A common open representation of mass spectrometry data and its application to proteomics research

ChromA: signal-based retention time alignment for chromatography-mass spectrometry data

Summary: We describe ChromA, a web-based alignment tool for chromatography–mass spectrometry data from the metabolomics and proteomics domains. Users can supply their data in open and standardized file formats for retention time alignment using dynamic time warping with different configurable local distance and similarity functions. Additionally, user-defined anchors can be used to constrain and speedup the alignment. A neighborhood around each anchor can be added to increase the flexibility of the constrained alignment. ChromA offers different visualizations of the alignment for easier qualitative interpretation and comparison of the data. For the multiple alignment of more than two data files, the center-star approximation is applied to select a reference among input files to align to.

Availability: ChromA is available at http://bibiserv.techfak.uni-bielefeld.de/chroma. Executables and source code under the L-GPL v3 license are provided for download at the same location.

Contact:stoye@techfak.uni-bielefeld.de

Supplementary information:Supplementary data are available at Bioinformatics online.

A HUPO test sample study reveals common problems in mass spectrometry-based proteomics

We performed a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in liquid chromatography-mass spectrometry-based proteomics. We distributed an equimolar test sample, comprising 20 highly purified recombinant human proteins, to 27 laboratories. Each protein contained one or more unique tryptic peptides of 1,250 Da to test for ion selection and sampling in the mass spectrometer. Of the 27 labs, members of only 7 labs initially reported all 20 proteins correctly, and members of only 1 lab reported all tryptic peptides of 1,250 Da. Centralized analysis of the raw data, however, revealed that all 20 proteins and most of the 1,250 Da peptides had been detected in all 27 labs. Our centralized analysis determined missed identifications (false negatives), environmental contamination, database matching and curation of protein identifications as sources of problems. Improved search engines and databases are needed for mass spectrometry-based proteomics.

Interpretation of tandem mass spectra obtained from cyclic nonribosomal peptides

Natural and non-natural cyclic peptides are a crucial component in drug discovery programs because of their considerable pharmaceutical properties. Cyclosporin, microcystins, and nodularins are all notable pharmacologically important cyclic peptides. Because these biologically active peptides are often biosynthesized nonribosomally, they often contain nonstandard amino acids, thus increasing the complexity of the resulting tandem mass spectrometry data. In addition, because of the cyclic nature, the fragmentation patterns of many of these peptides showed much higher complexity when compared to related counterparts. Therefore, at the present time it is still difficult to annotate cyclic peptides MS/MS spectra. In this current work, an annotation program was developed for the annotation and characterization of tandem mass spectra obtained from cyclic peptides. This program, which we call MS-CPA is available as a web tool (http://lol.ucsd.edu/ms-cpa_v1/Input.py). Using this program, we have successfully annotated the sequence of representative cyclic peptides, such as seglitide, tyrothricin, desmethoxymajusculamide C, dudawalamide A, and cyclomarins, in a rapid manner and also were able to provide the first-pass structure evidence of a newly discovered natural product based on predicted sequence. This compound is not available in sufficient quantities for structural elucidation by other means such as NMR. In addition to the development of this cyclic annotation program, it was observed that some cyclic peptides fragmented in unexpected ways resulting in the scrambling of sequences. In summary, MS-CPA not only provides a platform for rapid confirmation and annotation of tandem mass spectrometry data obtained with cyclic peptides but also enables quantitative analysis of the ion intensities. This program facilitates cyclic peptide analysis, sequencing, and also acts as a useful tool to investigate the uncommon fragmentation phenomena of cyclic peptides and aids the characterization of newly discovered cyclic peptides encountered in drug discovery programs.

A proteomic study of pectin-degrading enzymes secreted by Botrytis cinerea grown in liquid culture

Botrytis cinerea is a pathogenic filamentous fungus, which infects more than 200 plant species. The enzymes secreted by B. cinerea play an important role in the successful colonization of a host plant. Some of the secreted enzymes are involved in the degradation of pectin, a major component of the plant cell wall. A total of 126 proteins secreted by B. cinerea were identified by growing the fungus on highly or partially esterified pectin, or on sucrose in liquid culture. Sixty-seven common proteins were identified in each of the growth conditions, of which 50 proteins exhibited a SignalP motif. Thirteen B. cinerea proteins with functions related to pectin degradation were identified in both pectin growth conditions, while only four were identified in sucrose. Our results indicate it is unlikely that the activation of B. cinerea from the dormant state to active infection is solely dependent on changes in the degree of esterification of the pectin component of the plant cell wall. Further, these results suggest that future studies of the B. cinerea secretome in infections of ripe and unripe fruits will provide important information that will describe the mechanisms that the fungus employs to access nutrients and decompose tissues.

Biological knowledge management: the emerging role of the Semantic Web technologies

New knowledge is produced at a continuously increasing speed, and the list of papers, databases and other knowledge sources that a researcher in the life sciences needs to cope with is actually turning into a problem rather than an asset. The adequate management of knowledge is therefore becoming fundamentally important for life scientists, especially if they work with approaches that thoroughly depend on knowledge integration, such as systems biology. Several initiatives to organize biological knowledge sources into a readily exploitable resourceome are presently being carried out. Ontologies and Semantic Web technologies revolutionize these efforts. Here, we review the benefits, trends, current possibilities, and the potential this holds for the biosciences.

Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300-1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

HD desktop: an integrated platform for the analysis and visualization of H/D exchange data

Here we describe an integrated software platform titled HD Desktop designed specifically to enhance the analysis of hydrogen/deuterium exchange (HDX) mass spectrometry data. HD Desktop integrates tools for data extraction with visualization components within a single web-based application. The interface design enables users to navigate from the peptide view to the sample and experiment levels, tracking all manipulations while updating the aggregate graphs in real time. HD Desktop is integrated with a relational database designed to provide performance enhancements, as well as a robust model for data storage and retrieval. Additional features of the software include retention time determination, which is achieved with the use of theoretical isotope fitting; here, we assume that the best theoretical fit will occur at the correct retention time for any given peptide. Peptide data consolidation for the rendering of data in 2D was realized by automating known and novel approaches. Designed to address broad needs of the HDX community, the platform presented here provides an efficient and manageable workflow for HDX data analysis and is freely available as a web tool at the project home page http://hdx.florida.scripps.edu.

N-linked glycoproteomic analysis of formalin-fixed and paraffin-embedded tissues

Formalin-fixed, paraffin-embedded (FFPE) tissues have been used to discover disease-associated protein changes using mass spectrometry. Protein post-translational modifications such as glycosylation are known to associate with disease development. In this study, we investigated whether FFPE tissues preserve such modifications and therefore can be used as specimen of choice to identify the disease-associated modifications. We isolated the glycopeptides from the tryptic digest of frozen and FFPE lung tissues using solid-phase extraction of glycopeptides and analyzed them using mass spectrometry. The glycopeptides identified from FFPE lung tissue were compared to the ones from frozen lung tissue regarding their relative abundance, unique glycosylation sites, and subcellular locations. The results from our study confirmed that glycosylation in FFPE tissues are preserved and FFPE tissues can be used for discovery of new disease associated changes in protein modifications. Furthermore, we demonstrated the feasibility of applying the strategy of glycopeptide isolation from tryptic peptides of FFPE tissue to other tissues such as liver and heart.

Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry

We present a mass spectrometry-based strategy for the specific detection and quantification of cell surface proteome changes. The method is based on the label-free quantification of peptide patterns acquired by high mass accuracy mass spectrometry using new software tools and the cell surface capturing technology that selectively enriches glycopeptides exposed to the cell exterior. The method was applied to monitor dynamic protein changes in the cell surface glycoproteome of Drosophila melanogaster cells. The results led to the construction of a cell surface glycoprotein atlas consisting of 202 cell surface glycoproteins of D. melanogaster Kc167 cells and indicated relative quantitative changes of cell surface glycoproteins in four different cellular states. Furthermore we specifically investigated cell surface proteome changes upon prolonged insulin stimulation. The data revealed insulin-dependent cell surface glycoprotein dynamics, including insulin receptor internalization, and linked these changes to intracellular signaling networks.

PepSeeker: mining information from proteomic data

Driven by advances in mass spectrometry and analytical chemistry, coupled with the expanding number of completely sequenced genomes, proteomics is becoming a widely exploited technology for characterizing the proteins found in living systems. As proteomics becomes increasingly more high-throughput there is a parallel need for storage of the large quantities of data generated, to support data exchange and allow further analyses. The capture and storage of such data, along with subsequent release and dissemination, not only aid in sharing of the data throughout the proteomics community but also provide scientific insights into the observations between different laboratories, instruments, and software. Growing numbers of resources offer a range of approaches for the capture, storage, and dissemination of proteomic experimental data reflecting the fact that proteomics has now come of age in the postgenomic era and is delivering large, complex datasets that are rich in information. This chapter demonstrates how one such resource, PepSeeker, can be used to mine useful information from proteomic data, which can then be exploited for peptide identification algorithms via a better understanding of how peptides fragment inside mass spectrometers.

Data standards and controlled vocabularies for proteomics

Proteomics data can be diverse and complex, and are typically produced on a large scale. To allow sharing and centralized storage and dissemination of such results, the Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has created a set of community standards for the exchange of mass spectrometry and protein interaction data. We describe the origins and overall concepts behind these standards, as well as the individual efforts that are ongoing in the field of mass spectrometry proteomics and protein interactions.

Assembling proteomics data as a prerequisite for the analysis of large scale experiments

BACKGROUND

Despite the complete determination of the genome sequence of a huge number of bacteria, their proteomes remain relatively poorly defined. Beside new methods to increase the number of identified proteins new database applications are necessary to store and present results of large- scale proteomics experiments.

RESULTS

In the present study, a database concept has been developed to address these issues and to offer complete information via a web interface. In our concept, the Oracle based data repository system SQL-LIMS plays the central role in the proteomics workflow and was applied to the proteomes of Mycobacterium tuberculosis, Helicobacter pylori, Salmonella typhimurium and protein complexes such as 20S proteasome. Technical operations of our proteomics labs were used as the standard for SQL-LIMS template creation. By means of a Java based data parser, post-processed data of different approaches, such as LC/ESI-MS, MALDI-MS and 2-D gel electrophoresis (2-DE), were stored in SQL-LIMS. A minimum set of the proteomics data were transferred in our public 2D-PAGE database using a Java based interface (Data Transfer Tool) with the requirements of the PEDRo standardization. Furthermore, the stored proteomics data were extractable out of SQL-LIMS via XML.

CONCLUSION

The Oracle based data repository system SQL-LIMS played the central role in the proteomics workflow concept. Technical operations of our proteomics labs were used as standards for SQL-LIMS templates. Using a Java based parser, post-processed data of different approaches such as LC/ESI-MS, MALDI-MS and 1-DE and 2-DE were stored in SQL-LIMS. Thus, unique data formats of different instruments were unified and stored in SQL-LIMS tables. Moreover, a unique submission identifier allowed fast access to all experimental data. This was the main advantage compared to multi software solutions, especially if personnel fluctuations are high. Moreover, large scale and high-throughput experiments must be managed in a comprehensive repository system such as SQL-LIMS, to query results in a systematic manner. On the other hand, these database systems are expensive and require at least one full time administrator and specialized lab manager. Moreover, the high technical dynamics in proteomics may cause problems to adjust new data formats. To summarize, SQL-LIMS met the requirements of proteomics data handling especially in skilled processes such as gel-electrophoresis or mass spectrometry and fulfilled the PSI standardization criteria. The data transfer into a public domain via DTT facilitated validation of proteomics data. Additionally, evaluation of mass spectra by post-processing using MS-Screener improved the reliability of mass analysis and prevented storage of data junk.

Improving mass measurement accuracy in mass spectrometry based proteomics by combining open source tools for chromatographic alignment and internal calibration

Accurate mass determination enhances peptide identification in mass spectrometry based proteomics. We here describe the combination of two previously published open source software tools to improve mass measurement accuracy in Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS). The first program, msalign, aligns one MS/MS dataset with one FTICRMS dataset. The second software, recal2, uses peptides identified from the MS/MS data for automated internal calibration of the FTICR spectra, resulting in sub-ppm mass measurement errors.

Identification of secreted glycoproteins of human prostate and bladder stromal cells by comparative quantitative proteomics

BACKGROUND

Functional development of the prostate is governed by stromal mesenchyme induction and epithelial response. Stromal/epithelial signaling can be mediated through direct cell-cell contact and diffusible factors and their cell surface receptors. These inducers are likely secreted or membrane-associated extracellular proteins. Given the importance of intercellular communication, it is possible that diseases like cancer could arise from a loss of this communication. One approach to gain a molecular understanding of stromal cells is to identify, as a first step, secreted stromal signaling factors. We proposed to do this by comparative analysis between bladder and prostate.

METHODS

Secreted proteins were identified from cultured normal prostate and bladder stromal mesenchyme cells by glycopeptide-capture method followed by mass spectrometry. Differences in protein abundance between prostate and bladder were quantified from calculated peptide ion current area (PICA) followed by Western validation. Functional and pathway analyses of the proteins were carried out by Gene Ontology (GO) and Teranode software.

RESULTS

This analysis produced a list of 116 prostate and 84 bladder secreted glycoproteins with ProteinProphet probability scores > or =0.9. Stromal proteins upregulated in the prostate include cathepsin L, follistatin-related protein, neuroendocrine convertase, tumor necrosis factor receptor, and others that are known to be involved in signal transduction, extracellular matrix interaction, differentiation and transport.

CONCLUSIONS

We have identified a number of potential proteins for stromal signaling and bladder or prostate differentiation program. The prostate stromal/epithelial signaling may be accomplished through activation of the ECM-receptor interaction, complement and coagulation cascades, focal adhesion and cell adhesion pathways.

A PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) protein

The serine/threonine protein phosphatases are targeted to specific subcellular locations and substrates in part via interactions with a wide variety of regulatory proteins. Understanding these interactions is thus critical to understanding phosphatase function. Using an iterative affinity purification/mass spectrometry approach, we generated a high density interaction map surrounding the protein phosphatase 2A catalytic subunit. This approach recapitulated the assembly of the PP2A catalytic subunit into many different trimeric complexes but also revealed several new protein-protein interactions. Here we define a novel large multiprotein assembly, referred to as the striatin-interacting phosphatase and kinase (STRIPAK) complex. STRIPAK contains the PP2A catalytic (PP2Ac) and scaffolding (PP2A A) subunits, the striatins (PP2A regulatory B''' subunits), the striatin-associated protein Mob3, the novel proteins STRIP1 and STRIP2 (formerly FAM40A and FAM40B), the cerebral cavernous malformation 3 (CCM3) protein, and members of the germinal center kinase III family of Ste20 kinases. Although the function of the CCM3 protein is unknown, the CCM3 gene is mutated in familial cerebral cavernous malformations, a condition associated with seizures and strokes. Our proteomics survey indicates that a large portion of the CCM3 protein resides within the STRIPAK complex, opening the way for further studies of CCM3 biology. The STRIPAK assembly establishes mutually exclusive interactions with either the CTTNBP2 proteins (which interact with the cytoskeletal protein cortactin) or a second subcomplex consisting of the sarcolemmal membrane-associated protein (SLMAP) and the related coiled-coil proteins suppressor of IKKepsilon (SIKE) and FGFR1OP2. We have thus identified several novel PP2A-containing protein complexes, including a large assembly linking kinases and phosphatases to a gene mutated in human disease.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics

Background

Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.

Results

We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.

Conclusion

The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

Quantifying protein interface footprinting by hydroxyl radical oxidation and molecular dynamics simulation: application to galectin-1

Biomolecular surface mapping methods offer an important alternative method for characterizing protein-protein and protein-ligand interactions in cases in which it is not possible to determine high-resolution three-dimensional (3D) structures of complexes. Hydroxyl radical footprinting offers a significant advance in footprint resolution compared with traditional chemical derivatization. Here we present results of footprinting performed with hydroxyl radicals generated on the nanosecond time scale by laser-induced photodissociation of hydrogen peroxide. We applied this emerging method to a carbohydrate-binding protein, galectin-1. Since galectin-1 occurs as a homodimer, footprinting was employed to characterize the interface of the monomeric subunits. Efficient analysis of the mass spectrometry data for the oxidized protein was achieved with the recently developed ByOnic (Palo Alto, CA) software that was altered to handle the large number of modifications arising from side-chain oxidation. Quantification of the level of oxidation has been achieved by employing spectral intensities for all of the observed oxidation states on a per-residue basis. The level of accuracy achievable from spectral intensities was determined by examination of mixtures of synthetic peptides related to those present after oxidation and tryptic digestion of galectin-1. A direct relationship between side-chain solvent accessibility and level of oxidation emerged, which enabled the prediction of the level of oxidation given the 3D structure of the protein. The precision of this relationship was enhanced through the use of average solvent accessibilities computed from 10 ns molecular dynamics simulations of the protein.

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

Building consensus spectral libraries for peptide identification in proteomics

Spectral searching has drawn increasing interest as an alternative to sequence-database searching in proteomics. We developed and validated an open-source software toolkit, SpectraST, to enable proteomics researchers to build spectral libraries and to integrate this promising approach in their data-analysis pipeline. It allows individual researchers to condense raw data into spectral libraries, summarizing information about observed proteomes into a concise and retrievable format for future data analyses.

The collaboratory for MS3D: a new cyberinfrastructure for the structural elucidation of biological macromolecules and their assemblies using mass spectrometry-based approaches

Modern biomedical research is evolving with the rapid growth of diverse data types, biophysical characterization methods, computational tools and extensive collaboration among researchers spanning various communities and having complementary backgrounds and expertise. Collaborating researchers are increasingly dependent on shared data and tools made available by other investigators with common interests, thus forming communities that transcend the traditional boundaries of the single research laboratory or institution. Barriers, however, remain to the formation of these virtual communities, usually due to the steep learning curve associated with becoming familiar with new tools, or with the difficulties associated with transferring data between tools. Recognizing the need for shared reference data and analysis tools, we are developing an integrated knowledge environment that supports productive interactions among researchers. Here we report on our current collaborative environment, which focuses on bringing together structural biologists working in the area of mass spectrometric based methods for the analysis of tertiary and quaternary macromolecular structures (MS3D) called the Collaboratory for MS3D (C-MS3D). C-MS3D is a Web-portal designed to provide collaborators with a shared work environment that integrates data storage and management with data analysis tools. Files are stored and archived along with pertinent meta data in such a way as to allow file handling to be tracked (data provenance) and data files to be searched using keywords and modification dates. While at this time the portal is designed around a specific application, the shared work environment is a general approach to building collaborative work groups. The goal of this is to not only provide a common data sharing and archiving system, but also to assist in the building of new collaborations and to spur the development of new tools and technologies.

MS-BID: a Java package for label-free LC-MS-based comparative proteomic analysis

MS-BID (MS Biomarker Discovery Platform) is an integrative computational pipeline for biomarker discovery using LC-MS-based comparative proteomic analysis. This platform consists of several computational tools for: (i) detecting peptides in the collected patterns; (ii) matching detected peptides across a number of LC-MS datasets and (iii) selecting discriminatory peptides between classes of samples.

AVAILABILITY

MS-BID source codes, binaries and documentations are freely available under LGPL from http://tools.proteomecenter.org/msBID.php.

The impact of peptide abundance and dynamic range on stable-isotope-based quantitative proteomic analyses

Recently, mass spectrometry has been employed in many studies to provide unbiased, reproducible, and quantitative protein abundance information on a proteome-wide scale. However, how instruments' limited dynamic ranges impact the accuracy of such measurements has remained largely unexplored, especially in the context of complex mixtures. Here, we examined the distribution of peptide signal versus background noise (S/N) and its correlation with quantitative accuracy. With the use of metabolically labeled Jurkat cell lysate, over half of all confidently identified peptides had S/N ratios less than 10 when examined using both hybrid linear ion trap-Fourier transform ion cyclotron resonance and Orbitrap mass spectrometers. Quantification accuracy was also highly correlated with S/N. We developed a mass precision algorithm that significantly reduced measurement variance at low S/N beyond the use of highly accurate mass information alone and expanded it into a new software suite, Vista. We also evaluated the interplay between mass measurement accuracy and S/N; finding a balance between both parameters produced the greatest identification and quantification rates. Finally, we demonstrate that S/N can be a useful surrogate for relative abundance ratios when only a single species is detected.

Stable isotope labeling tandem mass spectrometry (SILT): integration with peptide identification and extension to data-dependent scans

Quantitation of relative or absolute amounts of proteins by mass spectrometry can be prone to large errors. The use of MS/MS ion intensities and stable isotope labeling, which we term stable isotope labeling tandem mass spectrometry (SILT), decreases the effects of contamination from unrelated compounds. We present a software package (SILTmass) that automates protein identification and quantification by the SILT method. SILTmass has the ability to analyze the kinetics of protein turnover, in addition to relative and absolute protein quantitation. Instead of extracting chromatograms to find elution peaks, SILTmass uses only scans in which a peptide is identified and that meet an ion intensity threshold. Using only scans with identified peptides, the accuracy and precision of SILT is shown to be superior to precursor ion intensities, particularly at high or low dilutions of the isotope labeled compounds or with low amounts of protein. Using example scans, we demonstrate likely reasons for the improvements in quantitation by SILT. The appropriate use of variable modifications in peptide identification is described for measurement of protein turnover kinetics. The combination of identification with SILT facilitates quantitation without peak detection and helps to ensure the appropriate use of variable modifications for kinetics experiments.

MeltDB: a software platform for the analysis and integration of metabolomics experiment data

MOTIVATION

The recent advances in metabolomics have created the potential to measure the levels of hundreds of metabolites which are the end products of cellular regulatory processes. The automation of the sample acquisition and subsequent analysis in high-throughput instruments that are capable of measuring metabolites is posing a challenge on the necessary systematic storage and computational processing of the experimental datasets. Whereas a multitude of specialized software systems for individual instruments and preprocessing methods exists, there is clearly a need for a free and platform-independent system that allows the standardized and integrated storage and analysis of data obtained from metabolomics experiments. Currently there exists no such system that on the one hand supports preprocessing of raw datasets but also allows to visualize and integrate the results of higher level statistical analyses within a functional genomics context.

RESULTS

To facilitate the systematic storage, analysis and integration of metabolomics experiments, we have implemented MeltDB, a web-based software platform for the analysis and annotation of datasets from metabolomics experiments. MeltDB supports open file formats (netCDF, mzXML, mzDATA) and facilitates the integration and evaluation of existing preprocessing methods. The system provides researchers with means to consistently describe and store their experimental datasets. Comprehensive analysis and visualization features of metabolomics datasets are offered to the community through a web-based user interface. The system covers the process from raw data to the visualization of results in a knowledge-based background and is integrated into the context of existing software platforms of genomics and transcriptomics at Bielefeld University. We demonstrate the potential of MeltDB by means of a sample experiment where we dissect the influence of three different carbon sources on the gram-negative bacterium Xanthomonas campestris pv. campestris on the level of measured metabolites. Experimental data are stored, analyzed and annotated within MeltDB and accessible via the public MeltDB web server.

AVAILABILITY

The system is publicly available at http://meltdb.cebitec.uni-bielefeld.de.

A Semantic Web management model for integrative biomedical informatics

BACKGROUND

Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.

METHODOLOGY/PRINCIPAL FINDINGS

The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MD Anderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.

CONCLUSIONS/SIGNIFICANCE

The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis.

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.

Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.

MRMer, an interactive open source and cross-platform system for data extraction and visualization of multiple reaction monitoring experiments

Multiple reaction monitoring (MRM) mass spectrometry identifies and quantifies specific peptides in a complex mixture with very high sensitivity and speed and thus has promise for the high throughput screening of clinical samples for candidate biomarkers. We have developed an interactive software platform, called MRMer, for managing highly complex MRM-MS experiments, including quantitative analyses using heavy/light isotopic peptide pairs. MRMer parses and extracts information from MS files encoded in the platform-independent mzXML data format. It extracts and infers precursor-product ion transition pairings, computes integrated ion intensities, and permits rapid visual curation for analyses exceeding 1000 precursor-product pairs. Results can be easily output for quantitative comparison of consecutive runs. Additionally MRMer incorporates features that permit the quantitative analysis experiments including heavy and light isotopic peptide pairs. MRMer is open source and provided under the Apache 2.0 license.

ProteoWizard: open source software for rapid proteomics tools development

Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, which has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and commercial projects. In addition to the library, we also introduce a rapidly growing set of companion tools whose implementation helps to illustrate the simplicity of developing applications on top of the ProteoWizard library.

Availability: Cross-platform software that compiles using native compilers (i.e. GCC on Linux, MSVC on Windows and XCode on OSX) is available for download free of charge, at http://proteowizard.sourceforge.net. This website also provides code examples, and documentation. It is our hope the ProteoWizard project will become a standard platform for proteomics development; consequently, code use, contribution and further development are strongly encouraged.

Contact:darren@proteowizard.org; parag@ucla.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Protein identification using TurboSEQUEST

SEQUEST is the most widely used software tool for identifying proteins in complex mixtures. It is a mature, robust program that identifies peptides directly from uninterpreted tandem mass spectra, thus making large-scale proteomic studies possible. Thermo Electron's TurboSEQUEST provides a Windows-based graphical user interface for running SEQUEST and interpreting results. The protocol in this unit describes the basic steps involved in processing mass spectrometric data and analyzing results using TurboSEQUEST. It also provides an overview of the SEQUEST algorithm and a discussion of data filtering methods, critical issues in data interpretation, and available resources that can facilitate proper interpretation of SEQUEST results.

ISPIDER Central: an integrated database web-server for proteomics

Despite the growing volumes of proteomic data, integration of the underlying results remains problematic owing to differences in formats, data captured, protein accessions and services available from the individual repositories. To address this, we present the ISPIDER Central Proteomic Database search (http://www.ispider.manchester.ac.uk/cgi-bin/ProteomicSearch.pl), an integration service offering novel search capabilities over leading, mature, proteomic repositories including PRoteomics IDEntifications database (PRIDE), PepSeeker, PeptideAtlas and the Global Proteome Machine. It enables users to search for proteins and peptides that have been characterised in mass spectrometry-based proteomics experiments from different groups, stored in different databases, and view the collated results with specialist viewers/clients. In order to overcome limitations imposed by the great variability in protein accessions used by individual laboratories, the European Bioinformatics Institute's Protein Identifier Cross-Reference (PICR) service is used to resolve accessions from different sequence repositories. Custom-built clients allow users to view peptide/protein identifications in different contexts from multiple experiments and repositories, as well as integration with the Dasty2 client supporting any annotations available from Distributed Annotation System servers. Further information on the protein hits may also be added via external web services able to take a protein as input. This web server offers the first truly integrated access to proteomics repositories and provides a unique service to biologists interested in mass spectrometry-based proteomics.

Comparative LC-MS: a landscape of peaks and valleys

Quantitative proteomics approaches using stable isotopes are well-known and used in many labs nowadays. More recently, high resolution quantitative approaches are reported that rely on LC-MS quantitation of peptide concentrations by comparing peak intensities between multiple runs obtained by continuous detection in MS mode. Characteristic of these comparative LC-MS procedures is that they do not rely on the use of stable isotopes; therefore the procedure is often referred to as label-free LC-MS. In order to compare at comprehensive scale peak intensity data in multiple LC-MS datasets, dedicated software is required for detection, matching and alignment of peaks. The high accuracy in quantitative determination of peptide abundance provides an impressive level of detail. This approach also requires an experimental set-up where quantitative aspects of protein extraction and reproducible separation conditions need to be well controlled. In this paper we will provide insight in the critical parameters that affect the quality of the results and list an overview of the most recent software packages that are available for this procedure.

Folding and assembly of large macromolecular complexes monitored by hydrogen-deuterium exchange and mass spectrometry

Recent advances in protein mass spectrometry (MS) have enabled determinations of hydrogen deuterium exchange (HDX) in large macromolecular complexes. HDX-MS became a valuable tool to follow protein folding, assembly and aggregation. The methodology has a wide range of applications in biotechnology ranging from quality control for over-expressed proteins and their complexes to screening of potential ligands and inhibitors. This review provides an introduction to protein folding and assembly followed by the principles of HDX and MS detection, and concludes with selected examples of applications that might be of interest to the biotechnology community.

Advanced data-mining strategies for the analysis of direct-infusion ion trap mass spectrometry data from the association of perennial ryegrass with its endophytic fungus, Neotyphodium lolii

Direct-infusion mass spectrometry (MS) was applied to study the metabolic effects of the symbiosis between the endophytic fungus Neotyphodium lolii and its host perennial ryegrass (Lolium perenne) in three different tissues (immature leaf, blade, and sheath). Unbiased direct-infusion MS using a linear ion trap mass spectrometer allowed metabolic effects to be determined free of any preconceptions and in a high-throughput fashion. Not only the full MS(1) mass spectra (range 150-1,000 mass-to-charge ratio) were obtained but also MS(2) and MS(3) product ion spectra were collected on the most intense MS(1) ions as described previously (Koulman et al., 2007b). We developed a novel computational methodology to take advantage of the MS(2) product ion spectra collected. Several heterogeneous MS(1) bins (different MS(2) spectra from the same nominal MS(1)) were identified with this method. Exploratory data analysis approaches were also developed to investigate how the metabolome differs in perennial ryegrass infected with N. lolii in comparison to uninfected perennial ryegrass. As well as some known fungal metabolites like peramine and mannitol, several novel metabolites involved in the symbiosis, including putative cyclic oligopeptides, were identified. Correlation network analysis revealed a group of structurally related oligosaccharides, which differed significantly in concentration in perennial ryegrass sheaths due to endophyte infection. This study demonstrates the potential of the combination of unbiased metabolite profiling using ion trap MS and advanced data-mining strategies for discovering unexpected perturbations of the metabolome, and generating new scientific questions for more detailed investigations in the future.

Exploration of the normal human bronchoalveolar lavage fluid proteome

We obtained insight into normal lung function by proteome analysis of bronchoalveolar lavage fluid (BALF) from six normal human subjects using a "Lyse-N-Go' shotgun proteomic protocol. Intra-sample variation was calculated using three different label-free methods, (i) protein sequence coverage; (ii) peptide spectral counts and (iii) peptide single-ion current areas (PICA), which generates protein expression data by summation of the area under the curve for a given peptide single-ion current trace and then adding values for all peptides from that same parent protein. PICA gave the least intra-subject variability and was used to calculate differences in protein expression between the six subjects. We observed an average threefold inter-sample variability, which affects analysis of changes in protein expression that occur in different diseases. We detected 167 unique proteins with >100 proteins detected in each of the six individual BAL samples, 42 of which were common to all six subjects. Gene ontology analysis demonstrated enrichment of several biological processes in the lung, reflecting its expected role in gas exchange and host defense as an immune organ. The same biological processes were enriched compared to either plasma or total genome proteome, suggesting an active enrichment of plasma proteins in the lung rather than passive capillary leak.

Glycerophospholipid identification and quantitation by electrospray ionization mass spectrometry

Glycerophospholipids are the structural building blocks of the cellular membrane. In addition to creating a protective barrier around the cell, lipids are precursors of intracellular signaling molecules that modulate membrane trafficking and are involved in transmembrane signal transduction. Phospholipids are also increasingly recognized as important participants in the regulation and control of cellular function and disease. Analysis and characterization of lipid species by mass spectrometry (MS) have evolved and advanced with improvements in instrumentation and technology. Key advances, including the development of "soft" ionization techniques for MS such as electrospray ionization (ESI), matrix-assisted laser desorption/ionization (MALDI), and tandem mass spectrometry (MS/MS), have facilitated the analysis of complex lipid mixtures by overcoming the earlier limitations. ESI-MS has become the technique of choice for the analysis of multi-component mixtures of lipids from biological samples due to its exceptional sensitivity and capacity for high throughput. This chapter covers qualitative and quantitative MS methods used for the elucidation of glycerophospholipid identity and quantity in cell or tissue extracts. Sections are included on the extraction, MS analysis, and data analysis of glycerophospholipids and polyphosphoinositides.

OpenMS - an open-source software framework for mass spectrometry

BACKGROUND

Mass spectrometry is an essential analytical technique for high-throughput analysis in proteomics and metabolomics. The development of new separation techniques, precise mass analyzers and experimental protocols is a very active field of research. This leads to more complex experimental setups yielding ever increasing amounts of data. Consequently, analysis of the data is currently often the bottleneck for experimental studies. Although software tools for many data analysis tasks are available today, they are often hard to combine with each other or not flexible enough to allow for rapid prototyping of a new analysis workflow.

RESULTS

We present OpenMS, a software framework for rapid application development in mass spectrometry. OpenMS has been designed to be portable, easy-to-use and robust while offering a rich functionality ranging from basic data structures to sophisticated algorithms for data analysis. This has already been demonstrated in several studies.

CONCLUSION

OpenMS is available under the Lesser GNU Public License (LGPL) from the project website at http://www.openms.de.

SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.

Five years of progress in the Standardization of Proteomics Data 4th Annual Spring Workshop of the HUPO-Proteomics Standards Initiative April 23-25, 2007 Ecole Nationale Supérieure (ENS), Lyon, France

Over the last five years, the Human Proteome Organisation Proteomics Standards Initiative (HUPO PSI) has produced and released community-accepted XML interchange formats in the fields of mass spectrometry, molecular interactions and gel electrophoresis, have led the field in the discussion of the minimum information with which such data should be annotated and are now in the process of publishing much of this information. At this 4(th) Spring workshop, the emphasis was on consolidating this effort, refining and improving the existing models and in pushing these forward to align with more broadly encompassing efforts such as FuGE (Jones, A.R., Pizarro, A., Spellman, P., Miller, M., FuGE Working Group FuGE: Functional Genomics Experiment Object Model. OMICS 2006, 10, 179-184) and the Ontology for Biomedical Investigation (OBI). The effort to merge the existing mass spectrometry XML interchange formats, mzData and mzXML, into one single standard mzML yielded significant progress. Also the preliminary design of AnalysisXML was extended to include several new use cases and better support for quantification information. Finally the Molecular Interaction group discussed the development of a molecular interaction scoring system with accompanying gold standard data test sets.