Targeted protein-omic methods are bridging the gap between proteomic and hypothesis-driven protein analysis approaches

While proteomic methods have illuminated many areas of biological protein space, many fundamental questions remain with regard to systems-level relationships between mRNAs, proteins and cell behaviors. While mass spectrometric methods offer a panoramic picture of the relative expression and modification of large numbers of proteins, they are neither optimal for the analysis of predefined targets across large numbers of samples nor for assessing differences in proteins between individual cells or cell compartments. Conversely, traditional antibody-based methods are effective at sensitively analyzing small numbers of proteins across small numbers of conditions, and can be used to analyze relative differences in protein abundance and modification between cells and cell compartments. However, traditional antibody-based approaches are not optimal for analyzing large numbers of protein abundances and modifications across many samples. In this article, we will review recent advances in methodologies and philosophies behind several microarray-based, intermediate-level, ‘protein-omic’ methods, including a focus on reverse-phase lysate arrays and micro-western arrays, which have been helpful for bridging gaps between large- and small-scale protein analysis approaches and have provided insight into the roles that protein systems play in several biological processes.     

Challenges facing the development and use of protein chips to analyze the phosphoproteome

Recent advances in analytical methods, particularly in the area of protein microarrays, have brought the field of proteomics to the forefront of biological science. Protein arrays have shown to be useful for the multiplexed analysis of several hundreds of proteins in parallel. While much of the effort has focused on developing methods to identify expressed proteins, the identification of post-translational modifications is equally important for comprehensive proteome characterization. Protein phosphorylation constitutes a major type of post-translational modification that mobilizes a high number of genes, is involved in many crucial cell functions and largely contributes to the complexity of the proteome. One of the major challenges to analyze phosphoproteins using arrays is the availability of specific antibodies. Thus far, this has hampered the development of highly complex phosphoprotein arrays. This review discusses some of the recent progress made in the development of techniques and reagents to quantitatively determine sites of protein phosphorylation.     

Challenges facing the development and use of protein chips to analyze the phosphoproteome

Recent advances in analytical methods, particularly in the area of protein microarrays, have brought the field of proteomics to the forefront of biological science. Protein arrays have shown to be useful for the multiplexed analysis of several hundreds of proteins in parallel. While much of the effort has focused on developing methods to identify expressed proteins, the identification of post-translational modifications is equally important for comprehensive proteome characterization. Protein phosphorylation constitutes a major type of post-translational modification that mobilizes a high number of genes, is involved in many crucial cell functions and largely contriubutes to the complexity of the proteome. One of the major challenges to analyze phosphoproteins using arrays is the availability of specific antibodies. Thus far, this has hampered the development of highly complex phosphoprotein arrays. This review discusses some of the recent progress made in the development of techniques and reagents to quantitatively determine sites of protein phosphorylation.     

Toward quantitative phosphotyrosine profiling in vivo

Tyrosine phosphorylation is a dynamic reversible post-translational modification that regulates many aspects of cell biology. To understand how this modification controls biological function, it is necessary to not only identify the specific sites of phosphorylation, but also to quantify how phosphorylation levels on these sites may be altered under specific physiological conditions. Due to its sensitivity and accuracy, mass spectrometry (MS) has widely been applied to the identification and characterization of phosphotyrosine signaling across biological systems. In this review we highlight the advances in both MS and phosphotyrosine enrichment methods that have been developed to enable the identification of low level tyrosine phosphorylation events. Computational and manual approaches to ensure confident identification of phosphopeptide sequence and determination of phosphorylation site localization are discussed along with methods that have been applied to the relative quantification of large numbers of phosphorylation sites. Finally, we provide an overview of the challenges ahead as we extend these technologies to the characterization of tyrosine phosphorylation signaling in vivo. With these latest developments in analytical and computational techniques, it is now possible to derive biological insight from quantitative MS-based analysis of signaling networks in vitro and in vivo. Application of these approaches to a wide variety of biological systems will define how signal transduction regulates cellular physiology in health and disease.     

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.     

Large-scale functional analysis using peptide or protein arrays

The array format for analyzing peptide and protein function offers an attractive experimental alternative to traditional library screens. Powerful new approaches have recently been described, ranging from synthetic peptide arrays to whole proteins expressed in living cells. Comprehensive sets of purified peptides and proteins permit high-throughput screening for discrete biochemical properties, whereas formats involving living cells facilitate large-scale genetic screening for novel biological activities. In the past year, three major genome-scale studies using yeast as a model organism have investigated different aspects of protein function, including biochemical activities, gene disruption phenotypes, and protein-protein interactions. Such studies show that protein arrays can be used to examine in parallel the functions of thousands of proteins previously known only by their DNA sequence.     

Geometrical distortions in two-dimensional gels: applicable correction methods

Two-dimensional electrophoresis (2-DE) provides a rapid means for separating thousands of proteins from cell and tissue samples in one run. Although this powerful research tool has been enthusiastically applied in many fields of biomedical research, accurate analysis and interpretation of the data have provided many challenges. Several analysis steps are needed to convert the large amount of noisy data obtained with 2-DE into reliable and interpretable biological information. The goals of such analysis steps include accurate protein detection and quantification, as well as the identification of differentially expressed proteins between samples run on different gels. To achieve these goals, systematic errors such as geometric distortions between the gels must be corrected by using computer-assisted methods. A wide range of computer software has been developed, but no general consensus exists as standard for 2-DE data analysis protocol. The choice of analysis approach is an important element depending both on the data and on the goals of the experiment. Therefore, basic understanding of the algorithms behind the software is required for optimal results. This review highlights some of the common themes in 2-DE data analysis, including protein spot detection and geometric image warping using both spot- and pixel-based approaches. Several computational strategies are overviewed and their relative merits and potential pitfalls discussed. Finally, we offer our own personal view of future trends and developments in large-scale proteome research.     

Increasing the sensitivity of reverse phase protein arrays by antibody-mediated signal amplification

Background  

Reverse phase protein arrays (RPPA) emerged as a useful experimental platform to analyze biological samples in a high-throughput format. Different signal detection methods have been described to generate a quantitative readout on RPPA including the use of fluorescently labeled antibodies. Increasing the sensitivity of RPPA approaches is important since many signaling proteins or posttranslational modifications are present at a low level.     

Chemical strategies for functional proteomics

With complete genome sequences now available for several prokaryotic and eukaryotic organisms, biological researchers are charged with the task of assigning molecular and cellular functions to thousands of predicted gene products. To address this problem, the field of proteomics seeks to develop and apply methods for the global analysis of protein expression and protein function. Here we review a promising new class of proteomic strategies that utilizes synthetic chemistry to create tools and assays for the characterization of protein samples of high complexity. These approaches include the development of chemical affinity tags to measure the relative expression level and post-translational modification state of proteins in cell and tissue proteomes. Additionally, we discuss the emerging field of activity-based protein profiling, which aims to synthesize and apply small molecule probes that monitor dynamics in protein function in complex proteomes.     

A Conserved Mammalian Protein Interaction Network

Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.     

Structural genomics—Impact on biomedicine and drug discovery

The field of structural genomics emerged as one of many 'omics disciplines more than a decade ago, and a multitude of large scale initiatives have been launched across the world. Development and implementation of methods for high-throughput structural biology represents a common denominator among different structural genomics programs. From another perspective a distinction between “biology-driven” versus “structure-driven” approaches can be made. This review outlines the general themes of structural genomics, its achievements and its impact on biomedicine and drug discovery. The growing number of high resolution structures of known and potential drug target proteins is expected to have tremendous value for future drug discovery programs. Moreover, the availability of large numbers of purified proteins enables generation of tool reagents, such as chemical probes and antibodies, to further explore protein function in the cell.     

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.     

Prospects for drug screening using the reverse two-hybrid system.

Rational drug-screening strategies have been limited by the number of available protein targets. The fields of genomics and functional genomics are now merging into 'chemical genomics' approaches, in which large numbers of potential target proteins can be used in standardized high-throughput drug-screening assays. Because protein-protein interactions are critical to most biological processes and can be tested in standardized assays, they may represent optimal targets in the chemical-genomics era. The reverse two-hybrid system appears to have several properties that would be critical for the success of this approach.     

Antibody microarrays for label-free cell-based applications

The recent advances in microtechnologies have shown the interest of developing microarrays dedicated to cell analysis. In this way, miniaturized cell analyzing platforms use several detection techniques requiring specific solid supports for microarray read-out (colorimetric, fluorescent, electrochemical, acoustic, optical…). Real-time and label-free techniques, such as Surface Plasmon Resonance imaging (SPRi), arouse increasing interest for applications in miniaturized formats. Thus, we focused our study on chemical methods for antibody-based microarray fabrication dedicated to the SPRi analysis of cells or cellular activity. Three different approaches were designed and developed for specific applications. In the first case, a polypyrrole-based chemistry was used to array antibody-microarray for specific capture of whole living cells. In the second case, the polypyrrole-based chemistry was complexified in a three molecular level assembly using DNA and antibody conjugates to allow the specific release of cells after their capture. Finally, in the third case, a thiol-based chemistry was developed for long incubation times of biological samples of high complexity. This last approach was focused on the simultaneous study of both cell type characterization and secretory activity (detection of proteins secreted by cells). This paper describes three original methods allowing a rapid and efficient analysis of cellular sample on-chip using immunoaffinity-based assays.     

Current progress in innovative engineered antibodies

As of May 1, 2017, 74 antibody-based molecules have been approved by a regulatory authority in a major market. Additionally, there are 70 and 575 antibodybased molecules in phase III and phase I/II clinical trials, respectively. These total 719 antibody-based clinical stage molecules include 493 naked IgGs, 87 antibodydrug conjugates, 61 bispecific antibodies, 37 total Fc fusion proteins, 17 radioimmunoglobulins, 13 antibody fragments, and 11 immunocytokines. New uses for these antibodies are being discovered each year. For oncology, many of the exciting new approaches involve antibody modulation of T-cells. There are over 80 antibodies in clinical trials targeting T cell checkpoints, 26 T-cellredirected bispecific antibodies, and 145 chimeric antigen receptor (CAR) cell-based candidates (all currently in phase I or II clinical trials), totaling more than 250 T cell interacting clinical stage antibody-based candidates. Finally, significant progress has been made recently on routes of delivery, including delivery of proteins across the blood-brain barrier, oral delivery to the gut, delivery to the cellular cytosol, and gene- and viral-based delivery of antibodies. Thus, there are currently at least 864 antibody-based clinical stage molecules or cells, with incredible diversity in how they are constructed and what activities they impart. These are followed by a next wave of novel molecules, approaches, and new methods and routes of delivery, demonstrating that the field of antibody-based biologics is very innovative and diverse in its approaches to fulfill their promise to treat unmet medical needs.     

Peptide arrays for kinome analysis: new opportunities and remaining challenges

Phosphorylation is the predominant mechanism of post-translational modification for regulation of protein function. With central roles in virtually every cellular process, and strong linkages with many diseases, there is a considerable interest in defining, and ultimately controlling, kinase activities. Investigations of human cellular phosphorylation events, which includes over 500 different kinases and tens of thousands of phosphorylation targets, represent a daunting challenge for proteomic researchers and cell biologists alike. As such, there is a priority to develop tools that enable the evaluation of cellular phosphorylation events in a high-throughput, and biologically relevant, fashion. Towards this objective, two distinct, but functionally related, experimental approaches have emerged; phosphoproteome investigations, which focus on the sub-population of proteins which undergo phosphorylation and kinome analysis, which considers the activities of the kinase enzymes mediating these phosphorylation events. Within kinome analysis, peptide arrays have demonstrated considerable potential as a cost-effective, high-throughput approach for defining phosphorylation-mediated signal transduction activity. In particular, a number of recent advances in the application of peptide arrays for kinome analysis have enabled researchers to tackle increasingly complex biological problems in a wider range of species. In this review, recent advances in kinomic analysis utilizing peptides arrays including several of the biological questions studied by our group, as well as outstanding challenges still facing this technology, are discussed.     

Simultaneous detection of multiple cytokines from conditioned media and patient's sera by an antibody-based protein array system.

We have developed a novel technique for high-throughput simultaneous screening of multiple cytokine expression based on a protein array system. Our method has the advantage of showing the specificity of enzyme-linked immunosorbent assays, sensitivity of enhanced chemiluminescence (ECL), and high-throughput of microspot. In this system, the cytokine array membranes were created by spotting capture antibodies onto the membranes. The membranes were then incubated with biological samples such as conditioned media and patient's sera. The bound proteins were then recognized by biotin-conjugated antibodies and detected by horseradish peroxidase-conjugated streptavidin coupled with ECL. Experiments demonstrated that 24 cytokines from conditioned media and patient's sera could be simultaneously detected using this new approach. This methodology should allow us to develop many high-density protein array systems to detect a variety of proteins. To validate and quantitate the expression of key molecules in a wide range of samples, we have developed conditioned medium arrays to evaluate hundreds and even thousands of samples from individual cells and patients in a single microarray. The combinations of protein arrays and conditioned medium arrays or serum arrays will provide a powerful tool to identify the protein expression profiles and rapidly validate their expression in many types and numbers of samples.     

Non-parametric quantification of protein lysate arrays

MOTIVATION: Proteins play a crucial role in biological activity, so much can be learned from measuring protein expression and post-translational modification quantitatively. The reverse-phase protein lysate arrays allow us to quantify the relative expression levels of a protein in many different cellular samples simultaneously. Existing approaches to quantify protein arrays use parametric response curves fit to dilution series data. The results can be biased when the parametric function does not fit the data. RESULTS: We propose a non-parametric approach which adapts to any monotone response curve. The non-parametric approach is shown to be promising via both simulation and real data studies; it reduces the bias due to model misspecification and protects against outliers in the data. The non-parametric approach enables more reliable quantification of protein lysate arrays. AVAILABILITY: Code to implement the proposed method in the statistical package R is available at: http://odin.mdacc.tmc.edu/jhu/lysatearray-analysis/