MSQuant, an open source platform for mass spectrometry-based quantitative proteomics

SILAC and alternatives in studying cellular proteomes of plants

Quantitative proteomics by metabolic labeling has a high impact on the growing field of plant systems biology. SILAC has been pioneered and optimized for plant cell culture systems allowing for SILAC-based quantitative experiments in specialized experimental setups. In comparison to other model organisms, the application of SILAC to whole plants is challenging. As autotrophic organisms, plants under their natural growth conditions can hardly be fully labeled with stable isotope-coded amino acids. The metabolic labeling with inorganic nitrogen is therefore the method of choice for most whole-plant physiological questions. Plants can easily metabolize different inorganic nitrogen isotopes. The incorporation of the labeled inorganic nitrogen then results in proteins and metabolites with distinct molecular mass, which can be detected on a mass spectrometer. In comparative quantitative experiments, similarly as in SILAC experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches were developed for metabolite identification. Here we present a typical hydroponics setup, the workflow for processing of samples, mass spectrometry and data analysis for large-scale metabolic labeling experiments of whole plants.

Fifteen years of Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC)

Here I describe the history of the Stable Isotope Labeling by Amino Acids in Cell culture (SILAC) technology. Although published in 2002, it had already been developed and used in my laboratory for a number of years. From the beginning, it was applied to challenging problems in cell signaling that were considered out of reach for proteomics at the time. It was also used to pioneer proteomic interactomics, time series and dynamic posttranslational modification studies. While initially developed for metabolically accessible systems, such as cell lines, it was subsequently extended to whole animal labeling as well as to clinical applications-in the form or spike-in or super-SILAC. New formats and applications for SILAC labeling continue to be developed, for instance for protein-turnover studies.

Quantifying in vivo, site-specific changes in protein methylation with SILAC

Interest in protein methylation has grown rapidly in recent years. Mass spectrometry-based proteomics is ideally suited to characterize protein modifications, but the multiplicity of methylated residues and the lack of efficient methods to enrich methylated proteins have limited the proteomic identification of protein methylation sites. In this protocol, we compare two metabolic labeling approaches, stable isotope labeling by amino acids in cell culture (SILAC) and its variant heavy methyl SILAC, for studying protein methylation. Instead of heavy lysine and arginine in the typical SILAC experiment, heavy methyl SILAC uses (13)C, (2)H methionine as the labeling amino acid. As cells convert methionine to S-adenosylmethionine, heavy methyl SILAC encodes a 4 Da mass tag for each methyl group, distinguishing between degrees of methylation is possible from mass difference alone. We provide a protocol for SILAC-based analyses of protein methylation and highlight the strengths and weaknesses of each method for targeted and proteomic analyses.

Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective

Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

SUMMARY

With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses.

AVAILABILITY AND IMPLEMENTATION

SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

Vascular endothelial growth factor (VEGF) and platelet (PF-4) factor 4 inputs modulate human microvascular endothelial signaling in a three-dimensional matrix migration context

The process of angiogenesis is under complex regulation in adult organisms, particularly as it often occurs in an inflammatory post-wound environment. As such, there are many impacting factors that will regulate the generation of new blood vessels which include not only pro-angiogenic growth factors such as vascular endothelial growth factor, but also angiostatic factors. During initial postwound hemostasis, a large initial bolus of platelet factor 4 is released into localized areas of damage before progression of wound healing toward tissue homeostasis. Because of its early presence and high concentration, the angiostatic chemokine platelet factor 4, which can induce endothelial anoikis, can strongly affect angiogenesis. In our work, we explored signaling crosstalk interactions between vascular endothelial growth factor and platelet factor 4 using phosphotyrosine-enriched mass spectrometry methods on human dermal microvascular endothelial cells cultured under conditions facilitating migratory sprouting into collagen gel matrices. We developed new methods to enable mass spectrometry-based phosphorylation analysis of primary cells cultured on collagen gels, and quantified signaling pathways over the first 48 h of treatment with vascular endothelial growth factor in the presence or absence of platelet factor 4. By observing early and late signaling dynamics in tandem with correlation network modeling, we found that platelet factor 4 has significant crosstalk with vascular endothelial growth factor by modulating cell migration and polarization pathways, centered around P38α MAPK, Src family kinases Fyn and Lyn, along with FAK. Interestingly, we found EphA2 correlational topology to strongly involve key migration-related signaling nodes after introduction of platelet factor 4, indicating an influence of the angiostatic factor on this ambiguous but generally angiogenic signal in this complex environment.

Quantitative mass spectrometry-based proteomics in angiogenesis

The process of new blood vessel formation from pre-existing ones is called angiogenesis. Beyond playing a critical role in the physiological development of the vascular system, angiogenesis is a well-recognised hallmark of cancer. Unbiased system-wide approaches are required to complement the current knowledge, and intimately understand the molecular mechanisms regulating this process in physiological and pathological conditions. In this review we describe the cellular and molecular dynamics regulating the physiological growth of vessels and their deregulation in cancer, survey in vitro and in vivo models currently exploited to investigate various aspects of angiogenesis and describe state-of-the-art and most widespread methods and technologies in MS shotgun proteomics. Finally, we focus on current applications of MS to better understand endothelial cell behaviour and propose how modern proteomics can impact on angiogenesis research.

Redox proteomics uncovers peroxynitrite-sensitive proteins that help Escherichia coli to overcome nitrosative stress

Peroxynitrite is a highly reactive chemical species with antibacterial properties that are synthesized in immune cells. In a proteomic approach, we identified specific target proteins of peroxynitrite-induced modifications in Escherichia coli. Although peroxynitrite caused a fairly indiscriminate nitration of tyrosine residues, reversible modifications of protein thiols were highly specific. We used a quantitative redox proteomic method based on isotope-coded affinity tag chemistry and identified four proteins consistently thiol-modified in cells treated with peroxynitrite as follows: AsnB, FrmA, MaeB, and RidA. All four were required for peroxynitrite stress tolerance in vivo. Three of the identified proteins were modified at highly conserved cysteines, and MaeB and FrmA are known to be directly involved in the oxidative and nitrosative stress response in E. coli. In in vitro studies, we could show that the activity of RidA, a recently discovered enamine/imine deaminase, is regulated in a specific manner by the modification of its single conserved cysteine. Mutation of this cysteine 107 to serine generated a constitutively active protein that was not susceptible to peroxynitrite.

APOOL is a cardiolipin-binding constituent of the Mitofilin/MINOS protein complex determining cristae morphology in mammalian mitochondria

Mitochondrial cristae morphology is highly variable and altered under numerous pathological conditions. The protein complexes involved are largely unknown or only insufficiently characterized. Using complexome profiling we identified apolipoprotein O (APOO) and apolipoprotein O-like protein (APOOL) as putative components of the Mitofilin/MINOS protein complex which was recently implicated in determining cristae morphology. We show that APOOL is a mitochondrial membrane protein facing the intermembrane space. It specifically binds to cardiolipin in vitro but not to the precursor lipid phosphatidylglycerol. Overexpression of APOOL led to fragmentation of mitochondria, a reduced basal oxygen consumption rate, and altered cristae morphology. Downregulation of APOOL impaired mitochondrial respiration and caused major alterations in cristae morphology. We further show that APOOL physically interacts with several subunits of the MINOS complex, namely Mitofilin, MINOS1, and SAMM50. We conclude that APOOL is a cardiolipin-binding component of the Mitofilin/MINOS protein complex determining cristae morphology in mammalian mitochondria. Our findings further assign an intracellular role to a member of the apolipoprotein family in mammals.

Stable isotope dimethyl labeling combined with LTQ mass spectrometric detection, a quantitative proteomics technology used in liver cancer research

Liver cancer is a common malignant disease, with high incidence and mortality rates. The study on the proteomics of liver cancer has attracted particular attention. The quantitative study method of proteomics depends predominantly on two-dimensional (2D) gel electrophoresis. In the present study we reported a rapid and accurate proteomics quantitative study method of high repeatability that includes the use of stable isotope labeling for the extraction of proteins and peptides via enzymolysis to achieve new type 2D capillary liquid chromatography-mass spectrometry separation using the separation mode of cation-exchange chromatography in conjunction with reversed-phase chromatography. LTQ OrbiTrap mass spectrometry detection was also performed. A total of 188 differential proteins were analyzed, including 122 upregulating [deuterium/hydrogen ratio (D/H) >1.5)] and 66 downregulating proteins (D/H<0.67). These proteins may play an important role in the occurrence, drug resistance, metastasis and recurrence of cancer or other pathological processes. Such a proteomics technology may provide biological data as well as a new methodological basis for liver cancer research.

Quantitative Proteomics via High Resolution MS Quantification: Capabilities and Limitations

Recent improvements in the mass accuracy and resolution of mass spectrometers have led to renewed interest in label-free quantification using data from the primary mass spectrum (MS1) acquired from data-dependent proteomics experiments. The capacity for higher specificity quantification of peptides from samples enriched for proteins of biological interest offers distinct advantages for hypothesis generating experiments relative to immunoassay detection methods or prespecified peptide ions measured by multiple reaction monitoring (MRM) approaches. Here we describe an evaluation of different methods to post-process peptide level quantification information to support protein level inference. We characterize the methods by examining their ability to recover a known dilution of a standard protein in background matrices of varying complexity. Additionally, the MS1 quantification results are compared to a standard, targeted, MRM approach on the same samples under equivalent instrument conditions. We show the existence of multiple peptides with MS1 quantification sensitivity similar to the best MRM peptides for each of the background matrices studied. Based on these results we provide recommendations on preferred approaches to leveraging quantitative measurements of multiple peptides to improve protein level inference.

Regulation of transcription through acetylation of H3K122 on the lateral surface of the histone octamer

Histone modifications are key regulators of chromatin function. However, little is known to what extent histone modifications can directly impact on chromatin. Here, we address how a modification within the globular domain of histones regulates chromatin function. We demonstrate that H3K122ac can be sufficient to stimulate transcription and that mutation of H3K122 impairs transcriptional activation, which we attribute to a direct effect of H3K122ac on histone-DNA binding. In line with this, we find that H3K122ac defines genome-wide genetic elements and chromatin features associated with active transcription. Furthermore, H3K122ac is catalyzed by the coactivators p300/CBP and can be induced by nuclear hormone receptor signaling. Collectively, this suggests that transcriptional regulators elicit their effects not only via signaling to histone tails but also via direct structural perturbation of nucleosomes by directing acetylation to their lateral surface.

Discovery of histone modification crosstalk networks by stable isotope labeling of amino acids in cell culture mass spectrometry (SILAC MS)

In this paper we describe an approach that combines stable isotope labeling of amino acids in cells culture, high mass accuracy liquid chromatography tandem mass spectrometry and a novel data analysis approach to accurately determine relative peptide post-translational modification levels. This paper describes the application of this approach to the discovery of novel histone modification crosstalk networks in Saccharomyces cerevisiae. Yeast histone mutants were generated to mimic the presence/absence of 44 well-known modifications on core histones H2A, H2B, H3, and H4. In each mutant strain the relative change in H3 K79 methylation and H3 K56 acetylation were determined using stable isotope labeling of amino acids in cells culture. This approach showed relative changes in H3 K79 methylation and H3 K56 acetylation that are consistent with known histone crosstalk networks. More importantly, this study revealed additional histone modification sites that affect H3 K79 methylation and H3 K56 acetylation.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Candidate biomarker discovery for angiogenesis by automatic integration of Orbitrap MS1 spectral- and X!Tandem MS2 sequencing information

Candidate protein biomarker discovery by full automatic integration of Orbitrap full MS1 spectral peptide profiling and X!Tandem MS2 peptide sequencing is investigated by analyzing mass spectra from brain tumor samples using Peptrix. Potential protein candidate biomarkers found for angiogenesis are compared with those previously reported in the literature and obtained from previous Fourier transform ion cyclotron resonance (FT-ICR) peptide profiling. Lower mass accuracy of peptide masses measured by Orbitrap compared to those measured by FT-ICR is compensated by the larger number of detected masses separated by liquid chromatography (LC), which can be directly linked to protein identifications. The number of peptide sequences divided by the number of unique sequences is 9248/6911 ≈ 1.3. Peptide sequences appear 1.3 times redundant per up-regulated protein on average in the peptide profile matrix, and do not seem always up-regulated due to tailing in LC retention time (40%), modifications (40%) and mass determination errors (20%). Significantly up-regulated proteins found by integration of X!Tandem are described in the literature as tumor markers and some are linked to angiogenesis. New potential biomarkers are found, but need to be validated independently. Eventually more proteins could be found by actively involving MS2 sequence information in the creation of the MS1 peptide profile matrix.

Target identification and mechanism of action in chemical biology and drug discovery

Target-identification and mechanism-of-action studies have important roles in small-molecule probe and drug discovery. Biological and technological advances have resulted in the increasing use of cell-based assays to discover new biologically active small molecules. Such studies allow small-molecule action to be tested in a more disease-relevant setting at the outset, but they require follow-up studies to determine the precise protein target or targets responsible for the observed phenotype. Target identification can be approached by direct biochemical methods, genetic interactions or computational inference. In many cases, however, combinations of approaches may be required to fully characterize on-target and off-target effects and to understand mechanisms of small-molecule action.

Global impact of Salmonella pathogenicity island 2-secreted effectors on the host phosphoproteome

During the late stages of infection, Salmonella secretes numerous effectors through a type III secretion system that is encoded within Salmonella pathogenicity island 2 (SPI2). Despite the importance of SPI2 as a major virulence factor leading to the systemic spread of the bacteria and diseases, a global view of its effects on host responses is still lacking. Here, we measured global impacts of SPI2 effectors on the host phosphorylation and protein expression levels in RAW264.7 and in HeLa cells, as macrophage and nonphagocytic models of infection. We observe that SPI2 effectors differentially modulate the host phosphoproteome and cellular processes (e.g. protein trafficking, cytoskeletal regulation, and immune signaling) in a host cell-dependent manner. Our unbiased approach reveals the involvement of many previously unrecognized proteins, including E3 ligases (HERC4, RanBP2, and RAD18), kinases (CDK, SIK3, and WNK1), and histones (H2B1F, H4, and H15), in late stages of Salmonella infection. Furthermore, from this phosphoproteome analysis and other quantitative screens, we identified HSP27 as a direct in vitro and in vivo molecular target of the only type III secreted kinase, SteC. Using biochemical and cell biological assays, we demonstrate that SteC phosphorylates multiple sites in HSP27 and induces actin rearrangement through this protein. Together, these results provide a broader landscape of host players contributing to specific processes/pathways mediated by SPI2 effectors than was previously appreciated.

Systematic evaluation of reference protein normalization in proteomic experiments

Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in Arabidopsis thaliana. Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.

Quantitative phosphoproteomics after auxin-stimulated lateral root induction identifies an SNX1 protein phosphorylation site required for growth

Protein phosphorylation is instrumental to early signaling events. Studying system-wide phosphorylation in relation to processes under investigation requires a quantitative proteomics approach. In Arabidopsis, auxin application can induce pericycle cell divisions and lateral root formation. Initiation of lateral root formation requires transcriptional reprogramming following auxin-mediated degradation of transcriptional repressors. The immediate early signaling events prior to this derepression are virtually uncharacterized. To identify the signal molecules responding to auxin application, we used a lateral root-inducible system that was previously developed to trigger synchronous division of pericycle cells. To identify and quantify the early signaling events following this induction, we combined (15)N-based metabolic labeling and phosphopeptide enrichment and applied a mass spectrometry-based approach. In total, 3068 phosphopeptides were identified from auxin-treated root tissue. This root proteome dataset contains largely phosphopeptides not previously reported and represents one of the largest quantitative phosphoprotein datasets from Arabidopsis to date. Key proteins responding to auxin treatment included the multidrug resistance-like and PIN2 auxin carriers, auxin response factor2 (ARF2), suppressor of auxin resistance 3 (SAR3), and sorting nexin1 (SNX1). Mutational analysis of serine 16 of SNX1 showed that overexpression of the mutated forms of SNX1 led to retarded growth and reduction of lateral root formation due to the reduced outgrowth of the primordium, showing proof of principle for our approach.

The ROK family regulator Rok7B7 pleiotropically affects xylose utilization, carbon catabolite repression, and antibiotic production in streptomyces coelicolor

Members of the ROK family of proteins are mostly transcriptional regulators and kinases that generally relate to the control of primary metabolism, whereby its member glucose kinase acts as the central control protein in carbon control in Streptomyces. Here, we show that deletion of SCO6008 (rok7B7) strongly affects carbon catabolite repression (CCR), growth, and antibiotic production in Streptomyces coelicolor. Deletion of SCO7543 also affected antibiotic production, while no major changes were observed after deletion of the rok family genes SCO0794, SCO1060, SCO2846, SCO6566, or SCO6600. Global expression profiling of the rok7B7 mutant by proteomics and microarray analysis revealed strong upregulation of the xylose transporter operon xylFGH, which lies immediately downstream of rok7B7, consistent with the improved growth and delayed development of the mutant on xylose. The enhanced CCR, which was especially obvious on rich or xylose-containing media, correlated with elevated expression of glucose kinase and of the glucose transporter GlcP. In liquid-grown cultures, expression of the biosynthetic enzymes for production of prodigionines, siderophores, and calcium-dependent antibiotic (CDA) was enhanced in the mutant, and overproduction of prodigionines was corroborated by matrix-assisted laser desorption ionization-time-of-flight analysis. These data present Rok7B7 as a pleiotropic regulator of growth, CCR, and antibiotic production in Streptomyces.

Stable isotope labeling methods in protein profiling

Mass spectrometry (MS) analysis of peptides and proteins has evolved dramatically over the last 20 years. Improvement of MS instrumentation, computational data analysis, and the availability of complete sequence databases for many species have made large-scale proteomics analyses possible. The measurement of global protein abundance by quantitative mass spectrometry has the potential to increase both speed and impact of biological and clinical research. However, to be able to detect and identify potential biomarkers, reproducible and accurate quantification is essential. The following chapter describes how to perform quantitative protein profiling using stable isotope labeling methods. Throughout, there is a focus on guidance in selection of an appropriate labeling strategy. With that in mind, we have included a section on acquisition and understanding of the liquid chromatography-mass spectrometry (LC-MS) data format. Further, we describe the different stable isotope labeling methods and their pros and cons. We start by giving an overview of the overall quantitative proteomics workflow in which extracting relevant biological information from the acquired data is the ultimate goal.

Determining protein concentrations of the human ventricular proteome

Proteomics is mostly used for measurements of relative differences in protein concentrations. Although such analyses are meaningful for comparing differences between two and more conditions, they do not directly provide details on the absolute protein concentrations within a system. Now, proteomics is heading more towards absolute quantitative strategies with results being expressed in copies/cell or ng/mg tissue. In the cardiac context, such quantitative information is crucial for (1) evaluating the feasibility of selecting a certain protein as potential novel drug target, (2) the expected concentration excreted into the circulation when selecting a biomarker, and (3) to build a model of cardiac function at the molecular level. At the same time, by mass spectrometry-based proteomics, a wealth of spectral information is gathered that can be used to evaluate protein levels of a select set of novel disease-altered proteins using, for instance, single reaction monitoring. Here we describe how to build a quantitative map of the human left ventricular proteome using a simple yet effective mass spectrometry-based spectral count method.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Proteomic analysis identifies a novel function for galectin-3 in the cell entry of parvovirus

Cellular factors associated with the parvovirus minute virus of mice (MVM) during infection are thought to play important roles in the MVM life cycle but only a few of these have been identified. Here we used a proteomic-based approach in order to identify host-binding partners of MVM. Using purified MVM as bait for immunoprecipitation assays, a total of 150 proteins were identified in MVM immunoprecipitates by quantitative liquid chromatography-tandem mass spectrometry. Galectin-3 was one of six proteins showing a statistically significant enrichment across replicates. Small interfering RNA depletion studies revealed an important role for galectin-3 in MVM endocytosis and infectivity in LA9 mouse fibroblast cells. Galectin-3-depleted cells were less susceptible to MVM infection than control cells and showed a significant reduction of MVM cellular uptake, but not of MVM binding to the cell surface. Our results indicate an important role for galectin-3 in the cellular uptake of MVM. We propose that galectin-3 facilitates the access of MVM to its receptor(s) at the plasma membrane and in this way promotes MVM endocytosis.

De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics

We report an algorithm designed for the calibration of low resolution peptide mass spectra. Our algorithm is implemented in a program called FineTune, which corrects systematic mass measurement error in 1 min, with no input required besides the mass spectra themselves. The mass measurement accuracy for a set of spectra collected on an LTQ-Velos improved 20-fold from -0.1776 ± 0.0010 m/z to 0.0078 ± 0.0006 m/z after calibration (avg ± 95 % confidence interval). The precision in mass measurement was improved due to the correction of non-linear variation in mass measurement accuracy across the m/z range.

Characterization of carbon nanotube protein corona by using quantitative proteomics

The protein corona of a nanomaterial is a complex layer of proteins spontaneously and stably formed when the material is exposed to body fluids or intracellular environments. In this study, we utilised stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics to characterise the binding of human cellular proteins to two forms of carbon nanoparticles: namely multi-walled carbon nanotubes (MWCNTs) and carbon black (CB). The relative binding efficiency of over 750 proteins to these materials is measured. The data indicate that MWCNTs and CB bind to vastly different sets of proteins. The molecular basis of selectivity in protein binding is investigated. This study is the first large-scale characterisation of protein corona on CNT, providing the biochemical basis for the assessment of the suitability of CNTs as biomedical tools, and as an emerging pollutant.

FROM THE CLINICAL EDITOR

This team of investigators performed the first large-scale characterization of protein corona on carbon nanotubes, studying 750 proteins and assessing the suitability of CNTs as biomedical tools and as an emerging pollutant.

Resolving protein interactions and complexes by affinity purification followed by label-based quantitative mass spectrometry

Label-based quantitative mass spectrometry analysis of affinity purified complexes, with its built-in negative controls and relative ease of use, is an increasingly popular choice for defining protein-protein interactions and multiprotein complexes. This approach, which differentially labels proteins/peptides from two or more populations and combines them prior to analysis, permits direct comparison of a protein pulldown (e.g. affinity purified tagged protein) to that of a control pulldown (e.g. affinity purified tag alone) in a single mass spectrometry (MS) run, thus avoiding the variability inherent in separate runs. The use of quantitative techniques has been driven in large part by significant improvements in the resolution and sensitivity of high-end mass spectrometers. Importantly, the availability of commercial reagents and open source identification/quantification software has made these powerful techniques accessible to nonspecialists. Benefits and drawbacks of the most popular labeling-based approaches are discussed here, and key steps/strategies for the use of labeling in quantitative immunoprecipitation experiments detailed.

Proteomics wants cRacker: automated standardized data analysis of LC-MS derived proteomic data

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100,000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC-MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

A protocol for the identification of protein-protein interactions based on 15N metabolic labeling, immunoprecipitation, quantitative mass spectrometry and affinity modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available¹. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment². Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice³. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown⁴, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)⁵ and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate^5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.

Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for ¹⁵N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein⁷ (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state⁸. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE1⁹.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Determination of monoisotopic masses of chimera spectra from high-resolution mass spectrometric data by use of isotopic peak intensity ratio modeling

RATIONALE

Chimera spectra make it challenging to identify proteins in complex mixtures by LC/MS/MS. Approximately half of the spectra collected are chimera spectra even when high-resolution tandem mass spectrometry is used. Chimera spectra are generated from the co-fragmentation of different co-elute peptides, and it is often difficult to distinguish monoisotopic precursors of these peptides from each other.

METHODS

In this paper, we propose a peak intensity ratio-based monoisotopic peak determination algorithm (PIRMD) to distinguish different monoisotopic precursors of chimera spectra. Monoisotopic peaks in non-overlapping clusters are detected by the edge features of the isotopic peak intensity ratios. For multiple overlapping clusters grouped as one cluster, monoisotopic peaks can be detected by an advanced estimation of the similarity between the estimated and the experimental isotopic distribution based on the isotopic peak intensity ratios.

RESULTS

High-resolution mass spectrometric datasets acquired from mixtures of 30 synthetic peptides and mixtures of 18 proteins were used to evaluate the efficiency and accuracy of PIRMD. The results indicate that PIRMD can recognize monoisotopic precursors from the chimera spectra containing non-overlapping and overlapping isotopic clusters. Compared to several published algorithms, PIRMD identifies approximately 2 ~ 14% more spectra and has fewer false positives.

CONCLUSIONS

The results on standard datasets and actual samples demonstrated that PIRMD could notably improve the successful identification rates of the spectra by identifying more chimera spectra, and of the identified spectra, approximately 25% are chimera spectra. This novel algorithm will help to interpret spectra produced by shotgun strategy in proteomics.

The effect of peptide identification search algorithms on MS2-based label-free protein quantification

Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.

Temporal profiling and pulsed SILAC labeling identify novel secreted proteins during ex vivo osteoblast differentiation of human stromal stem cells

It is well established that bone forming cells (osteoblasts) secrete proteins with autocrine, paracrine, and endocrine function. However, the identity and functional role for the majority of these secreted and differentially expressed proteins during the osteoblast (OB) differentiation process, is not fully established. To address these questions, we quantified the temporal dynamics of the human stromal (mesenchymal, skeletal) stem cell (hMSC) secretome during ex vivo OB differentiation using stable isotope labeling by amino acids in cell culture (SILAC). In addition, we employed pulsed SILAC labeling to distinguish genuine secreted proteins from intracellular contaminants. We identified 466 potentially secreted proteins that were quantified at 5 time-points during 14-days ex vivo OB differentiation including 41 proteins known to be involved in OB functions. Among these, 315 proteins exhibited more than 2-fold up or down-regulation. The pulsed SILAC method revealed a strong correlation between the fraction of isotope labeling and the subset of proteins known to be secreted and involved in OB differentiation. We verified SILAC data using qRT-PCR analysis of 9 identified potential novel regulators of OB differentiation. Furthermore, we studied the biological effects of one of these proteins, the hormone stanniocalcin 2 (STC2) and demonstrated its autocrine effects in enhancing osteoblastic differentiation of hMSC. In conclusion, combining complete and pulsed SILAC labeling facilitated the identification of novel factors produced by hMSC with potential role in OB differentiation. Our study demonstrates that the secretome of osteoblastic cells is more complex than previously reported and supports the emerging evidence that osteoblastic cells secrete proteins with endocrine functions and regulate cellular processes beyond bone formation.

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.

Data extraction from proteomics raw data: an evaluation of nine tandem MS tools using a large Orbitrap data set

In shot-gun proteomics raw tandem MS data are processed with extraction tools to produce condensed peak lists that can be uploaded to database search engines. Many extraction tools are available but to our knowledge, a systematic comparison of such tools has not yet been carried out. Using raw data containing more than 400,000 tandem MS spectra acquired using an Orbitrap Velos we compared 9 tandem MS extraction tools, freely available as well as commercial. We compared the tools with respect to number of extracted MS/MS events, fragment ion information, number of matches, precursor mass accuracies and agreement in-between tools. Processing a primary data set with 9 different tandem MS extraction tools resulted in a low overlap of identified peptides. The tools differ by assigned charge states of precursors, precursor and fragment ion masses, and we show that peptides identified very confidently using one extraction tool might not be matched when using another tool. We also found a bias towards peptides of lower charge state when extracting fragment ion data from higher resolution raw data without deconvolution. Collecting and comparing the extracted data from the same raw data allow adjusting parameters and expectations and selecting the right tool for extraction of tandem MS data.

Quantitative proteomics of primary tumors with varying metastatic capabilities using stable isotope-labeled proteins of multiple histogenic origins

The development of metastasis is a complex, multistep process that remains poorly defined. To identify proteins involved in the colonization phase of the metastatic process, we compared the proteome of tumors derived from inoculation of a panel of isogenic human cancer cell lines with different metastatic capabilities into the mammary fat pad of immunodeficient mice. Using a protein standard generated by SILAC-labeling, a total of 675 proteins were identified and 30 were differentially expressed between at least two of the tumors. The protein standard contained the proteomes of seven cell lines from multiple histogenic origins and displayed superior features compared to standard super-SILAC. The expression of some proteins correlated with metastatic capabilities, such as myosin-9 (nonmuscle myosin II A) and L-lactate dehydrogenase A, while the expression of elongation factor tu correlated inversely to metastatic capabilities. The expression of these proteins was biochemically validated, and expression of myosin-9 in clinical breast cancer samples was further shown to be altered in primary tumors versus corresponding lymph node metastasis. Our study demonstrates an improved strategy for quantitative comparison of an unlimited number of tumor tissues, and provides novel insights into key proteins associated with the colonization phase of metastasis formation.

Quantitative proteomic analysis of type III secretome of enteropathogenic Escherichia coli reveals an expanded effector repertoire for attaching/effacing bacterial pathogens

Type III secretion systems are central to the pathogenesis and virulence of many important Gram-negative bacterial pathogens, and elucidation of the secretion mechanism and identification of the secreted substrates are critical to our understanding of their pathogenic mechanisms and developing potential therapeutics. Stable isotope labeling with amino acids in cell culture-based mass spectrometry is a quantitative and highly sensitive proteomics tool that we have previously used to successfully analyze the type III secretomes of Citrobacter rodentium and Salmonella enterica serovar Typhimurium. In this report, stable isotope labeling with amino acids in cell culture was used to analyze the type III secretome of enteropathogenic Escherichia coli (EPEC), an important human pathogen, which, together with enterohemorrhagic E. coli and C. rodentium, represents the family of attaching and effacing bacterial pathogens. We not only confirmed all 25 known EPEC type III-secreted proteins and effectors previously identified by conventional molecular and bioinformatical techniques but also identified several new type III-secreted proteins, including two novel effectors, C_0814/NleJ and LifA, that were shown to be translocated into host cells. LifA is a known virulence factor believed to act as a toxin as well as an adhesin, but its mechanism of secretion and function is not understood. With a predicted molecular mass of 366 kDa, LifA is the largest type III effector identified thus far in any pathogen. We further demonstrated that Efa1, ToxB, and Z4332 (homologs of LifA in enterohemorrhagic E. coli) are also type III effectors. This study has comprehensively characterized the type III secretome of EPEC, expanded the repertoire of type III-secreted effectors for the attaching and effacing pathogens, and provided new insights into the mode of function for LifA/Efa1/ToxB/Z4332, an important family of virulence factors.

Diffprot - software for non-parametric statistical analysis of differential proteomics data

Mass spectrometry-based global proteomics experiments generate large sets of data that can be converted into useful information only with an appropriate statistical approach. We present Diffprot - a software tool for statistical analysis of MS-derived quantitative data. With implemented resampling-based statistical test and local variance estimate, Diffprot allows to draw significant results from small scale experiments and effectively eliminates false positive results. To demonstrate the advantages of this software, we performed two spike-in tests with complex biological matrices, one label-free and one based on iTRAQ quantification; in addition, we performed an iTRAQ experiment on bacterial samples. In the spike-in tests, protein ratios were estimated and were in good agreement with theoretical values; statistical significance was assigned to spiked proteins and single or no false positive results were obtained with Diffprot. We compared the performance of Diffprot with other statistical tests - widely used t-test and non-parametric Wilcoxon test. In contrast to Diffprot, both generated many false positive hits in the spike-in experiment. This proved the superiority of the resampling-based method in terms of specificity, making Diffprot a rational choice for small scale high-throughput experiments, when the need to control the false positive rate is particularly pressing.

Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches

This study reports the comprehensive comparison of (15)N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the (15)N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments.