Phosphoproteomics in analyzing signaling pathways

A surprisingly fewer than expected number of genes in the human genome suggests that sophistication of its biologic system is, in part, due to complex regulation of protein activities. The activities of most cellular proteins are regulated by post-translational modifications. One of the most important post-translational modifications is reversible protein phosphorylation, which decorates more than 30% of the proteome and regulates signal transduction pathways under normal conditions as well as in disorders such as diabetes, neurodegenerative diseases, autoimmune diseases and several forms of cancers. This review examines the recent developments in mass spectrometry-based methods for phosphoproteome analysis and its applications for the study of signal transduction pathways. The basic principles of non-mass spectrometry-based methods, such as chemical genetics and flow cytometry-based approaches, are also discussed as well as their specific advantages to signaling studies. Finally, signaling pathways are discussed in the light of large-scale protein interaction studies. The proteomic methods addressed in this review are emerging as some of the essential components in systems biology, which seeks to describe signaling networks through integration of diverse types of data and, in the future, to allow computational simulations of complex biologic pathways in health and disease.     

Phosphoproteomics in analyzing signaling pathways

A surprisingly fewer than expected number of genes in the human genome suggests that sophistication of its biologic system is, in part, due to complex regulation of protein activities. The activities of most cellular proteins are regulated by post-translational modifications. One of the most important post-translational modifications is reversible protein phosphorylation, which decorates more than 30% of the proteome and regulates signal transduction pathways under normal conditions as well as in disorders such as diabetes, neurodegenerative diseases, autoimmune diseases and several forms of cancers. This review examines the recent developments in mass spectrometry-based methods for phosphoproteome analysis and its applications for the study of signal transduction pathways. The basic principles of non-mass spectrometry-based methods, such as chemical genetics and flow cytometry-based approaches, are also discussed as well as their specific advantages to signaling studies. Finally, signaling pathways are discussed in the light of large-scale protein interaction studies. The proteomic methods addressed in this review are emerging as some of the essential components in systems biology, which seeks to describe signaling networks through integration of diverse types of data and, in the future, to allow computational simulations of complex biologic pathways in health and disease.     

From proteomics toward systems biology: integration of different types of proteomics data into network models

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.     

Identifying components of protein complexes in C. elegans using co-immunoprecipitation and mass spectrometry

Mass spectrometry-based proteomics is rapidly becoming an essential tool for biologists. One of the most common applications is identifying the components of protein complexes isolated by co-immunoprecipitation. In this review, we discuss the co-immunoprecipitation, mass spectrometry and data analysis techniques that have been used successfully to define protein complexes in C. elegans research. In this discussion, two strategies emerged. One approach is to use stringent biochemical purification methods and attempt to identify a small number of complex components with a high degree of certainty based on MS data. A second approach is to use less stringent purification and identification parameters, and ultimately test a longer list of potential binding partners in biological validation assays. This should provide a useful guide for biologists planning proteomic experiments.     

Mass spectrometry-based immunoassays for the next phase of clinical applications

Recent applications of affinity mass spectrometry into clinical laboratories brought a renewed interest in immunoaffinity mass spectrometry as a more specific affinity method capable of selectively targeting and studying protein biomarkers. In mass spectrometry-based immunoassays, proteins are affinity retrieved from biological samples via surface-immobilized antibodies, and are then detected via mass spectrometric analysis. The assays benefit from dual specificity, which is brought about by the affinity of the antibody and the protein mass readout. The mass spectrometry aspect of the assays enables single-step detection of protein isoforms and their individual quantification. This review offers a comprehensive review of mass spectrometry-based immunoassays, from historical perspectives in the development of the immunoaffinity mass spectrometry, to current applications of the assays in clinical and population proteomic endeavors. Described in more detail are two types of mass spectrometry-based immunoassays, one of which incorporates surface plasmon resonance detection for protein quantification. All mass spectrometry-based immunoassays offer high-throughput targeted protein investigation, with clear implications in clinical research, encompassing biomarker discovery and validation, and in diagnostic settings as the next-generation immunoassays.     

Mass spectrometry-based immunoassays for the next phase of clinical applications

Recent applications of affinity mass spectrometry into clinical laboratories brought a renewed interest in immunoaffinity mass spectrometry as a more specific affinity method capable of selectively targeting and studying protein biomarkers. In mass spectrometry-based immunoassays, proteins are affinity retrieved from biological samples via surface-immobilized antibodies, and are then detected via mass spectrometric analysis. The assays benefit from dual specificity, which is brought about by the affinity of the antibody and the protein mass readout. The mass spectrometry aspect of the assays enables single-step detection of protein isoforms and their individual quantification. This review offers a comprehensive review of mass spectrometry-based immunoassays, from historical perspectives in the development of the immunoaffinity mass spectrometry, to current applications of the assays in clinical and population proteomic endeavors. Described in more detail are two types of mass spectrometry-based immunoassays, one of which incorporates surface plasmon resonance detection for protein quantification. All mass spectrometry-based immunoassays offer high-throughput targeted protein investigation, with clear implications in clinical research, encompassing biomarker discovery and validation, and in diagnostic settings as the next-generation immunoassays.     

Analyzing proteomes and protein function using graphical comparative analysis of tandem mass spectrometry results

Although generating large amounts of proteomic data using tandem mass spectrometry has become routine, there is currently no single set of comprehensive tools for the rigorous analysis of tandem mass spectrometry results given the large variety of possible experimental aims. Currently available applications are typically designed for displaying proteins and posttranslational modifications from the point of view of the mass spectrometrist and are not versatile enough to allow investigators to develop biological models of protein function, protein structure, or cell state. In addition, storage and dissemination of mass spectrometry-based proteomic data are problems facing the scientific community. To address these issues, we have developed a relational database model that efficiently stores and manages large amounts of tandem mass spectrometry results. We have developed an integrated suite of multifunctional analysis software for interpreting, comparing, and displaying these results. Our system, Bioinformatic Graphical Comparative Analysis Tools (BIGCAT), allows sophisticated analysis of tandem mass spectrometry results in a biologically intuitive format and provides a solution to many data storage and dissemination issues.     

Native ion mobility-mass spectrometry and related methods in structural biology

Mass spectrometry-based methods have become increasingly important in structural biology — in particular for large and dynamic, even heterogeneous assemblies of biomolecules. Native electrospray ionization coupled to ion mobility-mass spectrometry provides access to stoichiometry, size and architecture of noncovalent assemblies; while non-native approaches such as covalent labeling and H/D exchange can highlight dynamic details of protein structures and capture intermediate states. In this overview article we will describe these methods and highlight some recent applications for proteins and protein complexes, with particular emphasis on native MS analysis. This article is part of a Special Issue entitled: Mass spectrometry in structural biology.     

Proteomic approaches for cancer epigenetics research

Introduction: Epigenetic dysregulation drives or supports numerous human cancers. The chromatin landscape in cancer cells is often marked by abnormal histone post-translational modification (PTM) patterns and by aberrant assembly and recruitment of protein complexes to specific genomic loci. Mass spectrometry-based proteomic analyses can support the discovery and characterization of both phenomena.

Areas covered: We broadly divide this literature into two parts: ‘modification-centric’ analyses that link histone PTMs to cancer biology; and ‘complex-centric’ analyses that examine protein–protein interactions that occur de novo as a result of oncogenic mutations. We also discuss proteomic studies of oncohistones. We highlight relevant examples, discuss limitations, and speculate about forthcoming innovations regarding each application.

Expert commentary: ‘Modification-centric’ analyses have been used to further understanding of cancer’s histone code and to identify associated therapeutic vulnerabilities. ‘Complex-centric’ analyses have likewise revealed insights into mechanisms of oncogenesis and suggested potential therapeutic targets, particularly in MLL-associated leukemia. Proteomic experiments have also supported some of the pioneering studies of oncohistone-mediated tumorigenesis. Additional applications of proteomics that may benefit cancer epigenetics research include middle-down and top-down histone PTM analysis, chromatin reader profiling, and genomic locus-specific protein identification. In the coming years, proteomic approaches will remain powerful ways to interrogate the biology of cancer.     

Type 1 diabetes: entering the proteomic era

During the last decade, a major breakthrough in the field of proteomics has been achieved. This review describes available techniques for proteomic analyses, both gel and non-gel based, particularly concentrating on relative quantification techniques. The principle of the different techniques is discussed, highlighting the advantages and drawbacks of recently available visualization methods in gel-based assays. In addition, recent developments for quantitative analysis in non-gel-based approaches are summarized. This review focuses on applications in Type 1 diabetes. These mainly include proteomic studies on pancreatic islets in animal models and in the human situation. Also discussed are mass spectrometry-based studies on T-cells, and studies on the development of diagnostic markers for diabetic nephropathology by capillary electrophoresis coupled to mass spectrometry.     

Type 1 diabetes: entering the proteomic era

During the last decade, a major breakthrough in the field of proteomics has been achieved. This review describes available techniques for proteomic analyses, both gel and non-gel based, particularly concentrating on relative quantification techniques. The principle of the different techniques is discussed, highlighting the advantages and drawbacks of recently available visualization methods in gel-based assays. In addition, recent developments for quantitative analysis in non-gel-based approaches are summarized. This review focuses on applications in Type 1 diabetes. These mainly include proteomic studies on pancreatic islets in animal models and in the human situation. Also discussed are mass spectrometry-based studies on T-cells, and studies on the development of diagnostic markers for diabetic nephropathology by capillary electrophoresis coupled to mass spectrometry.     

Systems approach to explore components and interactions in the presynapse

The application of proteomic techniques to neuroscientific research provides an opportunity for a greater understanding of nervous system structure and function. As increasing amounts of neuroproteomic data become available, it is necessary to formulate methods to integrate these data in a meaningful way to obtain a more comprehensive picture of neuronal subcompartments. Furthermore, computational methods can be used to make biologically relevant predictions from large proteomic data sets. Here, we applied an integrated proteomics and systems biology approach to characterize the presynaptic (PRE) nerve terminal. For this, we carried out proteomic analyses of presynaptically enriched fractions, and generated a PRE literature‐based protein–protein interaction network. We combined these with other proteomic analyses to generate a core list of 117 PRE proteins, and used graph theory‐inspired algorithms to predict 92 additional components and a PRE complex containing 17 proteins. Some of these predictions were validated experimentally, indicating that the computational analyses can identify novel proteins and complexes in a subcellular compartment. We conclude that the combination of techniques (proteomics, data integration, and computational analyses) used in this study are useful in obtaining a comprehensive understanding of functional components, especially low‐abundance entities and/or interactions in the PRE nerve terminal.     

Statistical Application and Challenges in Global Gel-Free Proteomic Analysis by Mass Spectrometry

Global gel-free proteomic analysis by mass spectrometry has been widely used as an important tool for exploring complex biological systems at the whole genome level. Simultaneous analysis of a large number of protein species is a complicated and challenging task. The challenges exist throughout all stages of a global gel-free proteomic analysis: experimental design, peptide/protein identification, data preprocessing and normalization, and inferential analysis. In addition to various efforts to improve the analytical technologies, statistical methodologies have been applied in all stages of proteomic analyses to help extract relevant information efficiently from large proteomic datasets. In this review, we summarize current applications of statistics in several stages of global gel-free proteomic analysis by mass spectrometry. We discuss the challenges associated with the applications of various statistical tools. Whenever possible, we also propose potential solutions on how to improve the data collection and interpretation for mass-spectrometry-based global proteomic analysis using more sophisticated and/or novel statistical approaches.     

Quantitative proteomic analyses of crop seedlings subjected to stress conditions; a commentary

Quantitative proteomics is one of the analytical approaches used to clarify crop responses to stress conditions. Recent remarkable advances in proteomics technologies allow for the identification of a wider range of proteins than was previously possible. Current proteomic methods fall into roughly two categories: gel-based quantification methods, including conventional two-dimensional gel electrophoresis and two-dimensional fluorescence difference gel electrophoresis, and MS-based quantification methods consists of label-based and label-free protein quantification approaches. Although MS-based quantification methods have become mainstream in recent years, gel-based quantification methods are still useful for proteomic analyses. Previous studies examining crop responses to stress conditions reveal that each method has both advantages and disadvantages in regard to protein quantification in comparative proteomic analyses. Furthermore, one proteomics approach cannot be fully substituted by another technique. In this review, we discuss and highlight the basis and applications of quantitative proteomic analysis approaches in crop seedlings in response to flooding and osmotic stress as two environmental stresses.     

Strategies for metagenomic-guided whole-community proteomics of complex microbial environments

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.     

Initial development and validation of a novel extraction method for quantitative mining of the formalin-fixed, paraffin-embedded tissue proteome for biomarker investigations

Annotated formalin-fixed, paraffin-embedded (FFPE) tissue archives constitute a valuable resource for retrospective biomarker discovery. However, proteomic exploration of archival tissue is impeded by extensive formalin-induced covalent cross-linking. Robust methodology enabling proteomic profiling of archival resources is urgently needed. Recent work is beginning to support the feasibility of biomarker discovery in archival tissues, but further developments in extraction methods which are compatible with quantitative approaches are urgently needed. We report a cost-effective extraction methodology permitting quantitative proteomic analyses of small amounts of FFPE tissue for biomarker investigation. This surfactant/heat-based approach results in effective and reproducible protein extraction in FFPE tissue blocks. In combination with a liquid chromatography-mass spectrometry-based label-free quantitative proteomics methodology, the protocol enables the robust representative and quantitative analyses of the archival proteome. Preliminary validation studies in renal cancer tissues have identified typically 250-300 proteins per 500 ng of tissue with 1D LC-MS/MS with comparable extraction in FFPE and fresh frozen tissue blocks and preservation of tumor/normal differential expression patterns (205 proteins, r = 0.682; p < 10(-15)). The initial methodology presented here provides a quantitative approach for assessing the potential suitability of the vast FFPE tissue archives as an alternate resource for biomarker discovery and will allow exploration of methods to increase depth of coverage and investigate the impact of preanalytical factors.