MSight: An image analysis software for liquid chromatography-mass spectrometry

The proteome of the blood-brain barrier in rat and mouse: highly specific identification of proteins on the luminal surface of brain microvessels by in vivo glycocapture

BACKGROUND

The active transport of molecules into the brain from blood is regulated by receptors, transporters, and other cell surface proteins that are present on the luminal surface of endothelial cells at the blood-brain barrier (BBB). However, proteomic profiling of proteins present on the luminal endothelial cell surface of the BBB has proven challenging due to difficulty in labelling these proteins in a way that allows efficient purification of these relatively low abundance cell surface proteins.

METHODS

Here we describe a novel perfusion-based labelling workflow: in vivo glycocapture. This workflow relies on the oxidation of glycans present on the luminal vessel surface via perfusion of a mild oxidizing agent, followed by subsequent isolation of glycoproteins by covalent linkage of their oxidized glycans to hydrazide beads. Mass spectrometry-based identification of the isolated proteins enables high-confidence identification of endothelial cell surface proteins in rats and mice.

RESULTS

Using the developed workflow, 347 proteins were identified from the BBB in rat and 224 proteins in mouse, for a total of 395 proteins in both species combined. These proteins included many proteins with transporter activity (73 proteins), cell adhesion proteins (47 proteins), and transmembrane signal receptors (31 proteins). To identify proteins that are enriched in vessels relative to the entire brain, we established a vessel-enrichment score and showed that proteins with a high vessel-enrichment score are involved in vascular development functions, binding to integrins, and cell adhesion. Using publicly-available single-cell RNAseq data, we show that the proteins identified by in vivo glycocapture were more likely to be detected by scRNAseq in endothelial cells than in any other cell type. Furthermore, nearly 50% of the genes encoding cell-surface proteins that were detected by scRNAseq in endothelial cells were also identified by in vivo glycocapture.

CONCLUSIONS

The proteins identified by in vivo glycocapture in this work represent the most complete and specific profiling of proteins on the luminal BBB surface to date. The identified proteins reflect possible targets for the development of antibodies to improve the crossing of therapeutic proteins into the brain and will contribute to our further understanding of BBB transport mechanisms.

TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics

Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability: The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnMS.

Peptidomics and Capillary Electrophoresis

Peptides play a crucial role in many vitally important functions of living organisms. The goal of peptidomics is the identification of the "peptidome," the whole peptide content of a cell, organ, tissue, body fluid, or organism. In peptidomic or proteomic studies, capillary electrophoresis (CE) is an alternative technique for liquid chromatography. It is a highly efficient and fast separation method requiring extremely low amounts of sample. In peptidomic approaches, CE is commonly combined with mass spectrometric (MS) detection. Most often, CE is coupled with electrospray ionization MS and less frequently with matrix-assisted laser desorption/ionization MS. CE-MS has been employed in numerous studies dealing with determination of peptide biomarkers in different body fluids for various diseases, or in food peptidomic research for the analysis and identification of peptides with special biological activities. In addition to the above topics, sample preparation techniques commonly applied in peptidomics before CE separation and possibilities for peptide identification and quantification by CE-MS or CE-MS/MS methods are discussed in this chapter.

Leveraging orthogonal mass spectrometry based strategies for comprehensive sequencing and characterization of ribosomal antimicrobial peptide natural products

Covering: Up to July 2020Ribosomal antimicrobial peptide (AMP) natural products, also known as ribosomally synthesized and post-translationally modified peptides (RiPPs) or host defense peptides, demonstrate potent bioactivities and impressive complexity that complicate molecular and biological characterization. Tandem mass spectrometry (MS) has rapidly accelerated bioactive peptide sequencing efforts, yet standard workflows insufficiently address intrinsic AMP diversity. Herein, orthogonal approaches to accelerate comprehensive and accurate molecular characterization without the need for prior isolation are reviewed. Chemical derivatization, proteolysis (enzymatic and chemical cleavage), multistage MS fragmentation, and separation (liquid chromatography and ion mobility) strategies can provide complementary amino acid composition and post-translational modification data to constrain sequence solutions. Examination of two complex case studies, gomesin and styelin D, highlights the practical implementation of the proposed approaches. Finally, we emphasize the importance of heterogeneous AMP peptidoforms that confer varying biological function, an area that warrants significant further development.

Adding polyvinylpyrrolidone to low level protein samples significantly improves peptide recovery in FASP digests: An inexpensive and simple modification to the FASP protocol

Filter-aided sample preparation (FASP) remains a popular choice for proteomic sample preparation, particularly for its ability to produce a 'clean' peptide sample clear of large molecule contaminants. However, sample loss continues to be a problem particularly for sample inputs that contain less than ten micrograms of protein. Here, we describe that the simple addition of a polymer, polyvinylpyrrolidone-40 (PVP-40) to the protein sample prior to FASP digest significantly improves peptide recovery and identifications, especially with lower level sample inputs. PVP-FASP produces clean samples which required no additional sample clean-up prior to nanoLC-MS analysis. In addition, PVP-FASP is compatible with other FASP modifications, including the use of sodium deoxycholate (DOC) to improve trypsin digestion. SIGNIFICANCE: Simple modification to FASP procedure improves sample recovery during proteomic digests in SDS, improving peptide identifications and median peptide intensity.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.

DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.

Antigen Identification for Cell-Binding Antibodies Using Ligand-Directed Crosslinking and Biotin Transfer

Panning approaches using antibody libraries often result in the isolation of antibodies that bind to cells through an unknown cellular receptor. Here, we describe a protocol that uses ligand-directed crosslinking with the aminooxy-sulfhydryl-biotin (ASB) trifunctional crosslinker followed by a proteomic analysis to identify the cellular receptors for orphan ligands. We describe the synthesis of the ASB crosslinker, labelling of the ligand with ASB, and cell binding of the labelled ligands. Next, biotin affinity purification and trypsin digestion of cell surface proteins that have been crosslinked by ASB are described. Lastly, several hints and tips to improve the proteomic analysis for these types of samples are provided.

Matriptase cleaves the amyloid-beta peptide 1-42 at Arg-5, Lys-16, and Lys-28

Objective

The type-II transmembrane extracellular serine protease matriptase was shown to cleave at Arg-102 in the amino-terminal region of the amyloid precursor protein (APP). In this study we determined matriptase cleavage sites in the amyloid-beta (Aβ) peptide region of APP (Asp-597 to Ala-638 in the APP695 isoform). A recombinant human matriptase protease domain was used to cleave a synthetic human Aβ1–42 peptide. The human APP695 or mutants at the candidate matriptase cleavage sites was co-expressed with the human matriptase or its protease-dead mutant in HEK293 cells to evaluate matriptase cleavage of APP. Overexpression of matriptase in the M17 human neuroblastoma cells was performed to determine the effect of matriptase expression on endogenous APP.

Results

The human Aβ1–42 peptide can be cleaved by the matriptase serine protease domain, at Arg-5, Lys-16, and Lys-28, as determined by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Co-expression of matriptase but not its protease-dead mutant with APP695 resulted in site-specific cleavages of the latter. Replacement of Arg-601 (Arg-5 in Aβ1–42) by Ala in APP695 prevented matriptase cleavage at this site. Overexpression of matriptase but not its protease-dead mutant in the M17 cells resulted in a significant reduction of the endogenous APP quantity.

Prioritization of Therapeutic Targets of Inflammation Using Proteomics, Bioinformatics, and In Silico Cell-Cell Interactomics

Protein-protein interactions play key roles in leukocyte extravasation process into the brain and have been attractive therapeutic targets for inhibiting brain inflammation using blocking (or neutralizing) antibodies. These targets include protein-protein interactions between cytokines (or chemokines) and their receptors on leukocytes and between adhesion molecules of leukocyte and brain endothelium. While a number of therapeutics against these targets are currently used in clinic for treatment of brain autoimmune and inflammatory disorders (e.g., multiple sclerosis), they are associated with side effects partly due to the off-target actions (i.e., nonspecific targets). There is a need for novel targets involved in the leukocyte extravasation process that are specific to leukocyte subsets or to individual inflammatory disorder and are amenable for drug development (i.e., druggable). We recently described the blood-brain barrier (BBB) Carta Project as a comprehensive collection of molecular "maps" consisting of multiple experimental omics (including RNA sequencing, proteomics, glycoproteomics, glycomics, metabolomics) and in silico informatics analyses on a number of mammalian species from hundreds of internal, publically available, or curated datasets. Utilizing the datasets and tools from the BBB Carta Project, we describe a methodology to identify novel "druggable" targets involving protein-protein interactions between activated leukocytes and brain endothelial cells using a combination of proteomics, bioinformatics, and in silico interactomics. The result is a prioritized list of protein-protein interactions in a network consisting of leukocyte-brain endothelial cell communication and contacts. These interactions can be further pursued for development of therapeutics such as neutralizing antibodies and their validation through preclinical testing. In addition to targeting brain inflammation, the method described here is applicable for peripheral inflammation and provides the opportunity to target important cell-cell interactions and communications that are more specific/selective for inflammatory disorders and improve currently available therapies.

Data Preprocessing, Visualization, and Statistical Analyses of Nontargeted Peptidomics Data from MALDI-MS

Mass spectrometric (MS) comparative analysis of peptides in biological specimens (nontargeted peptidomics) can result in large amounts of data due to chromatographic separation of a multitude of samples and subsequent MS analysis of numerous chromatographic fractions. Efficient yet effective strategies are needed to obtain relevant information. Combining visual and numerical data analysis offers a suitable approach to retrieve information and to filter data for significant differences as targets for succeeding MS/MS identifications.Visual analysis allows assessing features within a spatial context. Specific patterns are easily recognizable by the human eye. For example, derivatives representing modified forms of signals present are easily identifiable due to an apparent shift in mass and chromatographic retention times. On the other hand numerical data analysis offers the possibility to optimize spectra and to perform high-throughput calculations. A useful tool for such calculations is R, a freely available language and environment for statistical computing. R can be extended via packages to enable functionalities like mzML (open mass spectrometric data format) import and processing. R is capable of parallel processing enabling faster computation using the power of multicore systems.The combination and interplay of both approaches allows evaluating the data in a holistic way, thus helping the researcher to better understand data and experimental outcomes.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/.

Glycoproteomic comparison of clinical triple-negative and luminal breast tumors

Triple-negative (TN) breast cancer accounts for ∼ 15% of breast cancers and is characterized by a high likelihood of relapse and a lack of targeted therapies. In contrast, luminal-type tumors that express the estrogen and progesterone receptors (ER+/PR+) and lack expression of human epidermal growth factor receptor 2 (Her2-) are treated with targeted hormonal therapy and carry a better prognosis. To identify potential targets for the development of future therapeutics aimed specifically at TN breast cancers, we have used a hydrazide-based glycoproteomic workflow to compare protein expression in clinical tumors from nine TN (Her2-/ER-/PR-) and nine luminal (Her2-/ER+/PR+) patients. Using a label-free LC-MS based approach, we identified and quantified 2264 proteins. Of these, 90 proteins were more highly expressed and 86 proteins were underexpressed in the TN tumors relative to the luminal tumors. The expression level of four of these potential targets was verified in the original set of tumors by Western blot and correlated well with our mass-spectrometry-based quantification. Furthermore, 30% of the proteins differentially expressed between luminal and TN tumors were validated in a larger cohort of 406 TN and 469 luminal tumors through corresponding differences in their mRNA expression in publically available microarray data. A group of 29 of these differentially expressed proteins was shown to correctly classify 88% of TN and luminal tumors using microarray data of their associated mRNA levels. Interestingly, even within a group of TN breast cancer patients, the expression levels of these same mRNAs were able to significantly predict patient survival, suggesting that these proteins play a role in the aggressiveness seen in TN tumors. This study provides a comprehensive list of potential targets for the development of diagnostic and therapeutic agents specifically aimed at treating TN breast cancer and demonstrates the utility of using publicly available microarray data to further prioritize potential targets.

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.

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry

Background

Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.

Results

We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools.

Conclusions

AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0376-0) contains supplementary material, which is available to authorized users.

Overview of software options for processing, analysis and interpretation of mass spectrometric proteomic data

Recently, the interests in proteomics have been intensively increased, and the proteomic methods have been widely applied to many problems in cell biology. If the age of 1990s is considered to be a decade of genomics, we can claim that the following years of the new century is a decade of proteomics. The rapid evolution of proteomics has continued through these years, with a series of innovations in separation techniques and the core technologies of two-dimensional gel electrophoresis and MS. Both technologies are fueled by automation and high throughput computation for profiling of proteins from biological systems. As Patterson ever mentioned, 'data analysis is the Achilles heel of proteomics and our ability to generate data now outstrips our ability to analyze it'. The development of automatic and high throughput technologies for rapid identification of proteins is essential for large-scale proteome projects and automatic protein identification and characterization is essential for high throughput proteomics. This review provides a snap shot of the tools and applications that are available for mass spectrometric high throughput biocomputation. The review starts with a brief introduction of proteomics and MS. Computational tools that can be employed at various stages of analysis are presented, including that for data processing, identification, quantification, and the understanding of the biological functions of individual proteins and their dynamic interactions. The challenges of computation software development and its future trends in MS-based proteomics have also been speculated.

Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ

Protein quantification without isotopic labels has been a long-standing interest in the proteomics field. However, accurate and robust proteome-wide quantification with label-free approaches remains a challenge. We developed a new intensity determination and normalization procedure called MaxLFQ that is fully compatible with any peptide or protein separation prior to LC-MS analysis. Protein abundance profiles are assembled using the maximum possible information from MS signals, given that the presence of quantifiable peptides varies from sample to sample. For a benchmark dataset with two proteomes mixed at known ratios, we accurately detected the mixing ratio over the entire protein expression range, with greater precision for abundant proteins. The significance of individual label-free quantifications was obtained via a t test approach. For a second benchmark dataset, we accurately quantify fold changes over several orders of magnitude, a task that is challenging with label-based methods. MaxLFQ is a generic label-free quantification technology that is readily applicable to many biological questions; it is compatible with standard statistical analysis workflows, and it has been validated in many and diverse biological projects. Our algorithms can handle very large experiments of 500+ samples in a manageable computing time. It is implemented in the freely available MaxQuant computational proteomics platform and works completely seamlessly at the click of a button.

Identification of mechanisms for attenuation of the FSC043 mutant of Francisella tularensis SCHU S4

Previously, we identified a spontaneous, essentially avirulent mutant, FSC043, of the highly virulent strain SCHU S4 of Francisella tularensis subsp. tularensis. We have now characterized the phenotype of the mutant and the mechanisms of its attenuation in more detail. Genetic and proteomic analyses revealed that the pdpE gene and most of the pdpC gene were very markedly downregulated and, as previously demonstrated, that the strain expressed partially deleted and fused fupA and fupB genes. FSC043 showed minimal intracellular replication and induced no cell cytotoxicity. The mutant showed delayed phagosomal escape; at 18 h, colocalization with LAMP-1 was 80%, indicating phagosomal localization, whereas the corresponding percentages for SCHU S4 and the ΔfupA mutant were <10%. However, a small subset of the FSC043-infected cells contained up to 100 bacteria with LAMP-1 colocalization of around 30%. The unusual intracellular phenotype was similar to that of the ΔpdpC and ΔpdpC ΔpdpE mutants. Complementation of FSC043 with the intact fupA and fupB genes did not affect the phenotype, whereas complementation with the pdpC and pdpE genes restored intracellular replication and led to marked virulence. Even higher virulence was observed after complementation with both double-gene constructs. After immunization with the FSC043 strain, moderate protection against respiratory challenge with the SCHU S4 strain was observed. In summary, FSC043 showed a highly unusual intracellular phenotype, and based on our findings, we hypothesize that the mutation in the pdpC gene makes an essential contribution to the phenotype.

Fifteen years SIB Swiss Institute of Bioinformatics: life science databases, tools and support

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) was created in 1998 as an institution to foster excellence in bioinformatics. It is renowned worldwide for its databases and software tools, such as UniProtKB/Swiss-Prot, PROSITE, SWISS-MODEL, STRING, etc, that are all accessible on ExPASy.org, SIB's Bioinformatics Resource Portal. This article provides an overview of the scientific and training resources SIB has consistently been offering to the life science community for more than 15 years.

Prioritization of therapeutic targets of inflammation using proteomics, bioinformatics, and in silico cell-cell interactomics

Leukocyte extravasation is a multistep process, involving the movement of leukocytes out of the circulatory system, through vascular endothelium and to the site of tissue damage or infection. Protein-protein interactions play key roles in the extravasation process and have been attractive therapeutic targets for inhibiting inflammation using blocking (or neutralizing) antibodies. These targets include protein-protein interactions between cytokines (or chemokines) and their receptors on leukocytes and between adhesions molecules involving leukocyte-endothelium contacts. A number of therapeutics against these targets are currently used in clinic for treatment of inflammatory disorders, however, they are associated with side-effects partly due to the off-target actions (i.e., nonspecific targets). There is a need for novel targets involved in the leukocyte extravasation process that are specific to leukocyte subsets or to individual inflammatory disorder, and are amenable for drug development (i.e., duggable). In this chapter, we describe a methodology to identify novel "druggable" targets involving protein-protein interactions between activated leukocytes and endothelial cells using a combination of proteomics, bioinformatics and in silico interactomics. The result is a prioritized list of protein-protein interactions in a network consisting of leukocyte-endothelial cell communication and contacts. These prioritized targets can be pursued for the development of therapeutics such as neutralizing antibodies and for their validation through preclinical testing. The method described here provides the workflow to identify and clinically target important cell-cell interactions that are specific/selective for particular inflammatory disorders and to improve currently available therapies.

Peptidomics analysis of human blood specimens for biomarker discovery

This review addresses the concepts, limitations and perspectives for the application of peptidomics science and technologies to discover putative biomarkers in blood specimens. Peptidomics can be defined as the comprehensive multiplex analysis of endogenous peptides contained within a biological sample under defined conditions to describe the multitude of native peptides in a biological compartment. In addition to the discovery of disease associated biomarkers, an emerging field in peptidomics is the analysis of peptides to describe in vivo effects of protease inhibitors. The development and application of peptidomics technologies represent an arena of biomarker research that has the potential for adding significant clinical value.

Proteomics and the analysis of proteomic data: 2013 overview of current protein-profiling technologies

Mass spectrometry has become a major tool in the study of proteomes. The analysis of proteolytic peptides and their fragment ions by this technique enables the identification and quantitation of the precursor proteins in a mixture. However, deducing chemical structures and then protein sequences from mass-to-charge ratios is a challenging computational task. Software tools incorporating powerful algorithms and statistical methods improved our ability to process the large quantities of proteomics data. Repositories of spectral data make both data analysis and experimental design more efficient. New approaches in quantitative and statistical proteomics make possible a greater coverage of the proteome, the identification of more post-translational modifications, and a greater sensitivity in the quantitation of targeted proteins.

Characterization of oil sands process-affected waters by liquid chromatography orbitrap mass spectrometry

Recovery of bitumen from oil sands in northern Alberta, Canada, occurs by surface mining or in situ thermal recovery, and both methods produce toxic oil sands process-affected water (OSPW). A new characterization strategy for surface mining OSPW (sm-OSPW) and in situ OSPW (is-OSPW) was achieved by combining liquid chromatography with orbitrap mass spectrometry (MS). In electrospray positive and negative ionization modes (ESI(+)/ESI(-)), mass spectral data were acquired with high resolving power (RP > 100,000-190,000) and mass accuracy (<2 ppm). The additional chromatographic resolution allowed for separation of various isomers and interference-free MS(n) experiments. Overall, ∼3000 elemental compositions were revealed in each OSPW sample, corresponding to a range of heteroatom-containing homologue classes: Ox (where x = 1-6), NOx (where x = 1-4), SOx (where x = 1-4), NO₂S, N, and S. Despite similarities between the OSPW samples at the level of heteroatom class, the two samples were very different when considering isomer patterns and double-bond equivalent profiles. The chromatographic separations also allowed for confirmation that, in both OSPW samples, the O₂ species detected in ESI(-) (i.e., naphthenic acids) were chemically distinct from the corresponding O₂ species detected in ESI(+). In comparison to model compounds, tandem MS spectra of these new O₂ species suggested a group of non-acidic compounds with dihydroxy, diketo, or ketohydroxy functionality. In light of the known endocrine-disrupting potential of sm-OSPW, the toxicity of these O₂ species deserves attention and the method should be further applied to environmental forensic analysis of water in the region.

Current status and advances in quantitative proteomic mass spectrometry

The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.

Issues and applications in label-free quantitative mass spectrometry

To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets

Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC-MS/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC-MS/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.

Parasites, proteomes and systems: has Descartes' clock run out of time?

Systems biology aims to integrate multiple biological data types such as genomics, transcriptomics and proteomics across different levels of structure and scale; it represents an emerging paradigm in the scientific process which challenges the reductionism that has dominated biomedical research for hundreds of years. Systems biology will nevertheless only be successful if the technologies on which it is based are able to deliver the required type and quality of data. In this review we discuss how well positioned is proteomics to deliver the data necessary to support meaningful systems modelling in parasite biology. We summarise the current state of identification proteomics in parasites, but argue that a new generation of quantitative proteomics data is now needed to underpin effective systems modelling. We discuss the challenges faced to acquire more complete knowledge of protein post-translational modifications, protein turnover and protein-protein interactions in parasites. Finally we highlight the central role of proteome-informatics in ensuring that proteomics data is readily accessible to the user-community and can be translated and integrated with other relevant data types.

Strategies to dissect parasite proteomes

Proteomes are complex and dynamic entities that are still poorly understood, but the application of proteomic technologies has become invaluable in many areas of biology, including parasitology. These technologies can be exploited to identify proteins in both complex or relatively simple samples, that formerly could only be characterized by targeted approaches such as Western blotting. Quantitative proteomic approaches can reveal modulations in protein expression that accompany phenotypes of interest. Proteomic approaches have been exploited to understand some of the molecular basis for host:parasite interactions and to elucidate phenotypes such as virulence, antigenicity and drug resistance. Many of the same technologies can also be more easily applied to targeted sub-proteomes.

Examples from several studies on pathogen proteomes and sub-proteomes, from bacteria to helminths, are presented to illustrate the potential and limitations of proteomic technologies.

Minimization of side reactions during Lys Tag derivatization of C-terminal lysine peptides

Several issues need to be considered concerning chemical labeling strategies in proteomics. Some of these are labeling specificity, possible side reactions, completeness of reaction, recovery rate, conserving integrity of sample, hydrolysis of peptide bonds at high pH, and signal suppression in mass spectrometry (MS). We tested the effects of different reaction conditions for 2-methoxy-4,5-dihydro-1H-imidazole (Lys Tag) derivatization of the ε-amine group of lysine (K) residues. By using nanoflow LC-electrospray ionization-MS (LC-ESI-MS) and MS/MS in combination with MSight 2-D image analysis, we found that standard Lys Tag derivatization processes and conditions induce side reactions such as (i) Lys Tag labeling of the N-terminus, (ii) methylation of internal aspartic acid (D), glutamic acid (E) and C- and N-peptide termini and (iii) deamidation of asparagine (N) and glutamine (Q). We found temperature and pH to be the main variables to control side reactions. Lowering the reaction temperature from 55°C to room temperature reduced deamidation from 22.8±1.4% (SEM) to 7.7±5.5% (SEM) and almost totally blocked methylation (7.0±1.2% (SEM) to 0.4±0.4% (SEM) of the internal acidic amino acids (D and E) at high pH. We conclude that lowering the reaction temperature minimizes undesired side reactions during Lys Tag derivatization in solution.

Virtual separation of phytochemical constituents by their adduct-ion patterns in full mass spectra

In the present study, a tool called classifier for traditional Chinese medicine (CTCM) was developed to facilitate the discrimination of phytochemical constituents in two-dimensional datasets of liquid chromatography/mass spectrometry (LC/MS). Based on the full mass spectral characteristics of components in a mixture, particularly their adduct-ion patterns, an entire LC/MS dataset can be separated into several sub-datasets, each corresponding to one or several types of natural products. CTCM has been verified using 24 standard compounds and successfully applied in two previously reported LC/MS datasets, which confirmed the capability of proposed tool to extract adduct-ion patterns from LC/MS datasets. Moreover, the LC/MS dataset of a Wei-Fu-Chun (WFC) tablet, a prescription drug consisting of three crude herbs used for the treatment of enteric diseases, was analyzed using CTCM. The analysis indicated that the compounds in WFC could be split into three groups, with the main constituents including saponins from Radix Ginseng Rubra, flavonoids from Fructus aurantii, and phenolic compounds from Isodon amethystoides. The major compounds in the three groups were either positively identified or tentatively characterized by multi-stage and high resolution MS. The proposed tool provides a novel approach for processing the LC/MS datasets of complex samples, such as traditional Chinese medicine and botanical drugs.

A novel alignment method and multiple filters for exclusion of unqualified peptides to enhance label-free quantification using peptide intensity in LC-MS/MS

Though many software packages have been developed to perform label-free quantification of proteins in complex biological samples using peptide intensities generated by LC-MS/MS, two critical issues are generally ignored in this field: (i) peptides have multiple elution patterns across runs in an experiment, and (ii) many peptides cannot be used for protein quantification. To address these two key issues, we have developed a novel alignment method to enable accurate peptide peak retention time determination and multiple filters to eliminate unqualified peptides for protein quantification. Repeatability and linearity have been tested using six very different samples, i.e., standard peptides, kidney tissue lysates, HT29-MTX cell lysates, depleted human serum, human serum albumin-bound proteins, and standard proteins spiked in kidney tissue lysates. At least 90.8% of the proteins (up to 1,390) had CVs ≤ 30% across 10 technical replicates, and at least 93.6% (up to 2,013) had R(2) ≥ 0.9500 across 7 concentrations. Identical amounts of standard protein spiked in complex biological samples achieved a CV of 8.6% across eight injections of two groups. Further assessment was made by comparing mass spectrometric results to immunodetection, and consistent results were obtained. The new approach has novel and specific features enabling accurate label-free quantification.

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages.

MassChroQ: a versatile tool for mass spectrometry quantification

Recently, many software tools have been developed to perform quantification in LC-MS analyses. However, most of them are specific to either a quantification strategy (e.g. label-free or isotopic labelling) or a mass-spectrometry system (e.g. high or low resolution). In this context, we have developed MassChroQ (Mass Chromatogram Quantification), a versatile software that performs LC-MS data alignment and peptide quantification by peak area integration on extracted ion chromatograms. MassChroQ is suitable for quantification with or without labelling and is not limited to high-resolution systems. Peptides of interest (for example all the identified peptides) can be determined automatically, or manually by providing targeted m/z and retention time values. It can handle large experiments that include protein or peptide fractionation (as SDS-PAGE, 2-D LC). It is fully configurable. Every processing step is traceable, the produced data are in open standard formats and its modularity allows easy integration into proteomic pipelines. The output results are ready for use in statistical analyses. Evaluation of MassChroQ on complex label-free data obtained from low and high-resolution mass spectrometers showed low CVs for technical reproducibility (1.4%) and high coefficients of correlation to protein quantity (0.98). MassChroQ is freely available under the GNU General Public Licence v3.0 at http://pappso.inra.fr/bioinfo/masschroq/.

Quantitative phosphoproteomics strategies for understanding protein kinase-mediated signal transduction pathways

Protein phosphorylation is a central regulatory mechanism of cell signaling pathways. This highly controlled biochemical process is involved in most cellular functions, and defects in protein kinases and phosphatases have been implicated in many diseases, highlighting the importance of understanding phosphorylation-mediated signaling networks. However, phosphorylation is a transient modification, and phosphorylated proteins are often less abundant. Therefore, the large-scale identification and quantification of phosphoproteins and their phosphorylation sites under different conditions are one of the most interesting and challenging tasks in the field of proteomics. Both 2D gel electrophoresis and liquid chromatography-tandem mass spectrometry serve as key phosphoproteomic technologies in combination with prefractionation, such as enrichment of phosphorylated proteins/peptides. Recently, new possibilities for quantitative phosphoproteomic analysis have been offered by technical advances in sample preparation, enrichment, separation, instrumentation, quantification and informatics. In this article, we present an overview of several strategies for quantitative phosphoproteomics and discuss how phosphoproteomic analysis can help to elucidate signaling pathways that regulate various cellular processes.

Strategy for identification and detection of multiple oxidative modifications within proteins applied on persulfate-oxidized hemoglobin and human serum albumin

Oxidative stress has been suggested as an underlying mechanism of many human diseases. However, definitive evidence for this association has not been presented due to different shortcomings of the methods used to measure biomarkers of oxidative stress. Persulfates are oxidizing agents known to elicit hypersensitive reactions from the airways and skin. Despite a frequent use of persulfates at many work places, no biomarkers for persulfate exposure are available. The aim of this study was to develop a strategy for the identification and detection of multiple oxidative modifications within proteins. This strategy was applied on persulfate-oxidized proteins to identify oxidized peptides suitable for further investigation as biomarkers of persulfate exposure or oxidative stress. A strategy for the identification and the relative quantification of multiple oxidative modifications within proteins was developed. The usage of two software packages facilitated the search for modified peptides to a great extent. Oxidized peptides were relatively quantified using liquid chromatography/tandem mass spectrometry in selected reaction monitoring mode. The result showed that persulfates oxidize tryptophans and methionines resulting in mass shifts of 16 and/or 32 Da. Also, oxidized albumin peptides in nasal lavage fluid samples from subjects challenged with persulfate were detected. The oxidation degree before and after challenge remained constant for peptides containing methionine sulfoxide. For peptides containing oxidized tryptophan the oxidation degree increased after exposure. Some of these oxidized peptides may be suitable as biomarkers; however, further evaluation is required.

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.

Methods to study glycoproteins at the blood-brain barrier using mass spectrometry

Glycosylation is the most common posttranslational modification of proteins in mammalian cells and is limited mainly to membrane and secreted proteins. Glycoproteins play several key roles in the physiology and pathophysiology of the blood-brain barrier (BBB) and are attractive as diagnostic markers and therapeutic targets for many neurological diseases. However, large-scale glycoproteomic studies of the BBB have been lacking, largely due to the complexity of analyzing glycoproteins and a lack of available tools for this analysis. Recent development of the hydrazide capture method and significant advances in mass spectrometry (MS)-based proteomics over the last few years have enabled selective enrichment of glycoproteins from complex biological samples and their quantitative comparisons in multiple conditions. In this chapter, we describe methods for: (1) isolating membrane and secreted proteins from BEC and other cells of the neurovascular unit, (2) enriching glycoproteins using hydrazide capture, and (3) performing label-free quantitative proteomics to identify differential glycoprotein expression in various biological conditions. Hydrazide capture, when coupled with label-free quantitative proteomics, is a reproducible and sensitive method that allows for quantitative profiling of a large number of glycoproteins from biological samples for the purposes of differential expression measurements and biomarker discovery.

Bioinformatics for qualitative and quantitative proteomics

Mass spectrometry is today a key analytical technique to elucidate the amount and content of proteins expressed in a certain cellular context. The degree of automation in proteomics has yet to reach that of genomic techniques, but even current technologies make a manual inspection of the data infeasible. This article addresses the key algorithmic problems bioinformaticians face when handling modern proteomic samples and shows common solutions to them. We provide examples on how algorithms can be combined to build relatively complex analysis pipelines, point out certain pitfalls and aspects worth considering and give a list of current state-of-the-art tools.

LC/MS data processing for label-free quantitative analysis

In this chapter, we describe the use of SuperHirn and MSight, two complementary tools developed to the processing of label-free LC/MS data in view of the quantitation of proteomics samples. While MSight is mainly dedicated to the visualisation and navigation into LC/MS data, SuperHirn is specialised in peak detection, normalisation and alignment of LC/MS runs. These two tools can be used in a complementary way and one of the possible usages is described here.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.