/CIN 2 and 81% for normal/CIN 3; for the corresponding test sets the classification accuracies were 60%, 60% and 67%, respectively. CONCLUSION: The infrared spectra of exfoliated cervical cells carry information regarding the presence or absence of dysplasia, and that information is recoverable--albeit imperfectly at this stage--from the spectra of "real life" cell preparations.     

MALDI QqTOF MS combined with off-line HPLC for characterization of protein primary structure and post-translational modifications.

The addition of off-line high-performance liquid chromatography to matrix-assisted laser desorption/ionization mass spectrometry greatly reduces congestion in the mass spectra, and also provides complete decoupling of the separation process from mass detection and measurement. This removes the time constraints inherent in on-line coupling, and so enables the detailed mass-spectrometric study of samples at later times. We describe here our use of this method to successfully characterize two "unknown" protein mixtures that were set as problems by the ABRF Proteomics Research Group (PRG) in the years 2003 and 2004.     

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top‐Down Mass Spectrometry

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top‐down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top‐down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum‐based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top‐down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome‐level proteoform analyses.     

Predicting leaf traits of herbaceous species from their spectral characteristics

Trait predictions from leaf spectral properties are mainly applied to tree species, while herbaceous systems received little attention in this topic. Whether similar trait–spectrum relations can be derived for herbaceous plants that differ strongly in growing strategy and environmental constraints is therefore unknown. We used partial least squares regression to relate key traits to leaf spectra (reflectance, transmittance, and absorbance) for 35 herbaceous species, sampled from a wide range of environmental conditions. Specific Leaf Area and nutrient‐related traits (N and P content) were poorly predicted from any spectrum, although N prediction improved when expressed on a per area basis (mg/m² leaf surface) instead of mass basis (mg/g dry matter). Leaf dry matter content was moderately to good correlated with spectra. We explain our results by the range of environmental constraints encountered by herbaceous species; both N and P limitations as well as a range of light and water availabilities occurred. This weakened the relation between the measured response traits and the leaf constituents that are truly responsible for leaf spectral behavior. Indeed, N predictions improve considering solely upper or under canopy species. Therefore, trait predictions in herbaceous systems should focus on traits relating to dry matter content and the true, underlying drivers of spectral properties.     

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.     

Multi-spectra peptide sequencing and its applications to multistage mass spectrometry

Despite a recent surge of interest in database-independent peptide identifications, accurate de novo peptide sequencing remains an elusive goal. While the recently introduced spectral network approach resulted in accurate peptide sequencing in low-complexity samples, its success depends on the chance of presence of spectra from overlapping peptides. On the other hand, while multistage mass spectrometry (collecting multiple MS 3 spectra from each MS 2 spectrum) can be applied to all spectra in a complex sample, there are currently no software tools for de novo peptide sequencing by multistage mass spectrometry. We describe a rigorous probabilistic framework for analyzing spectra of overlapping peptides and show how to apply it for multistage mass spectrometry. Our software results in both accurate de novo peptide sequencing from multistage mass spectra (despite the inferior quality of MS 3 spectra) and improved interpretation of spectral networks. We further study the problem of de novo peptide sequencing with accurate parent mass (but inaccurate fragment masses), the protocol that may soon become the dominant mode of spectral acquisition. Most existing peptide sequencing algorithms (based on the spectrum graph approach) do not track the accurate parent mass and are thus not equipped for solving this problem. We describe a de novo peptide sequencing algorithm aimed at this experimental protocol and show that it improves the sequencing accuracy on both tandem and multistage mass spectrometry.     

Determination of serotypes of Shiga toxin-producing Escherichia coli isolates by intact cell matrix-assisted laser desorption ionization-time of flight mass spectrometry

Shiga toxin-producing Escherichia coli (STEC) isolates representing the serotypes O165:H25, O26:H11/H32, and O156:H25 were analyzed by matrix-assisted laser desorption/ionization (MALDI) mass spectra of whole cells, a procedure also known as intact cell mass spectrometry (ICMS or IC-MALDI MS) or MALDI-typing. We demonstrate that within the given species the three serotypes can be well discriminated by ICMS. Conditions for the construction of serotype-specific prototypic mass spectra were systematically optimized by filtering out masses that do not contribute to the discrimination of the serotypes. Binary distances between prototypic spectra and sample spectra were used to determine serotypes of unknown samples. With parameters optimized, only 0.7% of the assignments were incorrect compared to 31% when distances were calculated from alignments of unfiltered mass spectra. Within the different serotypes, clusters of genetically related E. coli most probably originating from single clones could be distinguished by restriction fragment length polymorphism analysis. Since ICMS did not reproduce these clusters, we conclude that the power of ICMS is just sufficient to discriminate E. coli serotypes under certain conditions but fails for the differentiation of E. coli below this level.     

De novo analysis of peptide tandem mass spectra by spectral graph partitioning.

We report on a new de novo peptide sequencing algorithm that uses spectral graph partitioning. In this approach, relationships between m/z peaks are represented by attractive and repulsive springs, and the vibrational modes of the spring system are used to infer information about the peaks (such as "likely b-ion" or "likely y-ion"). We demonstrate the effectiveness of this approach by comparison with other de novo sequencers on test sets of ion-trap and QTOF spectra, including spectra of mixtures of peptides. On all datasets, we outperform the other sequencers. Along with spectral graph theory techniques, the new de novo sequencer EigenMS incorporates another improvement of independent interest: robust statistical methods for recalibration of time-of-flight mass measurements. Robust recalibration greatly outperforms simple least-squares recalibration, achieving about three times the accuracy for one QTOF dataset.     

De novo peptide sequencing via tandem mass spectrometry.

Peptide sequencing via tandem mass spectrometry (MS/MS) is one of the most powerful tools in proteomics for identifying proteins. Because complete genome sequences are accumulating rapidly, the recent trend in interpretation of MS/MS spectra has been database search. However, de novo MS/MS spectral interpretation remains an open problem typically involving manual interpretation by expert mass spectrometrists. We have developed a new algorithm, SHERENGA, for de novo interpretation that automatically learns fragment ion types and intensity thresholds from a collection of test spectra generated from any type of mass spectrometer. The test data are used to construct optimal path scoring in the graph representations of MS/MS spectra. A ranked list of high scoring paths corresponds to potential peptide sequences. SHERENGA is most useful for interpreting sequences of peptides resulting from unknown proteins and for validating the results of database search algorithms in fully automated, high-throughput peptide sequencing.