Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.     

Gel-free mass spectrometry-based high throughput proteomics: tools for studying biological response of proteins and proteomes

Revolutionary advances in biological mass spectrometry (MS) have provided a basic tool to make possible comprehensive proteomic analysis. Traditionally, two-dimensional gel electrophoresis has been used as a separation method coupled with MS to facilitate analysis of complex protein mixtures. Despite the utility of this method, the many challenges of comprehensive proteomic analysis has motivated the development of gel-free MS-based strategies to obtain information not accessible using two-dimensional gel separations. These advanced strategies have enabled researchers to dig deeper into complex proteomes, gaining insights into the composition, quantitative response, covalent modifications and macromolecular interactions of proteins that collectively drive cellular function. This review describes the current state of gel-free, high throughput proteomic strategies using MS, including (i) the separation approaches commonly used for complex mixture analysis; (ii) strategies for large-scale quantitative analysis; (iii) analysis of post-translational modifications; and (iv) recent advances and future directions. The use of these strategies to make new discoveries at the proteome level into the effects of disease or other cellular perturbations is discussed in a variety of contexts, providing information on the potential of these tools in electromagnetic field research.     

Multidimensional protein identification technology: current status and future prospects

Protein profiling using high-throughput tandem mass spectrometry has become a powerful method for analyzing changes in global protein expression patterns in cells and tissues as a function of developmental, physiologic and disease processes. This review summarizes the utility and practical application of multidimensional protein identification technology as a platform for comprehensive proteomic profiling of complex biologic samples. The strengths and potential problems and limitations associated with this powerful technology are discussed, with an emphasis placed on one of the biggest challenges currently facing large-scale expression profiling projects – namely, data analysis. Complementary bioinformatic computational data mining strategies, such as clustering, functional annotation and statistical inference, are also discussed as these are increasingly necessary for interpreting the results of global proteomic profiling studies.     

Highlights on the capacities of "Gel-based" proteomics

Gel-based proteomic is the most popular and versatile method of global protein separation and quantification. This is a mature approach to screen the protein expression at the large scale, and a cheaper approach as compared with gel-free proteomics. Based on two independent biochemical characteristics of proteins, two-dimensional electrophoresis combines isoelectric focusing, which separates proteins according to their isoelectric point, and SDS-PAGE, which separates them further according to their molecular mass. The next typical steps of the flow of gel-based proteomics are spots visualization and evaluation, expression analysis and finally protein identification by mass spectrometry. For the study of differentially expressed proteins, two-dimensional electrophoresis allows simultaneously to detect, quantify and compare up to thousand protein spots isoforms, including post-translational modifications, in the same gel and in a wide range of biological systems. In this review article, the limits, benefits, and perspectives of gel-based proteomic approaches are discussed using concrete examples.     

Multidimensional protein identification technology: current status and future prospects

Protein profiling using high-throughput tandem mass spectrometry has become a powerful method for analyzing changes in global protein expression patterns in cells and tissues as a function of developmental, physiologic and disease processes. This review summarizes the utility and practical application of multidimensional protein identification technology as a platform for comprehensive proteomic profiling of complex biologic samples. The strengths and potential problems and limitations associated with this powerful technology are discussed, with an emphasis placed on one of the biggest challenges currently facing large-scale expression profiling projects -- namely, data analysis. Complementary bioinformatic computational data mining strategies, such as clustering, functional annotation and statistical inference, are also discussed as these are increasingly necessary for interpreting the results of global proteomic profiling studies.     

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.     

Comparison of ultrafiltration units for proteomic and N-glycoproteomic analysis by the filter-aided sample preparation method

The filter-aided sample preparation (FASP) method allows gel-free processing of biological samples solubilized with detergents for proteomic analysis by mass spectrometry. In FASP detergents are removed by ultrafiltration, and after protein digestion peptides are separated from undigested material. Here we compare the effectiveness of different filtration devices for analysis of proteomes and glycoproteomes. We show that Microcon and Vivacon filtration units with nominal molecular weight cutoffs of 30,000 and 50,000 (30 and 50 k, respectively) are equally suitable for FASP, whereas Microcon 30 k units are most appropriate for mapping of N-glycosylation sites. The use of filters with these relatively large cutoffs facilitates depletion of detergents.     

Targeted proteome investigation via selected reaction monitoring mass spectrometry

Due to the enormous complexity of proteomes which constitute the entirety of protein species expressed by a certain cell or tissue, proteome-wide studies performed in discovery mode are still limited in their ability to reproducibly identify and quantify all proteins present in complex biological samples. Therefore, the targeted analysis of informative subsets of the proteome has been beneficial to generate reproducible data sets across multiple samples. Here we review the repertoire of antibody- and mass spectrometry (MS) -based analytical tools which is currently available for the directed analysis of predefined sets of proteins. The topics of emphasis for this review are Selected Reaction Monitoring (SRM) mass spectrometry, emerging tools to control error rates in targeted proteomic experiments, and some representative examples of applications. The ability to cost- and time-efficiently generate specific and quantitative assays for large numbers of proteins and posttranslational modifications has the potential to greatly expand the range of targeted proteomic coverage in biological studies. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.     

Proteome analysis of Escherichia coli using high-performance liquid chromatography and Fourier transform ion cyclotron resonance mass spectrometry

The basic problem of complexity poses a significant challenge for proteomic studies. To date two-dimensional gel electrophoresis (2-DE) followed by enzymatic in-gel digestion of the peptides, and subsequent identification by mass spectrometry (MS) is the most commonly used method to analyze complex protein mixtures. However, 2-DE is a slow and labor-intensive technique, which is not able to resolve all proteins of a proteome. To overcome these limitations gel-free approaches are developed based on high performance liquid chromatography (HPLC) and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The high resolution and excellent mass accuracy of FT-ICR MS provides a basis for simultaneous analysis of numerous compounds. In the present study, a small protein subfraction of an Escherichia coli cell lysate was prepared by size-exclusion chromatography and proteins were analyzed using C4 reversed phase (RP)-HPLC for pre-separation followed by C18 RP nanoHPLC/nanoESI FT-ICR MS for analysis of the peptide mixtures after tryptic digestion of the protein fractions. We identified 231 proteins and thus demonstrated that a combination of two RP separation steps - one on the protein and one on the peptide level - in combination with high-resolution FT-ICR MS has the potential to become a powerful method for global proteomics studies.     

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.     

Two-dimensional mass spectrometric mapping

The identification and relative quantification of proteins in closely related biological samples is the backbone for many investigations in systems biology and for the discovery of biomarkers. While two-dimensional gel-based methodologies are still widely used for comparative proteomic studies, the recent advent of gel-free methodologies may allow the analysis of a larger number of samples in an automated fashion. Most of the technologies presented in this review require a chemical modification of proteins before analysis, and rely on the relative intensities of mass spectrometry signals for protein quantification. In particular, two-dimensional mass spectrometric mapping methodologies provide a visual representation of mass spectrometric data, thus facilitating the identification of differences in relative protein abundance.     

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape.     

Deconvolution of targeted protein-protein interaction maps

Current proteomic techniques allow researchers to analyze chosen biological pathways or an ensemble of related protein complexes at a global level via the measure of physical protein-protein interactions by affinity purification mass spectrometry (AP-MS). Such experiments yield information-rich but complex interaction maps whose unbiased interpretation is challenging. Guided by current knowledge on the modular structure of protein complexes, we propose a novel statistical approach, named BI-MAP, complemented by software tools and a visual grammar to present the inferred modules. We show that the BI-MAP tools can be applied from small and very detailed maps to large, sparse, and much noisier data sets. The BI-MAP tool implementation and test data are made freely available.     

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis,and the application of statistical tools for data analysis and interpretation

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.