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/.     

基于质谱的植物蛋白质组学研究方法

基于质谱的植物蛋白质组学研究方法,从定性和定量蛋白质组学两个方向进行了归纳总结,并对近年来出现的靶向蛋白质组学、DIA/SWATH技术、化学蛋白质组学,以及多组学联合分析等蛋白质组学研究的新技术、新方法和新应用进行了综述。     

Absolute quantification strategies in proteomics based on mass spectrometry

The strong need for quantitative information in proteomics has fueled the development of mass spectrometry-based analytical methods that are able to determine protein abundances. This article reviews mass spectrometry experiments aimed at providing an absolute quantification of proteins. The experiments make use of the isotope-dilution concept by spiking a known amount of synthetic, isotope-labeled reference peptide into the analyte sample. Quantification is achieved by comparing the mass spectrometry signal intensities of the reference with an endogenous peptide that is generated upon proteolytic cleavage of the target protein. In an analogous manner, the level of post-translational modification at a distinct residue within a target protein can be determined. Among the strengths of absolute quantification are low detection limits reaching subfemtomole levels, a high dynamic range spanning approximately five orders of magnitude, low requirements for sample clean-up, and a fast and straightforward method development. Recent studies have demonstrated the compatibility of absolute quantification with various mass spectrometry readout techniques and sample purification steps such as 1D gel electrophoresis, size-exclusion chromatography, isoelectric peptide focusing, strong cation exchange and reversed phase or affinity chromatography. Under ideal conditions, quantification errors and coefficients of variation below 5% have been reported. However, the fact that at the start of the experiment the analyte is a protein and the internal standard is a peptide, severe quantification errors may result due to the selection of unsuitable reference peptides and/or imperfect protein proteolysis. Within the ensemble of mass spectrometry-based quantification methods, absolute quantification is the method of choice in cases where absolute numbers, many repetitive experiments or precise levels of post-translational modifications are required for a few, preselected species of interest. Consequently, prominent application areas include biomarker quantification, the study of post-translational modifications such as phosphorylation or ubiquitination and the comparison of concentrations of interacting proteins.     

Absolute quantification strategies in proteomics based on mass spectrometry

The strong need for quantitative information in proteomics has fueled the development of mass spectrometry-based analytical methods that are able to determine protein abundances. This article reviews mass spectrometry experiments aimed at providing an absolute quantification of proteins. The experiments make use of the isotope-dilution concept by spiking a known amount of synthetic, isotope-labeled reference peptide into the analyte sample. Quantification is achieved by comparing the mass spectrometry signal intensities of the reference with an endogenous peptide that is generated upon proteolytic cleavage of the target protein. In an analogous manner, the level of post-translational modification at a distinct residue within a target protein can be determined. Among the strengths of absolute quantification are low detection limits reaching subfemtomole levels, a high dynamic range spanning approximately five orders of magnitude, low requirements for sample clean-up, and a fast and straightforward method development. Recent studies have demonstrated the compatibility of absolute quantification with various mass spectrometry readout techniques and sample purification steps such as 1D gel electrophoresis, size-exclusion chromatography, isoelectric peptide focusing, strong cation exchange and reversed phase or affinity chromatography. Under ideal conditions, quantification errors and coefficients of variation below 5% have been reported. However, the fact that at the start of the experiment the analyte is a protein and the internal standard is a peptide, severe quantification errors may result due to the selection of unsuitable reference peptides and/or imperfect protein proteolysis. Within the ensemble of mass spectrometry-based quantification methods, absolute quantification is the method of choice in cases where absolute numbers, many repetitive experiments or precise levels of post-translational modifications are required for a few, preselected species of interest. Consequently, prominent application areas include biomarker quantification, the study of post-translational modifications such as phosphorylation or ubiquitination and the comparison of concentrations of interacting proteins.     

Trends in mass spectrometry instrumentation for proteomics

Mass spectrometry has become a primary tool for proteomics because of its capabilities for rapid and sensitive protein identification and quantitation. It is now possible to identify thousands of proteins from microgram sample quantities in a single day and to quantify relative protein abundances. However, the need for increased capabilities for proteome measurements is immense and is now driving both new strategies and instrument advances. These developments include those based on integration with multi-dimensional liquid separations and high accuracy mass measurements and promise more than order of magnitude improvements in sensitivity, dynamic range and throughput for proteomic analyses in the near future.     

Quantitative proteomics using mass spectrometry

The use of stable isotopes as internal standards in mass spectrometry has opened a new era for quantitative proteomics. Depending on the point at which the label is introduced, most procedures can be classified as in vivo labeling, in vitro pre-digestion labeling or in vitro post-digestion labeling. In vivo labeling has been used for cells that can be grown in culture and has the advantage of being more accurate. The pre-digestion and post-digestion labeling procedures are suitable for all types of sample including human body fluids and biopsies. Several new mass spectrometric strategies mark significant achievements in determining relative protein concentrations and in quantifying post-translational modifications. However, further technology developments are needed for understanding the complexity of a dynamic system like the proteome.     

Utilization and evaluation of CHO-specific sequence databases for mass spectrometry based proteomics

Recently released sequence information on Chinese hamster ovary (CHO) cells promises to not only facilitate our understanding of these industrially important cell factories through direct analysis of the sequence, but also to enhance existing methodologies and allow new tools to be developed. In this article we demonstrate the utilization of CHO specific sequence information to improve mass spectrometry (MS) based proteomic identification. The use of various CHO specific databases enabled the identification of 282 additional proteins, thus increasing the total number of identified proteins by 40-50%, depending on the sample source and methods used. In addition, a considerable portion of those proteins that were identified previously based on inter-species sequence homology were now identified by a larger number of peptides matched, thus increasing the confidence of identification. The new sequence information offers improved interpretation of proteomic analyses and will, in the years to come, prove vital to unraveling the CHO proteome.     

Decision tree-driven tandem mass spectrometry for shotgun proteomics

Mass spectrometry has become a key technology for modern large-scale protein sequencing. Tandem mass spectrometry, the process of peptide ion dissociation followed by mass-to-charge ratio (m/z) analysis, is the critical component for peptide identification. Recent advances in mass spectrometry now permit two discrete, and complementary, types of peptide ion fragmentation: collision-activated dissociation (CAD) and electron transfer dissociation (ETD) on a single instrument. To exploit this complementarity and increase sequencing success rates, we designed and embedded a data-dependent decision tree algorithm (DT) to make unsupervised, real-time decisions of which fragmentation method to use based on precursor charge and m/z. Applying the DT to large-scale proteome analyses of Saccharomyces cerevisiae and human embryonic stem cells, we identified 53,055 peptides in total, which was greater than by using CAD (38,293) or ETD (39,507) alone. In addition, the DT method also identified 7,422 phosphopeptides, compared to either 2,801 (CAD) or 5,874 (ETD) phosphopeptides.     

Charge state estimation for tandem mass spectrometry proteomics

High-throughput protein analysis by tandem mass spectrometry produces anywhere from thousands to millions of spectra that are being used for peptide and protein identifications. Though each spectrum corresponds only to one charged peptide (ion) state, repetitive database searches of multiple charge states are typically conducted since the resolution of many common mass spectrometers is not sufficient to determine the charge state. The resulting database searches are both error-prone and time-consuming. We describe a straightforward, accurate approach on charge state estimation (CHASTE). CHASTE relies on fragment ion peak distributions, and by using reliable logistic regression models, combines different measurements to improve its accuracy. CHASTE's performance has been validated on data sets, comprised of known peptide dissociation spectra, obtained by replicate analyses of our earlier developed protein standard mixture using ion trap mass spectrometers at different laboratories. CHASTE was able to reduce number of needed database searches by at least 60% and the number of redundant searches by at least 90% virtually without any informational loss. This greatly alleviates one of the major bottlenecks in high throughput peptide and protein identifications. Thresholds and parameter estimates can be tailored to specific analysis situations, pipelines, and instrumentations. CHASTE was implemented in Java GUI-based and command-line-based interfaces.     

Optimized peptide separation and identification for mass spectrometry based proteomics via free-flow electrophoresis

Multidimensional LC-MS based shotgun proteomics experiments at the peptide level have traditionally been carried out by ion exchange in the first dimension and reversed-phase liquid chromatography in the second. Recently, it has been shown that isoelectric focusing (IEF) is an interesting alternative approach to ion exchange separation of peptides in the first dimension. Here we present an improved protocol for peptide separation by continuous free-flow electrophoresis (FFE) as the first dimension in a two-dimensional peptide separation work flow. By the use of a flat pI gradient and a mannitol and urea based separation media we were able to perform high-throughput proteome analysis with improved interfacing between FFE and RPLC-MS/MS. The developed protocol was applied to a cytosolic fraction from Schneider S2 cells from Drosophila melanogaster, resulting in the identification of more than 10,000 unique peptides with high probability. To improve the accuracy of the peptide identification following FFE-IEF we incorporated the pI information as an additional parameter into a statistical model for discrimination between correct and incorrect peptide assignments to MS/MS spectra.     

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。     

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.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

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.     

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.     

Spectral quality assessment for high-throughput tandem mass spectrometry proteomics

Current techniques in tandem mass spectrometric analyses of cellular protein contents often produce thousands to tens of thousands of spectra per experiment. This study introduces a new algorithm, named SPEQUAL, which is aimed at automated tandem mass spectral quality assessment. The quality of a given spectrum can be evaluated from three basic components: (i) charge state differentiation, (ii) total signal intensity, and (iii) signal-to-noise estimates. The differentiation between single and multiple precursor charge states (i) provides a binary score for a given spectrum. Components (ii) and (iii) provide partial scores which are subsequently summarized and multiplied by the first score. SPEQUAL was applied to over 10,000 data files derived from almost 3,000 tandem mass spectra, and the results (final cumulative scores) were manually verified. SPEQUAL's performance was determined to have high sensitivity and specificity and low error rates for both spectral quality estimates in general and precursor charge state differentiation in particular. Each of the partial scores is controlled by adjustable thresholds to fine-tune SPEQUAL's performance for different analysis pipelines and instrumentation. This spectral quality assessment tool is intended to act in an advisory role to the researcher, assisting in filtration of thousands of spectra typically produced by high throughput tandem mass spectrometric proteome analyses. Lastly, SPEQUAL was implemented as Java GUI-based and command-line-based interfaces freely available for both academic and industrial researchers.     

Clinical proteomics and mass spectrometry profiling for cancer detection

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot?, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.     

Performance of a genetic algorithm for mass spectrometry proteomics

Background  

Recently, mass spectrometry data have been mined using a genetic algorithm to produce discriminatory models that distinguish healthy individuals from those with cancer. This algorithm is the basis for claims of 100% sensitivity and specificity in two related publicly available datasets. To date, no detailed attempts have been made to explore the properties of this genetic algorithm within proteomic applications. Here the algorithm's performance on these datasets is evaluated relative to other methods.     

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.