A mass spectrometry proteomics data management platform

Mass spectrometry-based proteomics is increasingly being used in biomedical research. These experiments typically generate a large volume of highly complex data, and the volume and complexity are only increasing with time. There exist many software pipelines for analyzing these data (each typically with its own file formats), and as technology improves, these file formats change and new formats are developed. Files produced from these myriad software programs may accumulate on hard disks or tape drives over time, with older files being rendered progressively more obsolete and unusable with each successive technical advancement and data format change. Although initiatives exist to standardize the file formats used in proteomics, they do not address the core failings of a file-based data management system: (1) files are typically poorly annotated experimentally, (2) files are organically distributed across laboratory file systems in an ad hoc manner, (3) files formats become obsolete, and (4) searching the data and comparing and contrasting results across separate experiments is very inefficient (if possible at all). Here we present a relational database architecture and accompanying web application dubbed Mass Spectrometry Data Platform that is designed to address the failings of the file-based mass spectrometry data management approach. The database is designed such that the output of disparate software pipelines may be imported into a core set of unified tables, with these core tables being extended to support data generated by specific pipelines. Because the data are unified, they may be queried, viewed, and compared across multiple experiments using a common web interface. Mass Spectrometry Data Platform is open source and freely available at http://code.google.com/p/msdapl/.     

The application of mass spectrometry to membrane proteomics

Membrane proteins perform some of the most important functions in the cell, including the regulation of cell signaling through surface receptors, cell-cell interactions, and the intracellular compartmentalization of organelles. Recent developments in proteomic strategies have focused on the inclusion of membrane proteins in high-throughput analyses. While slow and steady progress continues to be made in gel-based technologies, significant advances have been reported in non-gel shotgun methods using liquid chromatography coupled to mass spectrometry (LC/MS). These latter strategies facilitate the identification of large numbers of membrane proteins and modifications, and have the potential to provide insights into protein topology and orientation in membranes.     

生物质谱与蛋白质组学

蛋白质组学是后基因组学时代最受关注的研究领域之一,其核心的鉴定技术——生物质谱近年来在仪器设计以及鉴定通量、分辨率和灵敏度等各方面均有质的飞跃,促进了蛋白质表达谱作图、定量蛋白质组分析、亚细胞器蛋白质组作图、蛋白质翻译后修饰以及蛋白质相互作用等蛋白质组研究各个领域的飞速发展。本综述了生物质谱技术的最新进展,及其在蛋白质组学研究中的应用。     

Improving large-scale proteomics by clustering of mass spectrometry data

Tandem mass spectrometry (MS/MS), coupled with liquid chromatography (LC), is a powerful tool for the analysis and comparison of complex protein and peptide mixtures. However, the extremely large amounts of data that result from the process are very complex and difficult to analyze. We show how the clustering of similar spectra from multiple LC-MS/MS runs can help in data management and improve the analysis of complex peptide mixtures. The major effect of spectrum clustering is the reduction of the huge amounts of data to a manageable size. As a result, analysis time is shorter and more data can be stored for further analysis. Furthermore, spectrum quality improvement allows the identification of more peptides with greater confidence, the comparison of complex peptide mixtures is facilitated, and the entire proteomics project is presented in concise form. Pep-Miner is an advanced software tool that implements these clustering-based applications. It proved useful in several comparative proteomics projects involving lung cancer cells and various other cell types. In one of these projects, Pep-Miner reduced 517 000 spectra to 20 900 clusters and identified 2518 peptides derived from 830 proteins. Clustering and identification lasted less than two hours on an IBM Thinkpad T23 computer (laptop). Pep-Miner's unique properties make it a very useful tool for large-scale shotgun proteomics projects.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Recent progress in mass spectrometry proteomics for biomedical research

Proteins are the key players in many cellular processes. Their composition, trafficking, and interactions underlie the dynamic processes of life. Furthermore, diseases are frequently accompanied by malfunction of proteins at multiple levels. Understanding how biological processes are regulated at the protein level is critically important to understanding the molecular basis for diseases and often shed light on disease prevention, diagnosis, and treatment. With rapid advances in mass spectrometry (MS) instruments and experimental methodologies, MS-based proteomics has become a reliable and essential tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed great expansion of knowledge of human diseases with the application of MS-based proteomic technologies, which has led to many exciting discoveries. Herein we review the recent progress in MS-based proteomics in biomedical research, including that in establishing disease-related proteomes and interactomes. We also discuss how this progress will benefit biomedical research and clinical diagnosis and treatment of disease.     

A collection of open source applications for mass spectrometry data mining

We present several bioinformatics applications for the identification and quantification of phosphoproteome components by MS. These applications include a front‐end graphical user interface that combines several Thermo RAW formats to MASCOT? Generic Format extractors (EasierMgf), two graphical user interfaces for search engines OMSSA and SEQUEST (OmssaGui and SequestGui), and three applications, one for the management of databases in FASTA format (FastaTools), another for the integration of search results from up to three search engines (Integrator), and another one for the visualization of mass spectra and their corresponding database search results (JsonVisor). These applications were developed to solve some of the common problems found in proteomic and phosphoproteomic data analysis and were integrated in the workflow for data processing and feeding on our LymPHOS database. Applications were designed modularly and can be used standalone. These tools are written in Perl and Python programming languages and are supported on Windows platforms. They are all released under an Open Source Software license and can be freely downloaded from our software repository hosted at GoogleCode.     

Homology-based functional proteomics by mass spectrometry: application to the Xenopus microtubule-associated proteome

The application of functional proteomics to important model organisms with unsequenced genomes is restricted because of the limited ability to identify proteins by conventional mass spectrometry (MS) methods. Here we applied MS and sequence-similarity database searching strategies to characterize the Xenopus laevis microtubule-associated proteome. We identified over 40 unique, and many novel, microtubule-bound proteins, as well as two macromolecular protein complexes involved in protein translation. This finding was corroborated by electron microscopy showing the presence of ribosomes on spindles assembled from frog egg extracts. Taken together, these results suggest that protein translation occurs on the spindle during meiosis in the Xenopus oocyte. These findings were made possible due to the application of sequence-similarity methods, which extended mass spectrometric protein identification capabilities by 2-fold compared to conventional methods.     

A noise model for mass spectrometry based proteomics

Motivation: Mass spectrometry data are subjected to considerablenoise. Good noise models are required for proper detection andquantification of peptides. We have characterized noise in bothquadrupole time-of-flight (Q-TOF) and ion trap data, and haveconstructed models for the noise. Results: We find that the noise in Q-TOF data from Applied BiosystemsQSTAR fits well to a combination of multinomial and Poissonmodel with detector dead-time correction. In comparison, iontrap noise from Agilent MSD-Trap-SL is larger than the Q-TOFnoise and is proportional to Poisson noise. We then demonstratethat the noise model can be used to improve deisotoping forpeptide detection, by estimating appropriate cutoffs of thegoodness of fit parameter at prescribed error rates. The noisemodels also have implications in noise reduction, retentiontime alignment and significance testing for biomarker discovery. Contact: pdu{at}us.ibm.com Supplementary information: Supplementary data are availableat Bioinfomatics Online. Associate Editor: Olga Troyanskaya     

Analytical aspects of mass spectrometry and proteomics

Mass spectrometry plays an essential role in proteomics analysis and research. In recent years, it has been increasingly recognized that a key to proteomics using mass spectrometry relies not only on the instrument itself, but also on the analytical strategies and front-end sample-handling techniques. The advances of separations and mass spectrometry are having an increasing impact on the discovery of disease biomarkers and the understanding of cellular processes.     

Intact protein mass spectrometry and top-down proteomics

American Society for Mass Spectrometry Sanibel meeting on top-down mass spectrometry

St Pete Beach, FL, USA, 24–27 January 2013

Top-down mass spectrometry involves analysis of intact proteins, typically using electrospray ionization, as multiple charging enhances dissociation and thus identification by comparison of precursor and product ion masses with protein sequence databases. Traditionally a low-throughput, precision technology performed on high-resolution Fourier-transform ion cyclotron resonance mass analyzers, top-down proteomics aims to increase throughput for whole proteome analysis while preserving the inherent value of an intact protein mass measurement. This years’ American Society for Mass Spectrometry Sanibel meeting brought together established scientists who have demonstrated the viability of the top-down approach and its applicability to virtually all segments of the proteome, mixing them with researchers from diverse areas and with the common interest of advancing top-down into the high-throughput proteomics mainstream. Advances in instrumentation including the orbitrap analyzer, ionization mechanisms, dissociation strategies and informatics, as well as a wide variety of applications, were discussed in depth, leading to the inescapable conclusion that the future for top-down is bright.     

A hybrid LC-Gel-MS method for proteomics research and its application to protease functional pathway mapping

Two-dimensional (2D) gel electrophoresis is the most common protein separation method in proteomics research. It can provide high resolution and high sensitivity. However, 2D gel methods have several limitations, such as labor-intensive procedures, poor reproducibility, and limited dynamic range of detection. In fact, many investigators have returned to couple the one-dimensional (1D) SDS-PAGE with mass spectrometry for protein identification. The limitation of this approach is the increased protein complexity in each one-dimensional gel band. To overcome this problem and provide reproducible quantitative information, we describe here a 2D method for protein mixture separation using a combination of high performance liquid chromatography (HPLC) and 1D SDS-PAGE. The study shows that the step-gradient fractionation method we have applied provides excellent reproducibility. In addition, high mass accuracy of LC-FTICR-MS can allow more confident protein identifications by high resolution and ultra-high mass measurement accuracy. This approach was applied to comparative proteomics since protein abundance level changes can be easily visualized with side-by-side vertical comparison in one gel. Furthermore, separation of multi-samples in the same gel significantly reduces run-to-run variation, as is shown with differential image gel electrophoresis (DIGE). Finally, this approach readily incorporates immunological methods to normalize relative abundances of multiple samples within a single gel. This paper presents the results of our developments and our initial application of this strategy for mapping protease function of beta amyloid cleaving enzyme (BACE) in biological systems.     

Lipidomic mass spectrometry and its application in neuroscience

Central and peripheral nervous systems are lipid rich tissues. Lipids, in the context of lipid-protein complexes, surround neurons and provide electrical insulation for transmission of signals allowing neurons to remain embedded within a conducting environment. Lipids play a key role in vesicle formation and fusion in synapses. They provide means of rapid signaling, cell motility and migration for astrocytes and other cell types that surround and play supporting roles neurons. Unlike many other signaling molecules, lipids are capable of multiple signaling events based on the different fragments generated from a single precursor during each event. Lipidomics, until recently suffered from two major disadvantages:(1) level of expertise required an overwhelming amount of chemical detail to correctly identify a vast number of different lipids which could be close in their chemical reactivity; and(2) high amount of purified compounds needed by analytical techniques to determine their structures. Advances in mass spectrometry have enabled overcoming these two limitations. Mass spectrometry offers a great degree of simplicity in identification and quantification of lipids directly extracted from complex biological mixtures. Mass spectrometers can be regarded to as mass analyzers. There are those that separate and analyze the product ion fragments in space(spatial) and those which separate product ions in time in the same space(temporal). Databases and standardized instrument parameters have further aided the capabilities of the spatial instruments while recent advances in bioinformatics have made the identification and quantification possible using temporal instruments.     

Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae

Protein kinases are coded by more than 2,000 genes and thus constitute the largest single enzyme family in the human genome. Most cellular processes are in fact regulated by the reversible phosphorylation of proteins on serine, threonine, and tyrosine residues. At least 30% of all proteins are thought to contain covalently bound phosphate. Despite the importance and widespread occurrence of this modification, identification of sites of protein phosphorylation is still a challenge, even when performed on highly purified protein. Reported here is methodology that should make it possible to characterize most, if not all, phosphoproteins from a whole-cell lysate in a single experiment. Proteins are digested with trypsin and the resulting peptides are then converted to methyl esters, enriched for phosphopeptides by immobilized metal-affinity chromatography (IMAC), and analyzed by nanoflow HPLC/electrospray ionization mass spectrometry. More than 1,000 phosphopeptides were detected when the methodology was applied to the analysis of a whole-cell lysate from Saccharomyces cerevisiae. A total of 216 peptide sequences defining 383 sites of phosphorylation were determined. Of these, 60 were singly phosphorylated, 145 doubly phosphorylated, and 11 triply phosphorylated. Comparison with the literature revealed that 18 of these sites were previously identified, including the doubly phosphorylated motif pTXpY derived from the activation loop of two mitogen-activated protein (MAP) kinases. We note that the methodology can easily be extended to display and quantify differential expression of phosphoproteins in two different cell systems, and therefore demonstrates an approach for "phosphoprofiling" as a measure of cellular states.     

Tissue proteomics using chemical immobilization and mass spectrometry

Proteomics analysis is important for characterizing tissues to gain biological and pathological insights, which could lead to the identification of disease-associated proteins for disease diagnostics or targeted therapy. However, tissues are commonly embedded in optimal cutting temperature medium (OCT) or are formalin-fixed and paraffin-embedded (FFPE) in order to maintain tissue morphology for histology evaluation. Although several tissue proteomic analyses have been performed on FFPE tissues using advanced mass spectrometry (MS) technologies, high-throughput proteomic analysis of OCT-embedded tissues has been difficult due to the interference of OCT in the MS analysis. In addition, molecules other than proteins present in tissues further complicate tissue proteomic analysis. Here, we report the development of a method using chemical immobilization of proteins for peptide extraction (CIPPE). In this method, proteins are chemically immobilized onto a solid support; interferences from tissues and OCT embedding are removed by extensive washing of proteins conjugated on the solid support. Peptides are then released from the solid phase by proteolysis, enabling MS analysis. This method was first validated by eliminating OCT interference from a standard protein, human serum albumin, where all of the unique peaks contributed by OCT contamination were eradicated. Finally, this method was applied for the proteomic analysis of frozen and OCT-embedded tissues using iTRAQ (isobaric tag for relative and absolute quantitation) labeling and two-dimensional liquid chromatography tandem mass spectrometry. The data showed reproducible extraction and quantitation of 10,284 proteins from 3996 protein groups and a minimal impact of OCT embedding on the analysis of the global proteome of the stored tissue samples.     

imzML--a common data format for the flexible exchange and processing of mass spectrometry imaging data

The application of mass spectrometry imaging (MS imaging) is rapidly growing with a constantly increasing number of different instrumental systems and software tools. The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software. imzML data is divided in two files which are linked by a universally unique identifier (UUID). Experimental details are stored in an XML file which is based on the HUPO-PSI format mzML. Information is provided in the form of a 'controlled vocabulary' (CV) in order to unequivocally describe the parameters and to avoid redundancy in nomenclature. Mass spectral data are stored in a binary file in order to allow efficient storage. imzML is supported by a growing number of software tools. Users will be no longer limited to proprietary software, but are able to use the processing software best suited for a specific question or application. MS imaging data from different instruments can be converted to imzML and displayed with identical parameters in one software package for easier comparison. All technical details necessary to implement imzML and additional background information is available at www.imzml.org.     

基质辅助激光解吸电离飞行时间质谱技术用于常见益生菌鉴定的初步研究

目的建立基质辅助激光解吸电离飞行时间质谱(MADLI-TOF MS)技术鉴定常见益生菌的实验方法并对MADLI-TOF MS技术的适用性进行初步评价。方法对MADLI-TOF MS技术鉴定常见益生菌过程中各影响因素进行考察,筛选出最佳的实验条件。利用19株供试菌株所得的蛋白指纹图谱对MADLI-TOF MS技术的适用性进行研究。结果建立了MADLI-TOF MS技术鉴定常见益生菌的最佳实验方法。初步证明MADLI-TOF MS技术具备在属、种、亚种以及菌株水平上鉴定常见益生菌的能力。结论建立的实验方法稳定性高、重复性好,可以作为MADLI-TOF MS技术鉴定常见益生菌的参考方法。MADLI-TOF MS技术可以作为常见益生菌鉴定的方法之一。     

A proteomics approach to survey the antigenicity of the influenza virus by mass spectrometry

A proteomics-based approach is described that combines gel electrophoresis and MS in order to identify protein interactions and the nature of the interaction interface with high-sample throughput and sensitivity. Results for protein antigens of the influenza virus have demonstrated that the approach can be successfully employed to detect determinants within the hemagglutinin antigen of two divergent type A forms of the virus in present circulation. The determinants are localised to residues 206-224 following tryptic digestion of the hemagglutinin antigen. Specific peptide-antibody complexes formed after treatment of gel-recovered antigen are shown to be able to be preserved on the MALDI target array as has been previously demonstrated in this laboratory for whole virus. The approach has broad applicability for the analysis of a wide array of protein complexes with identification of the interaction interface in a single step with high-sample throughput and at low sample levels.