Current methods for global proteome identification

PyViscount: Validating False Discovery Rate Estimation Methods via Random Search Space Partition

Validating false discovery rate (FDR) estimation is an essential but surprisingly understudied aspect of method development in shotgun proteomics. Currently available validation protocols mostly rely on ground truth data sets, which typically involve manipulating the properties of the search space or query spectra used. As a result, comparing estimated FDR and ground truth-based false discovery proportion values may not be representative of the scenarios involving natural data sets encountered in practice. In this study, we introduce PyViscount─a Python tool implementing a novel validation protocol based on random search space partition, which enables generating a quasi ground-truth using unaltered search spaces of unique candidate peptides and generic data sets of experimental query spectra. Furthermore, validation of existing FDR estimation methods by PyViscount is consistent with alternative validation protocols. The presented novel approach to validation free from the need for synthetic data sets or dubious manipulation of the data may be an attractive alternative for proteomics practitioners, allowing them to obtain deeper insights into the performance of existing and new FDR estimation methods.

Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells

To identify proteins by the bottom-up mass spectrometry workflow, enzymatic digestion is essential to break down proteins into smaller peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity and generation of peptides with desirable positively charged N- and C-terminal amino acid residues that are amenable to reverse phase HPLC separation and MS/MS analyses. However, trypsin can yield variable digestion profiles and its protein cleavage activity is interdependent on trypsin source and quality, digestion time and temperature, pH, denaturant, trypsin and substrate concentrations, composition/complexity of the sample matrix, and other factors. There is therefore a need for a more standardized, general-purpose trypsin digestion protocol. Based on a review of the literature we delineate optimal conditions for carrying out trypsin digestions of complex proteomes from bulk samples to limiting amounts of protein extracts. Furthermore, we highlight recent developments and technological advances used in digestion protocols to quantify complex proteomes from single cells. SIGNIFICANCE: Currently, bottom-up MS-based proteomics is the method of choice for global proteome analysis. Since trypsin is the most utilized protease in bottom-up MS proteomics, delineating optimal conditions for carrying out trypsin digestions of complex proteomes in samples ranging from tissues to single cells should positively impact a broad range of biomedical research.

Protein Purification Technologies

Protein Biotechnology is an exciting and fast- growing area of research, with numerous industrial applications. The growing demand for developing efficient and rapid protein purification methods is driving research and growth in this area. Advances and progress in the techniques and methods of protein purification have been such that one can reasonably expect that any protein of a given order of stability may be purified to currently acceptable standards of homogeneity. However, protein manufacturing cost remains extremely high, with downstream processing constituting a substantial proportion of the overall cost. Understanding of the methods and optimization of the experimental conditions have become critical to the manufacturing industry in order to minimize production costs while satisfying the quality as well as all regulatory requirements. New purification processes exploiting specific, effective and robust methods and chromatographic materials are expected to guide the future of the protein purification market.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Emerging proteomic approaches to identify the underlying pathophysiology of neurodevelopmental and neurodegenerative disorders

Proteomics is the large-scale study of the total protein content and their overall function within a cell through multiple facets of research. Advancements in proteomic methods have moved past the simple quantification of proteins to the identification of post-translational modifications (PTMs) and the ability to probe interactions between these proteins, spatially and temporally. Increased sensitivity and resolution of mass spectrometers and sample preparation protocols have drastically reduced the large amount of cells required and the experimental variability that had previously hindered its use in studying human neurological disorders. Proteomics offers a new perspective to study the altered molecular pathways and networks that are associated with autism spectrum disorders (ASD). The differences between the transcriptome and proteome, combined with the various types of post-translation modifications that regulate protein function and localization, highlight a novel level of research that has not been appropriately investigated. In this review, we will discuss strategies using proteomics to study ASD and other neurological disorders, with a focus on how these approaches can be combined with induced pluripotent stem cell (iPSC) studies. Proteomic analysis of iPSC-derived neurons have already been used to measure changes in the proteome caused by patient mutations, analyze changes in PTMs that resulted in altered biological pathways, and identify potential biomarkers. Further advancements in both proteomic techniques and human iPSC differentiation protocols will continue to push the field towards better understanding ASD disease pathophysiology. Proteomics using iPSC-derived neurons from individuals with ASD offers a window for observing the altered proteome, which is necessary in the future development of therapeutics against specific targets.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

Systemic effects of ionizing radiation at the proteome and metabolome levels in the blood of cancer patients treated with radiotherapy: the influence of inflammation and radiation toxicity

PURPOSE

Blood is the most common replacement tissue used to study systemic responses of organisms to different types of pathological conditions and environmental insults. Local irradiation during cancer radiotherapy induces whole body responses that can be observed at the blood proteome and metabolome levels. Hence, comparative blood proteomics and metabolomics are emerging approaches used in the discovery of radiation biomarkers. These techniques enable the simultaneous measurement of hundreds of molecules and the identification of sets of components that can discriminate different physiological states of the human body. Radiation-induced changes are affected by the dose and volume of irradiated tissues; hence, the molecular composition of blood is a hypothetical source of biomarkers for dose assessment and the prediction and monitoring of systemic responses to radiation. This review aims to provide a comprehensive overview on the available evidence regarding molecular responses to ionizing radiation detected at the level of the human blood proteome and metabolome. It focuses on patients exposed to radiation during cancer radiotherapy and emphasizes effects related to radiation-induced toxicity and inflammation.

CONCLUSIONS

Systemic responses to radiation detected at the blood proteome and metabolome levels are primarily related to the intensity of radiation-induced toxicity, including inflammatory responses. Thus, several inflammation-associated molecules can be used to monitor or even predict radiation-induced toxicity. However, these abundant molecular features have a rather limited applicability as universal biomarkers for dose assessment, reflecting the individual predisposition of the immune system and tissue-specific mechanisms involved in radiation-induced damage.

Methods to Calculate Spectrum Similarity

Scoring functions that assess spectrum similarity play a crucial role in many computational mass spectrometry algorithms. These functions are used to compare an experimentally acquired fragmentation (MS/MS) spectrum against two different types of target MS/MS spectra: either against a theoretical MS/MS spectrum derived from a peptide from a sequence database, or against another, previously acquired MS/MS spectrum. The former is typically encountered in database searching, while the latter is used in spectrum clustering and spectral library searching. The comparison between acquired versus theoretical MS/MS spectra is most commonly performed using cross-correlations or probability derived scoring functions, while the comparison of two acquired MS/MS spectra typically makes use of a normalized dot product, especially in spectrum library search algorithms. In addition to these scoring functions, Pearson's or Spearman's correlation coefficients, mean squared error, or median absolute deviation scores can also be used for the same purpose. Here, we describe and evaluate these scoring functions with regards to their ability to assess spectrum similarity for theoretical versus acquired, and acquired versus acquired spectra.

Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics

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Proteomics data sharing moved from validation to re-use.

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New tools and services make data very easily accessible.

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Metadata provision can still benefit from improvements.

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Quality control metrics will soon be reported along with submitted data.

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Data re-use will enable the advent of actual in silico proteomics.

A Simple Workflow for Large Scale Shotgun Glycoproteomics

Targeting subproteomes is a good strategy to decrease the complexity of a sample, for example in body fluid biomarker studies. Glycoproteins are proteins with carbohydrates of varying size and structure attached to the polypeptide chain, and it has been shown that glycosylation plays essential roles in several vital cellular processes, making glycosylation a particularly interesting field of study. Here, we describe a method for the enrichment of glycosylated peptides from trypsin digested proteins in human cerebrospinal fluid. We also describe how to perform the data analysis on the mass spectrometry data for such samples, focusing on site-specific identification of glycosylation sites, using user friendly open source software.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

MZDASoft: a software architecture that enables large-scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

RATIONALE

Without accurate peak linking/alignment, only the expression levels of a small percentage of proteins can be compared across multiple samples in Liquid Chromatography/Mass Spectrometry/Tandem Mass Spectrometry (LC/MS/MS) due to the selective nature of tandem MS peptide identification. This greatly hampers biomedical research that aims at finding biomarkers for disease diagnosis, treatment, and the understanding of disease mechanisms. A recent algorithm, PeakLink, has allowed the accurate linking of LC/MS peaks without tandem MS identifications to their corresponding ones with identifications across multiple samples collected from different instruments, tissues and labs, which greatly enhanced the ability of comparing proteins. However, PeakLink cannot be implemented practically for large numbers of samples based on existing software architectures, because it requires access to peak elution profiles from multiple LC/MS/MS samples simultaneously.

METHODS

We propose a new architecture based on parallel processing, which extracts LC/MS peak features, and saves them in database files to enable the implementation of PeakLink for multiple samples. The software has been deployed in High-Performance Computing (HPC) environments. The core part of the software, MZDASoft Parallel Peak Extractor (PPE), can be downloaded with a user and developer's guide, and it can be run on HPC centers directly. The quantification applications, MZDASoft TandemQuant and MZDASoft PeakLink, are written in Matlab, which are compiled with a Matlab runtime compiler. A sample script that incorporates all necessary processing steps of MZDASoft for LC/MS/MS quantification in a parallel processing environment is available. The project webpage is http://compgenomics.utsa.edu/zgroup/MZDASoft.

RESULTS

The proposed architecture enables the implementation of PeakLink for multiple samples. Significantly more (100%-500%) proteins can be compared over multiple samples with better quantification accuracy in test cases.

CONCLUSION

MZDASoft enables large-scale comparison of protein expression levels over multiple samples with much larger protein comparison coverage and better quantification accuracy. It is an efficient implementation based on parallel processing which can be used to process large amounts of data.

Identification of serum proteome signatures of locally advanced and metastatic gastric cancer: a pilot study

BACKGROUND

The gastric cancer is one of the most common and mortal cancer worldwide. The initial asymptomatic development and further nonspecific symptoms result in diagnosis at the advanced stage with poor prognosis. Yet, no clinically useful biomarkers are available for this malignancy, and invasive gastrointestinal endoscopy remains the only reliable option at the moment. Hence, there is a need for discovery of clinically useful noninvasive diagnostic and/or prognostic tool as an alternative (or complement) for current diagnostic tools. Here we aimed to search for serum proteins characteristic for local and invasive gastric cancer.

METHODS

Pre-treatment blood samples were collected from patients with diagnosed gastric adenocarcinoma at the different stage of disease: 35 patients with locally advanced cancer and 18 patients with metastatic cancer; 50 healthy donors were also included as a control group. The low-molecular-weight fraction of serum proteome (i.e., endogenous peptidome) was profiled by the MALDI-ToF mass spectrometry, and the whole proteome components were identified and quantified by the LC-MS/MS shotgun approach.

RESULTS

Multicomponent peptidome signatures were revealed that allowed good discrimination between healthy controls and cancer patients, as well as between patients with locally advanced and metastatic cancer. Moreover, a LC-MS/MS approach revealed 49 serum proteins with different abundances between healthy donors and cancer patients (predominantly proteins associated with inflammation and acute phase response). Furthermore, 19 serum proteins with different abundances between patients with locally advanced and metastatic cancer were identified (including proteins associated with cytokine/chemokine response and metabolism of nucleic acids). However, neither peptidome profiling nor shotgun proteomics approach allowed detecting serum components discriminating between two subgroups of patients with local disease who either developed or did not develop metastases during follow-up.

CONCLUSIONS

The molecular differences between locally advanced and metastatic gastric cancer, as well as more obvious differences between healthy individuals and cancer patients, have marked reflection at the level of serum proteome. However, we have no evidence that features of pre-treatment serum proteome could predict a risk of cancer dissemination in patients treated due to local disease. Nevertheless, presented data confirmed potential applicability of a serum proteome signature-based biomarker in diagnostics of gastric cancer.

Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines

Quantitative label-free mass spectrometry is increasingly used to analyze the proteomes of complex biological samples. However, the choice of appropriate data analysis methods remains a major challenge. We therefore provide a rigorous comparison between peptide-based models and peptide-summarization-based pipelines. We show that peptide-based models outperform summarization-based pipelines in terms of sensitivity, specificity, accuracy, and precision. We also demonstrate that the predefined FDR cutoffs for the detection of differentially regulated proteins can become problematic when differentially expressed (DE) proteins are highly abundant in one or more samples. Care should therefore be taken when data are interpreted from samples with spiked-in internal controls and from samples that contain a few very highly abundant proteins. We do, however, show that specific diagnostic plots can be used for assessing differentially expressed proteins and the overall quality of the obtained fold change estimates. Finally, our study also illustrates that imputation under the "missing by low abundance" assumption is beneficial for the detection of differential expression in proteins with low abundance, but it negatively affects moderately to highly abundant proteins. Hence, imputation strategies that are commonly implemented in standard proteomics software should be used with care.

Navigating through metaproteomics data: a logbook of database searching

Metaproteomic research involves various computational challenges during the identification of fragmentation spectra acquired from the proteome of a complex microbiome. These issues are manifold and range from the construction of customized sequence databases, the optimal setting of search parameters to limitations in the identification search algorithms themselves. In order to assess the importance of these individual factors, we studied the effect of strategies to combine different search algorithms, explored the influence of chosen database search settings, and investigated the impact of the size of the protein sequence database used for identification. Furthermore, we applied de novo sequencing as a complementary approach to classic database searching. All evaluations were performed on a human intestinal metaproteome dataset. Pyrococcus furiosus proteome data were used to contrast database searching of metaproteomic data to a classic proteomic experiment. Searching against subsets of metaproteome databases and the use of multiple search engines increased the number of identifications. The integration of P. furiosus sequences in a metaproteomic sequence database showcased the limitation of the target-decoy-controlled false discovery rate approach in combination with large sequence databases. The selection of varying search engine parameters and the application of de novo sequencing represented useful methods to increase the reliability of the results. Based on our findings, we provide recommendations for the data analysis that help researchers to establish or improve analysis workflows in metaproteomics.

Viewing the proteome: how to visualize proteomics data?

Proteomics has become one of the main approaches for analyzing and understanding biological systems. Yet similar to other high-throughput analysis methods, the presentation of the large amounts of obtained data in easily interpretable ways remains challenging. In this review, we present an overview of the different ways in which proteomics software supports the visualization and interpretation of proteomics data. The unique challenges and current solutions for visualizing the different aspects of proteomics data, from acquired spectra via protein identification and quantification to pathway analysis, are discussed, and examples of the most useful visualization approaches are highlighted. Finally, we offer our ideas about future directions for proteomics data visualization.

An update on LNCipedia: a database for annotated human lncRNA sequences

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90,000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.

Metalloproteomics: focus on metabolic issues relating to metals

PURPOSE OF REVIEW

By determining metalloproteomes via high-throughput methodology, metalloproteomics provides a research strategy for investigating nutritional and metabolic issues relating to metals. In this review, we examine recent developments in metalloproteomics since its early days approximately 12 years ago, when we utilized metalloproteomics to investigate copper disposition in hepatocytes in relation to Wilson disease.

RECENT FINDINGS

A metalloproteome is the set of proteins that have metal-binding capacity by being metalloproteins or manifesting metal-binding sites. Like all proteomes, a metalloproteome is determined within the context of a well defined system. It can be ascertained for a single metal or multiple metals in that system. Apart from major technological advances in analytical techniques, recent work has examined metalloproteomes for metals other than copper, notably nickel, zinc and manganese. Given the importance of microbiomes to metabolism, microbial metalloproteomics is a rapidly expanding and promising new field.

SUMMARY

Metals play key roles in metabolic processes. Sufficient technological progress has taken place in the past decade to make metalloproteomics an exciting and innovative type of research in nutrition and metabolism. It elucidates how metals contribute to metabolic physiology across the phyla, including in microbes. For humans, it may clarify mechanisms as well as identify informative diagnostic or prognostic biomarkers.

Effects of L-theanine on posttraumatic stress disorder induced changes in rat brain gene expression

Posttraumatic stress disorder (PTSD) is characterized by the occurrence of a traumatic event that is beyond the normal range of human experience. The future of PTSD treatment may specifically target the molecular mechanisms of PTSD. In the US, approximately 20% of adults report taking herbal products to treat medical illnesses. L-theanine is the amino acid in green tea primarily responsible for relaxation effects. No studies have evaluated the potential therapeutic properties of herbal medications on gene expression in PTSD. We evaluated gene expression in PTSD-induced changes in the amygdala and hippocampus of Sprague-Dawley rats. The rats were assigned to PTSD-stressed and nonstressed groups that received either saline, midazolam, L-theanine, or L-theanine + midazolam. Amygdala and hippocampus tissue samples were analyzed for changes in gene expression. One-way ANOVA was used to detect significant difference between groups in the amygdala and hippocampus. Of 88 genes examined, 17 had a large effect size greater than 0.138. Of these, 3 genes in the hippocampus and 5 genes in the amygdala were considered significant (P < 0.05) between the groups. RT-PCR analysis revealed significant changes between groups in several genes implicated in a variety of disorders ranging from PTSD, anxiety, mood disorders, and substance dependence.

Quantitative proteomics in cardiovascular research: global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.

Proteomic differences between microvascular endothelial cells and the EA.hy926 cell line forming three-dimensional structures

Proteomic changes of two types of human endothelial cells (ECs) were determined and compared to morphological alterations occurring during the scaffold-free in vitro formation of 3D structures resembling vascular intimas. The EA.hy926 cell line and human microvascular ECs (HMVECs) were cultured on a random positioning machine or static on ground (normal gravity) for 5 and 7 days, before their morphology was examined and their protein content was analysed by MS after free-flow electrophoretic separation. A total of 1175 types of proteins were found in EA.hy926 cells and 846 in HMVEC forming 3D structures faster than the EA.hy926 cells. Five hundred and eighty-four of these kinds of proteins were present in both types of cells. They included a number of metabolic enzymes, of structure-related and stress proteins. Comparing proteins of EA.hy926 cells growing either adherently on ground or in 3D aggregates on the random positioning machine revealed that ribosomal proteins were enhanced, while tubes are formed and various components of 26S proteasomes remained prevalent in static normal gravity control cells only. The fast developing tube-like 3D structures of HMVEC suggested a transient augmentation of ribosomal proteins during the 3D assembling of ECs.

Protein purification: an overview

Biological macromolecules such as proteins constitute an important class of products in the food, biotechnology, pharmaceutical, and cosmetics industries. The growing need to develop efficient and rapid protein purification methods is driving research and growth in this area. Advances and progress in the methods and techniques of protein purification have been such that one can reasonably expect that any protein of a given order of stability may be purified to currently acceptable standards of homogeneity. However, protein production cost remains extremely high, with downstream processing constituting a substantial proportion of the overall cost. Understanding of the methods and optimization of experimental conditions have become critical to the manufacturing industry in order to minimize production costs while satisfying all regulatory requirements. New purification protocols exploiting specific, effective, and robust methods and materials are expected to guide the future of the protein purification area.

Distributed computing and data storage in proteomics: many hands make light work, and a stronger memory

Modern day proteomics generates ever more complex data, causing the requirements on the storage and processing of such data to outgrow the capacity of most desktop computers. To cope with the increased computational demands, distributed architectures have gained substantial popularity in the recent years. In this review, we provide an overview of the current techniques for distributed computing, along with examples of how the techniques are currently being employed in the field of proteomics. We thus underline the benefits of distributed computing in proteomics, while also pointing out the potential issues and pitfalls involved.

Getting intimate with trypsin, the leading protease in proteomics

Nowadays, mass spectrometry-based proteomics is carried out primarily in a bottom-up fashion, with peptides obtained after proteolytic digest of a whole proteome lysate as the primary analytes instead of the proteins themselves. This experimental setup crucially relies on a protease to digest an abundant and complex protein mixture into a far more complex peptide mixture. Full knowledge of the working mechanism and specificity of the used proteases is therefore crucial, both for the digestion step itself as well as for the downstream identification and quantification of the (fragmentation) mass spectra acquired for the peptides in the mixture. Targeted protein analysis through selected reaction monitoring, a relative newcomer in the specific field of mass spectrometry-based proteomics, even requires a priori understanding of protease behavior for the proteins of interest. Because of the rapidly increasing popularity of proteomics as an analytical tool in the life sciences, there is now a renewed demand for detailed knowledge on trypsin, the workhorse protease in proteomics. This review addresses this need and provides an overview on the structure and working mechanism of trypsin, followed by a critical analysis of its cleavage behavior, typically simply accepted to occur exclusively yet consistently after Arg and Lys, unless they are followed by a Pro. In this context, shortcomings in our ability to understand and predict the behavior of trypsin will be highlighted, along with the downstream implications. Furthermore, an analysis is carried out on the inherent shortcomings of trypsin with regard to whole proteome analysis, and alternative approaches will be presented that can alleviate these issues. Finally, some reflections on the future of trypsin as the workhorse protease in mass spectrometry-based proteomics will be provided.

Free-flow electrophoresis in proteome sample preparation

An aim of proteome research is to identify the entire complement of proteins expressed in defined cell types of humans, animals, plants, and microorganisms. The approach requires searching for low abundant or even rarely expressed proteins in many cell types, as well as the determination of the protein expression levels in subcellular compartments and organelles. In recent years, rather powerful MS technologies have been developed. At this stage of MS device development, it is of highest interest to purify intact cell types or isolate subcellular compartments, where the proteins of interest are originating from, which determine the final composition of a peptide mixture. Free-flow electrophoresis proved to be useful to prepare meaningful peptide mixtures because of its improved capabilities in particle electrophoresis and the enhanced resolution in protein separation. Sample preparation by free-flow electrophoresis mediated particle separation was preferentially performed for purification of either organelles and their subspecies or major protein complexes. Especially, the introduction of isotachophoresis and interval zone electrophoresis improved the purity of the gained analytes of interest. In addition, free-flow IEF proved to be helpful, when proteins of low solubility, obtained, e.g. from cell membranes, were investigated.

Searching for a needle in a stack of needles: challenges in metaproteomics data analysis

In the past years the integral study of microbial communities of varying complexity has gained increasing research interest. Mass spectrometry-driven metaproteomics enables the analysis of such communities on the functional level, but this fledgling field still faces various technical and semantic challenges regarding experimental data analysis and interpretation. In the present review, we outline the hurdles involved and attempt to cover the most valuable methods and software implementations available to researchers in the field today. Beyond merely focusing on protein identification, we provide an overview on different data pre- and post-processing steps, such as metabolic pathway analysis, that can be useful in a typical metaproteomics workflow. Finally, we briefly discuss directions for future work.

Exosomal Proteome Profiling: A Potential Multi-Marker Cellular Phenotyping Tool to Characterize Hypoxia-Induced Radiation Resistance in Breast Cancer

Radiation and drug resistance are significant challenges in the treatment of locally advanced, recurrent and metastatic breast cancer that contribute to mortality. Clinically, radiotherapy requires oxygen to generate cytotoxic free radicals that cause DNA damage and allow that damage to become fixed in the genome rather than repaired. However, approximately 40% of all breast cancers have hypoxic tumor microenvironments that render cancer cells significantly more resistant to irradiation. Hypoxic stimuli trigger changes in the cell death/survival pathway that lead to increased cellular radiation resistance. As a result, the development of noninvasive strategies to assess tumor hypoxia in breast cancer has recently received considerable attention. Exosomes are secreted nanovesicles that have roles in paracrine signaling during breast tumor progression, including tumor-stromal interactions, activation of proliferative pathways and immunosuppression. The recent development of protocols to isolate and purify exosomes, as well as advances in mass spectrometry-based proteomics have facilitated the comprehensive analysis of exosome content and function. Using these tools, studies have demonstrated that the proteome profiles of tumor-derived exosomes are indicative of the oxygenation status of patient tumors. They have also demonstrated that exosome signaling pathways are potentially targetable drivers of hypoxia-dependent intercellular signaling during tumorigenesis. This article provides an overview of how proteomic tools can be effectively used to characterize exosomes and elucidate fundamental signaling pathways and survival mechanisms underlying hypoxia-mediated radiation resistance in breast cancer.

Can proteomics yield insight into aging aorta?

The aging aorta exhibits structural and physiological changes that are reflected in the proteome of its component cells types. The advance in proteomic technologies has made it possible to analyze the quantity of proteins associated with the natural history of aortic aging. These alterations reflect the molecular and cellular mechanisms of aging and could provide an opportunity to predict vascular health. This paper focuses on whether discoveries stemming from the application of proteomic approaches of the intact aging aorta or vascular smooth muscle cells can provide useful insights. Although there have been limited studies to date, a number of interesting proteins have been identified that are closely associated with aging in the rat aorta. Such proteins, including milk fat globule-EGF factor 8, matrix metalloproteinase type-2, and vitronectin, could be used as indicators of vascular health, or even explored as therapeutic targets for aging-related vascular diseases.

SCX charge state selective separation of tryptic peptides combined with 2D-RP-HPLC allows for detailed proteome mapping

Multidimensional peptide fractionation is widely used in proteomics to reduce the complexity of peptide mixtures prior to mass spectrometric analysis. Here, we describe the sequential use of strong cation exchange and reversed phase liquid chromatography in both basic and acidic pH buffers for separating tryptic peptides from complex mixtures of proteins. Strong cation exchange exclusively separates peptide by their charge state into neutral, singly and multi-charged species. To further reduce complexity, each peptide group was separated by reversed phase liquid chromatography at basic pH and the resultant fractions were analyzed by LC-MS/MS. This workflow was applied to a soluble protein lysate from mouse embryonic fibroblast cells, and more than 5000 proteins from 29,843 peptides were identified. The high selectivity displayed during the SCX step (93% to 100%) and the overlaps between proteins identified from the SCX-separated peptide groups, are interesting assets of the procedure.

BIOLOGICAL SIGNIFICANCE

The present work shows how complex mixture of peptides can be selectively separated by SCX based essentially on the net charge of peptides. The proposed workflow results in three well-defined subset of peptides of specific amino acid composition, which are representative of the constituent proteins. The very high selectivity obtained (93% to 99%) on the peptide side, underscores for the first time the possibility of SCX chromatography to aid in validating identified peptides.

Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics

With the advent of mass spectrometry based proteomics, the identification of thousands of proteins has become commonplace in biology nowadays. Increasingly, efforts have also been invested toward the detection and localization of posttranslational modifications. It is furthermore common practice to quantify the identified entities, a task supported by a panel of different methods. Finally, the results can also be enriched with functional knowledge gained on the proteins, detecting for instance differentially expressed gene ontology terms or biological pathways. In this study, we review the resources, methods and tools available for the researcher to achieve such a quantitative functional analysis. These include statistics for the post-processing of identification and quantification results, online resources and public repositories. With a focus on free but user-friendly software, preferably also open-source, we provide a list of tools designed to help the researcher manage the vast amount of data generated. We also indicate where such applications currently remain lacking. Moreover, we stress the eventual pitfalls of every step of such studies. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.