Analysis of acidic peptides with a matrix-assisted laser desorption/ionization mass spectrometry using positive and negative ion modes with additive monoammonium phosphate

Acidic PTMs such as phosphorylation and sulfonation of proteins are known to play important roles in many cellular processes including signal transductions and protein-protein interactions. In MS, the acidic modified peptides, that have negative charge, are observable in negative ion mode rather than in positive ion mode. Moreover, addition of ammonium salt into MALDI matrix solution improves the relative intensity of ionization of the phosphorylated peptide to unmodified one. We demonstrate that a combination of the negative ion mode and addition of ammonium salt is more effective in the ionization of the acidic modified peptides. We applied this method to 2-DE separated proteins of Caenorhabditis elegans. As a result, 42 spots were identified as modified proteins, of which 34 proteins were nonoverlapping unique proteins. Furthermore, our study revealed that pI shifts of the DIM-1 and MLC-1 proteins in the 2-DE gel were attributed to the presence of the acidic modifications. The negative ion mode together with the addition of ammonium salt provides us a useful method to detect the phosphorylation and/or sulfonation of protein in a simple manner.     

Signal detection in high-resolution mass spectrometry data

Mass spectrometry data from high-resolution time-of-flight instruments often contain a vast number of noninformative background-ion peaks whose signal is similar to that of peptide peaks. Consequently, seeking peptide signal in these spectra based on a signal-to-noise ratio will remove signal peaks as well as noise. This work characterizes the background as a precursor to seeking peptide-related features. Robust-regression methods are used to estimate distributions for null (background) peak intensities and locations. Defining signal peaks as outliers with respect to these distributions leads to more precision in detecting the isotopic envelope of peaks from low-abundance peptides in high-resolution spectra.     

Targeted metabolomic analysis of Escherichia coli by desorption electrospray ionization and extractive electrospray ionization mass spectrometry

Desorption electrospray ionization (DESI) was utilized to monitor the presence of targeted central carbon metabolites within bacterial cell extracts and the quench supernatant of Escherichia coli. The targeted metabolites were identified through tandem mass spectrometry (MS/MS) product ion scans using collision-induced dissociation in the negative ion mode. Picogram detection limits were achieved for a majority of the metabolites during MS/MS analysis of standard metabolite solutions. In a [U-(13)C]glucose pulse experiment, where uniformly labeled glucose was fed to E. coli, the corresponding fragment ions from labeled metabolites in extracts were generally observed. There was evidence of matrix effects including moderate suppression by other metabolites within the spectra of the labeled and unlabeled extracts. To improve the specificity and sensitivity of detection, optimized in situ ambient chemical reactions using DESI and extractive electrospray ionization (EESI) were carried out for targeted compounds. This study provides the first indication of the potential to perform in situ targeted metabolomics of a bacterial sample via ambient ionization mass spectrometry.     

Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies

Background

Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.

Aim of review

This tutorial review will guide the reader through the use of system suitability and QC samples, why these samples should be applied and how the quality of data can be reported.

Key scientific concepts of review

System suitability samples are applied to assess the operation and lack of contamination of the analytical platform prior to sample analysis. Isotopically-labelled internal standards are applied to assess system stability for each sample analysed. Pooled QC samples are applied to condition the analytical platform, perform intra-study reproducibility measurements (QC) and to correct mathematically for systematic errors. Standard reference materials and long-term reference QC samples are applied for inter-study and inter-laboratory assessment of data.

     

Ambient mass spectrometry employing a DART ion source for metabolomic fingerprinting/profiling: a powerful tool for beer origin recognition

A metabolomic fingerprinting/profiling generated by ambient mass spectrometry (MS) employing a direct analysis in real time (DART) ion source coupled to high-resolution time-of-flight mass spectrometry (TOFMS) was employed as a tool for beer origin recognition. In a first step, the DART–TOFMS instrumental conditions were optimized to obtain the broadest possible representation of ionizable compounds occurring in beer samples (direct measurement, no sample preparation). In the next step, metabolomic profiles (mass spectra) of a large set of different beer brands (Trappist and non-Trappist specialty beers) were acquired. In the final phase, the experimental data were analyzed using partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and artificial neural networks with multilayer perceptrons (ANN-MLP) with the aim of distinguishing (i) the beers labeled as Rochefort 8; (ii) a group consisting of Rochefort 6, 8, 10 beers; and (iii) Trappist beers. The best prediction ability was obtained for the model that distinguished the group of Rochefort 6, 8, 10 beers from the rest of beers. In this case, all chemometric tools employed provided ≥95% correct classification. The current study showed that DART–TOFMS metabolomic fingerprinting/profiling is a powerful analytical strategy enabling quality monitoring/authenticity assessment to be conducted in real time.     

Implementation and application of a versatile clustering tool for tandem mass spectrometry data

High-throughput proteomics experiments typically generate large amounts of peptide fragmentation mass spectra during a single experiment. There is often a substantial amount of redundant fragmentation of the same precursors among these spectra, which is usually considered a nuisance. We here discuss the potential of clustering and merging redundant spectra to turn this redundancy into a useful property of the dataset. To this end, we have created the first general-purpose, freely available open-source software application for clustering and merging MS/MS spectra. The application also introduces a novel approach to calculating the similarity of fragmentation mass spectra that takes into account the increased precision of modern mass spectrometers, and we suggest a simple but effective improvement to single-linkage clustering. The application and the novel algorithms are applied to several real-life proteomic datasets and the results are discussed. An analysis of the influence of the different algorithms available and their parameters is given, as well as a number of important applications of the overall approach.     

MassChroQ: a versatile tool for mass spectrometry quantification

Recently, many software tools have been developed to perform quantification in LC-MS analyses. However, most of them are specific to either a quantification strategy (e.g. label-free or isotopic labelling) or a mass-spectrometry system (e.g. high or low resolution). In this context, we have developed MassChroQ (Mass Chromatogram Quantification), a versatile software that performs LC-MS data alignment and peptide quantification by peak area integration on extracted ion chromatograms. MassChroQ is suitable for quantification with or without labelling and is not limited to high-resolution systems. Peptides of interest (for example all the identified peptides) can be determined automatically, or manually by providing targeted m/z and retention time values. It can handle large experiments that include protein or peptide fractionation (as SDS-PAGE, 2-D LC). It is fully configurable. Every processing step is traceable, the produced data are in open standard formats and its modularity allows easy integration into proteomic pipelines. The output results are ready for use in statistical analyses. Evaluation of MassChroQ on complex label-free data obtained from low and high-resolution mass spectrometers showed low CVs for technical reproducibility (1.4%) and high coefficients of correlation to protein quantity (0.98). MassChroQ is freely available under the GNU General Public Licence v3.0 at http://pappso.inra.fr/bioinfo/masschroq/.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

MassSorter: a tool for administrating and analyzing data from mass spectrometry experiments on proteins with known amino acid sequences

Background  

Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identification and protein characterization. Many small laboratories are interested in the characterization of a small number of proteins, e.g., how posttranslational modifications change under different conditions.     

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.     

PhoPepMass: A database and search tool assisting human phosphorylation peptide identification from mass spectrometry data

Protein phosphorylation, one of the most important protein post-translational modifications, is involved in various biological processes, and the identification of phosphorylation peptides (phosphopeptides) and their corresponding phosphorylation sites (phosphosites) will facilitate the understanding of the molecular mechanism and function of phosphorylation. Mass spectrometry (MS) provides a high-throughput technology that enables the identification of large numbers of phosphosites. PhoPepMass is designed to assist human phosphopeptide identification from MS data based on a specific database of phophopeptide masses and a multivariate hypergeometric matching algorithm. It contains 244,915 phosphosites from several public sources. Moreover, the accurate masses of peptides and fragments with phosphosites were calculated. It is the first database that provides a systematic resource for the query of phosphosites on peptides and their corresponding masses. This allows researchers to search certain proteins of which phosphosites have been reported, to browse detailed phosphopeptide and fragment information, to match masses from MS analyses with defined threshold to the corresponding phosphopeptide, and to compare proprietary phosphopeptide discovery results with results from previous studies. Additionally, a database search software is created and a “two-stage search strategy” is suggested to identify phosphopeptides from tandem mass spectra of proteomics data. We expect PhoPepMass to be a useful tool and a source of reference for proteomics researchers. PhoPepMass is available at https://www.scbit.org/phopepmass/index.html.     

基于模拟退火算法的高分辨率蛋白质质谱数据特征选择

蛋白质质谱技术是蛋白质组学的重要研究工具,它被出色地应用于癌症早期诊断等领域,但是蛋白质质谱数据带来的维灾难问题使得降维成为质谱分析的必需的步骤。本文首先将美国国家癌症研究所提供的高分辨率SELDI—TOF卵巢质谱数据进行预处理;然后将质谱数据的特征选择问题转化成基于模拟退火算法的组合优化模型,用基于线性判别式分析的分类错误率和样本后验概率构造待优化目标函数,用基于均匀分布和控制参数的方法构造新解产生器,在退火过程中添加记忆功能;然后用10-fold交叉验证法选择训练和测试样本,用线性判别式分析分类器评价降维后的质谱数据。实验证明,用模拟退火算法选择6个以上特征时,能够将高分辨率SELDI—TOF卵巢质谱数据全部正确分类,说明模拟退火算法可以很好地应用于蛋白质质谱数据的特征选择。     

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.     

Hydroxyl negative chemical ionization mass spectrometry linked with collisionally activated decomposition. A modern analytical tool in inborn errors of metabolism

Two kinds of inborn errors of metabolism, dicarboxylic aciduria and hyperoxaluria, have been studied by means of hydroxyl negative ion chemical ionization [NICI(OH-)], linked with collisionally activated decomposition experiments on the [M-H]- species of the pathognomonic organic acids. This method has led to non-controversial qualitative determinations of C4-C10 dicarboxylic acids and oxalic, glyceric and glyoxylic acids. NICI(OH-) linked with collisionally activated decomposition mass analysed ion kinetic energy spectrometry (CAD MIKES) is proposed herein for diagnostic purposes, as a valid mass spectrometric alternative to standard gas chromatographic/mass spectrometric analysis. The procedure is characterized by simplified sample treatment and by fast execution.     

Wave-spec: a preprocessing package for mass spectrometry data

Wave-spec is a pre-processing package for mass spectrometry (MS) data. The package includes several novel algorithms that overcome conventional difficulties with the pre-processing of such data. In this application note, we demonstrate step-by-step use of this package on a real-world MALDI dataset. AVAILABILITY: The package can be downloaded at http://www.vicc.org/biostatistics/supp.php. A shared mailbox (wave-spec@vanderbilt.edu) also is available for questions regarding application of the package.     

Protocols for disease classification from mass spectrometry data

We report our results in classifying protein matrix-assisted laser desorption/ionization-time of flight mass spectra obtained from serum samples into diseased and healthy groups. We discuss in detail five of the steps in preprocessing the mass spectral data for biomarker discovery, as well as our criterion for choosing a small set of peaks for classifying the samples. Cross-validation studies with four selected proteins yielded misclassification rates in the 10-15% range for all the classification methods. Three of these proteins or protein fragments are down-regulated and one up-regulated in lung cancer, the disease under consideration in this data set. When cross-validation studies are performed, care must be taken to ensure that the test set does not influence the choice of the peaks used in the classification. Misclassification rates are lower when both the training and test sets are used to select the peaks used in classification versus when only the training set is used. This expectation was validated for various statistical discrimination methods when thirteen peaks were used in cross-validation studies. One particular classification method, a linear support vector machine, exhibited especially robust performance when the number of peaks was varied from four to thirteen, and when the peaks were selected from the training set alone. Experiments with the samples randomly assigned to the two classes confirmed that misclassification rates were significantly higher in such cases than those observed with the true data. This indicates that our findings are indeed significant. We found closely matching masses in a database for protein expression in lung cancer for three of the four proteins we used to classify lung cancer. Data from additional samples, increased experience with the performance of various preprocessing techniques, and affirmation of the biological roles of the proteins that help in classification, will strengthen our conclusions in the future.