Bottom-up and Shotgun Proteomics to Identify a Comprehensive Cochlear Proteome

Proteomics is a commonly used approach that can provide insights into complex biological systems. The cochlear sensory epithelium contains receptors that transduce the mechanical energy of sound into an electro-chemical energy processed by the peripheral and central nervous systems. Several proteomic techniques have been developed to study the cochlear inner ear, such as two-dimensional difference gel electrophoresis (2D-DIGE), antibody microarray, and mass spectrometry (MS). MS is the most comprehensive and versatile tool in proteomics and in conjunction with separation methods can provide an in-depth proteome of biological samples. Separation methods combined with MS has the ability to enrich protein samples, detect low molecular weight and hydrophobic proteins, and identify low abundant proteins by reducing the proteome dynamic range. Different digestion strategies can be applied to whole lysate or to fractionated protein lysate to enhance peptide and protein sequence coverage. Utilization of different separation techniques, including strong cation exchange (SCX), reversed-phase (RP), and gel-eluted liquid fraction entrapment electrophoresis (GELFrEE) can be applied to reduce sample complexity prior to MS analysis for protein identification.     

Dynamic proteomics in mammalian cells: capabilities and challenges

A long term goal for molecular biologists is to visualize and quantify the levels and localizations of all proteins at the single cell level under endogenous regulation throughout time. Recent advances in protein tagging, microscopy, and image analysis have brought this goal much closer. But how to integrate these techniques to arrive at proteome scale results? Here I review one approach, incorporating random endogenous gene tagging, high-throughput incubated time-lapse microscopy, and automated image analysis, that can provide information on, for example, the accumulation rates of proteins throughout the cell cycle and the variability of protein level expression. Dynamic proteomics has the potential to shed light on many long standing questions and could contribute to challenging undertakings such as following signal transduction in a mammalian cell from input to output.     

Alternative workflows for plant proteomic analysis

High-throughput separations are intrinsic to the detection and analysis of peptides and proteins by mass spectrometry (MS). Together, efficient separation and MS can lead to the identification of thousands of proteins in a sample, cell or tissue and help build proteome maps that can be used to define a cell type or cellular state. Although 2D gels have been successfully used to separate proteins for subsequent MS analysis, alternative separation efficiencies and, consequently deeper results could be obtained with HPLC or other separation techniques that improve throughput. This highlight is aimed toward plant scientists who have special separation needs due to the nature of plant cells and who could benefit from knowing options and requirements for adopting alternative separation protocols. Through the various sample processing and protein separation strategies, plant biologists should be able to improve the quality of their proteomic reference maps and gain new information about the proteins that define plant cells.     

Protein-protein interactions in plants

The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.     

Protein phosphorylation analysis by site-specific arginine-mimic labeling in gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Although recent advances in gel electrophoresis and mass spectrometry have greatly facilitated separation, purification, and identification of proteins, significant challenges remain in relation to phosphoprotein analysis. Here we introduce a powerful method for analysis of protein phosphorylation in which phosphorylation sites are labeled with guanidinoethanethiol (GET) by beta-elimination/Michael addition prior to proteolysis and mass spectrometry (MS) analysis. This technique is especially useful in conjunction with gel-based technology in that all of the processes involved, including GET labeling, washing, and phosphospecific enzymatic hydrolysis, can be carried out in excised gel slices, thereby minimizing sample loss and contamination. The novel GET tag, which has a highly basic guanidine group, increases the peak intensities for the GET-labeled tryptic peptides by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS. In addition, phosphospecific proteolytic cleavage occurs at guanidinoethylcysteine (Gec) residue, which is arginine-mimic formed by GET tagging of phosphorylated serine residues. Thus, GET tagging is especially useful in analysis of long tryptic phosphopeptides. To illustrate the utility of the in-gel GET tagging and digestion approach, we used it to precisely analyze the phosphorylation sites of human glutathione S-transferase P1 (GSTP1), an enzyme involved in phase II metabolism of many carcinogens and anticancer drugs. The in-gel GET tagging/digestion technique significantly enhances the analytical potential of gel electrophoresis/MS in studies of proteome phosphorylation.     

磷蛋白组的研究技术及其进展

真核细胞中蛋白质磷酸化是一个重要事件。真核细胞利用可逆的蛋白磷酸化来控制许多细胞过程包括信号转换、基因表达、细胞周期等。磷蛋白组的研究涉及磷蛋白的分离和鉴定 ,磷酸化残基定位和定量分析。由于蛋白质磷酸化是一个动态过程 ,在细胞中磷蛋白含量低 ,磷酸化位点可变 ,且磷酸肽的质谱信号常常会受到抑制 ,所以磷蛋白的分析存在更多的困难。本文介绍了国内外在磷酸蛋白的分离鉴定及定量分析方面的研究技术以及进展情况。目前 ,质谱仍然是核心的鉴定技术 ,寻找更好富集方法是最大的挑战。定量蛋白组学是对蛋白质的差异表达进行精确的定量分析。目前还不存在一种独立的方法可以完成磷蛋白的分离、鉴定 ,以及磷酸位点的定位和定量分析。随着样品分离技术和相关仪器的发展 ,磷酸蛋白快速、准确、全面分析鉴定将能够实现。     

Recent development of multi-dimensional chromatography strategies in proteome research

As a complementary approach to two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), multi-dimensional chromatography separation methods have been widely applied in all kinds of biological sample investigations. Multi-dimensional liquid chromatography (MDLC) coupled with bio-mass spectrometry (MS) is playing important roles in proteome research due to its high speed, high resolution and high sensitivity. Proteome analysis strategies mainly include bottom-up and top-down approaches which carry out biological sample separation based on peptide and protein levels, respectively. Electrophoretic methods combined with liquid chromatography like IEF-HPLC and HPLC-SDS-PAGE have been successful applied for protein separations. As for MDLC strategy, ion-exchange chromatography (IEX) together with reversed phase liquid chromatography (RPLC) is still a most widely used chromatography in proteome analysis, other chromatographic methods are also frequently used in protein pre-fractionations, while affinity chromatography is usually adopted for specific functional protein analysis. Recent MDLC technologies and applications to variety of proteome analysis have been achieved great development. A digest peptide-based approach as so-called "bottom-up" and intact protein-based approach "top-down" analysis of proteome samples were briefly reviewed in this paper. The diversity of combinations of different chromatography modes to set up MDLC systems was demonstrated and discussed. Novel developments of MDLC techniques such as high-abundance protein depletion and chromatography array were also included in this review.     

Proteome wide screening using peptide affinity capture

MS‐based strategies are key technologies for identifying proteins in proteomic research. Despite significant improvements in recent years efficient fractionation processes of target analytes remain major bottlenecks in MS‐based protein analysis. Immunoaffinity‐based sample fractionation strategies have shown their potential for the enrichment of analyte peptides of interest, but only small numbers of analytes can be quantified in one experiment. The lack of appropriate capture reagents limits the application of immunoaffinity‐based approaches and only biased biomarker discovery approaches are possible. This perspective discusses the current status of immunoaffinity MS‐based approaches and introduces a novel concept that uses group specific anti‐peptide antibodies – Triple X Proteomics Antibodies – for the enrichment of signature peptides. Classes of peptides with identical termini can be fractionated based on TXP immunoaffinity enrichment steps and can subsequently be identified using established tandem MS procedures. Based on bioinformatic algorithms minimal sets of TXP epitopes can be specified, that cover a wide range of given proteome landscapes of one or even several different species. This opens the possibility to use a minimal number of TXP antibodies as a universal toolbox for general immunoaffinity‐based approaches in proteome analysis.     

Comparing SILAC and two-dimensional gel electrophoresis image analysis for profiling urokinase plasminogen activator signaling in ovarian cancer cells

Two-dimensional gel electrophoresis (2-DE) image analysis is conventionally used for comparative proteomics. However, there are a number of technical difficulties associated with 2-DE protein separation that limit the depth of proteome coverage, and the image analysis steps are typically labor-intensive and low-throughput. Recently, mass spectrometry-based quantitation strategies have been described as alternative differential proteome analysis techniques. In this study, we investigated changes in protein expression using an ovarian cancer cell line, OVMZ6, 24 h post-stimulation with the relatively weak agonist, urokinase-type plasminogen activator (uPA). Quantitative protein profiles were obtained by MALDI-TOF/TOF from stable isotope-labeled cells in culture (SILAC), and these results were compared to the quantitative ratios obtained using 2-DE gel image analysis. MALDI-TOF/TOF mass spectrometry showed that differential quantitation using SILAC was highly reproducible (approximately 8% coefficient of variation (CV)), and this variance was considerably lower than that achieved using automated 2-DE image analysis strategies (CV approximately 25%). Both techniques revealed subtle alterations in cellular protein expression following uPA stimulation. However, due to the lower variances associated with the SILAC technique, smaller changes in expression of uPA-inducible proteins could be found with greater certainty.     

Identification and quantification of N-linked glycoproteins using hydrazide chemistry,stable isotope labeling and mass spectrometry

Quantitative proteome profiling using stable isotope protein tagging and automated tandem mass spectrometry (MS/MS) is an emerging technology with great potential for the functional analysis of biological systems and for the detection of clinical diagnostic or prognostic marker proteins. Owing to the enormous complexity of proteomes, their comprehensive analysis is an as-yet-unresolved technical challenge. However, biologically or clinically important information can be obtained if specific, information-rich protein classes, or sub-proteomes, are isolated and analyzed. Glycosylation is the most common post-translational modification. Here we describe a method for the selective isolation, identification and quantification of peptides that contain N-linked carbohydrates. It is based on the conjugation of glycoproteins to a solid support using hydrazide chemistry, stable isotope labeling of glycopeptides and the specific release of formerly N-linked glycosylated peptides via peptide- N-glycosidase F (PNGase F). The recovered peptides are then identified and quantified by MS/MS. We applied the approach to the analysis of plasma membrane proteins and proteins contained in human blood serum.     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

Systematic Evaluation of Immobilized Trypsin‐Based Fast Protein Digestion for Deep and High‐Throughput Bottom‐Up Proteomics

Immobilized trypsin (IM) has been recognized as an alternative to free trypsin (FT) for accelerating protein digestion 30 years ago. However, some questions of IM still need to be answered. How does the solid matrix of IM influence its preference for protein cleavage and how well can IM perform for deep bottom‐up proteomics compared to FT? By analyzing Escherichia coli proteome samples digested with amine or carboxyl functionalized magnetic bead–based IM (IM‐N or IM‐C) or FT, it is observed that IM‐N with the nearly neutral solid matrix, IM‐C with the negatively charged solid matrix, and FT have similar cleavage preference considering the microenvironment surrounding the cleavage sites. IM‐N (15 min) and FT (12 h) both approach 9000 protein identifications (IDs) from a mouse brain proteome. Compared to FT, IM‐N has no bias in the digestion of proteins that are involved in various biological processes, are located in different components of cells, have diverse functions, and are expressed in varying abundance. A high‐throughput bottom‐up proteomics workflow comprising IM‐N‐based rapid protein cleavage and fast CZE‐MS/MS enables the completion of protein sample preparation, CZE‐MS/MS analysis, and data analysis in only 3 h, resulting in 1000 protein IDs from the mouse brain proteome.     

Bottom up proteomics data analysis strategies to explore protein modifications and genomic variants

The quest to understand biological systems requires further attention of the scientific community to the challenges faced in proteomics. In fact the complexity of the proteome reaches uncountable orders of magnitude. This means that significant technical and data‐analytic innovations will be needed for the full understanding of biology. Current state of art MS is probably our best choice for studying protein complexity and exploring new ways to use MS and MS derived data should be given higher priority. We present here a brief overview of visualization and statistical analysis strategies for quantitative peptide values on an individual protein basis. These analysis strategies can help pinpoint protein modifications, splice, and genomic variants of biological relevance. We demonstrate the application of these data analysis strategies using a bottom‐up proteomics dataset obtained in a drug profiling experiment. Furthermore, we have also observed that the presented methods are useful for studying peptide distributions from clinical samples from a large number of individuals. We expect that the presented data analysis strategy will be useful in the future to define functional protein variants in biological model systems and disease studies. Therefore robust software implementing these strategies is urgently needed.     

Gas chromatographic determination and mechanism of formation of D-amino acids occurring in fermented and roasted cocoa beans, cocoa powder, chocolate and cocoa shell

Summary. Pseudomonas sp. strain phDV1, being a phenol degrading bacterium, has been found to utilize phenol as sole carbon source via the meta pathway. Blue native polyacrylamide gel electrophoresis (BN-PAGE) is widely used for the analysis of oligomeric state and molecular mass non-dissociated protein complexes. In this study, a number of proteomic techniques were used to investigate the oligomeric state enzymes involved in the aromatic degradation pathway. In particular, the Pseudomonas sp. strain phDV1 proteome was monitored under two different growth substrate conditions, using glucose or phenol as sole carbon source. The protein complexes map was compared by BN-PAGE after fractionation by sucrose density centrifugation of the cell extracts. Multiple differences were detected. Further, analysis and identification of the subunit composition of these complexes was carried out using MALDI-TOF MS, allowing the identification of 49 proteins. Additionally, functional information regarding protein–protein interactions was assembled, by coupling 2-D BN-PAGE with MALDI-TOF MS. Application of this functional proteomics method resulted in an higher number of the identified proteins.     

Developing the wool proteome

The wool proteome has been largely uncharted due to a lack of database coverage, poor protein extractability and dynamic range issues. Yet, investigating correlations between wool physical properties and protein content, or characterising UV-, heat- or processing-induced protein damage requires the availability of an identifiable and identified proteome.In this study we have achieved unprecedented wool proteome identification through a strategy of comprehensive data acquisition, iterative protein identification/validation and concurrent augmentation of the sequence database. Data acquisition comprised a range of different hyphenated MS techniques including LC–MS/MS, LC–MALDI, 2D-LC–MS/MS and SDS-PAGE LC–MS. Using iterative searching of databases and search result combination using ProteinScape, a systematic expansion of identifiable proteins in the sequence database was achieved. This was followed by extensive validation and rationalisation of the protein identifications. In total, 72 complete and 30 partial ovine-specific protein sequences were added to the database, and 113 wool proteins were identified.Enhanced access to ovine-specific protein identification and characterisation will facilitate all wool fibre protein chemistry and proteomics research.     

Toward the complete yeast mitochondrial proteome: multidimensional separation techniques for mitochondrial proteomics

Proteomic analyses of different subcellular compartments, so-called organellar proteomics, facilitate the understanding of cellular functions on a molecular level. In this work, various orthogonal multidimensional separation techniques both on the protein and on the peptide level are compared with regard to the number of identified proteins as well as the classes of proteins accessible by the respective methodology. The most complete overview was achieved by a combination of such orthogonal techniques as shown by the analysis of the yeast mitochondrial proteome. A total of 851 different proteins (PROMITO dataset) were identified by use of multidimensional LC-MS/MS, 1D-SDS-PAGE combined with nano-LC-MS/MS and 2D-PAGE with subsequent MALDI-mass fingerprinting. Our PROMITO approach identified the 749 proteins, which were found in the largest previous study on the yeast mitochondrial proteome, and additionally 102 proteins including 42 open reading frames with unknown function, providing the basis for a more detailed elucidation of mitochondrial processes. Comparison of the different approaches emphasizes a bias of 2D-PAGE against proteins with very high isoelectric points as well as large and hydrophobic proteins, which can be accessed more appropriately by the other methods. While 2D-PAGE has advantages in the possible separation of protein isoforms and quantitative differential profiling, 1D-SDS-PAGE with nano-LC-MS/MS and multidimensional LC-MS/MS are better suited for efficient protein identification as they are less biased against distinct classes of proteins. Thus, comprehensive proteome analyses can only be realized by a combination of such orthogonal approaches, leading to the largest dataset available for the mitochondrial proteome of yeast.     

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.