Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics.

The rise of systems biology implied a growing demand for highly sensitive techniques for the fast and consistent detection and quantification of target sets of proteins across multiple samples. This is only partly achieved by classical mass spectrometry or affinity-based methods. We applied a targeted proteomics approach based on selected reaction monitoring (SRM) to detect and quantify proteins expressed to a concentration below 50 copies/cell in total S. cerevisiae digests. The detection range can be extended to single-digit copies/cell and to proteins undetected by classical methods. We illustrate the power of the technique by the consistent and fast measurement of a network of proteins spanning the entire abundance range over a growth time course of S. cerevisiae transiting through a series of metabolic phases. We therefore demonstrate the potential of SRM-based proteomics to provide assays for the measurement of any set of proteins of interest in yeast at high-throughput and quantitative accuracy.

Interactome of the amyloid precursor protein APP in brain reveals a protein network involved in synaptic vesicle turnover and a close association with Synaptotagmin-1.

Knowledge of the protein networks interacting with the amyloid precursor protein (APP) in vivo can shed light on the physiological function of APP. To date, most proteins interacting with the APP intracellular domain (AICD) have been identified by Yeast Two Hybrid screens which only detect direct interaction partners. We used a proteomics-based approach by biochemically isolating tagged APP from the brains of transgenic mice and subjecting the affinity-purified complex to mass spectrometric (MS) analysis. Using two different quantitative MS approaches, we compared the protein composition of affinity-purified samples isolated from wild-type mice versus transgenic mice expressing tagged APP. This enabled us to assess truly enriched proteins in the transgenic sample and yielded an overlapping set of proteins containing the major proteins involved in synaptic vesicle endo- and exocytosis. Confocal microscopy analyses of cotransfected primary neurons showed colocalization of APP with synaptic vesicle proteins in vesicular structures throughout the neurites. We analyzed the interaction of APP with these proteins using pulldown experiments from transgenic mice or cotransfected cells followed by Western blotting. Synaptotagmin-1 (Stg1), a resident synaptic vesicle protein, was found to directly bind to APP. We fused Citrine and Cerulean to APP and the candidate proteins and measured fluorescence resonance energy transfer (FRET) in differentiated SH-SY5Y cells. Differentially tagged APPs showed clear sensitized FRET emission, in line with the described dimerization of APP. Among the candidate APP-interacting proteins, again only Stg1 was in close proximity to APP. Our results strongly argue for a function of APP in synaptic vesicle turnover in vivo. Thus, in addition to the APP cleavage product Aß, which influences synaptic transmission at the postsynapse, APP interacts with the calcium sensor of synaptic vesicles and might thus play a role in the regulation of synaptic vesicle exocytosis.

Proteome analysis of Legionella vacuoles purified by magnetic immunoseparation reveals secretory and endosomal GTPases.

Legionella pneumophila, the causative agent of Legionnaires' disease, replicates in macrophages and amoebae within 'Legionella-containing vacuoles' (LCVs), which communicate with the early secretory pathway and the endoplasmic reticulum. Formation of LCVs requires the bacterial Icm/Dot type IV secretion system. The Icm/Dot-translocated effector protein SidC selectively anchors to LCVs by binding the host lipid phosphatidylinositol-4-phosphate (PtdIns(4)P). Here, we describe a novel and simple approach to purify intact vacuoles formed by L. pneumophila within Dictyostelium discoideum by using magnetic immunoseparation with an antibody against SidC, followed by density gradient centrifugation. To monitor LCV purification by fluorescence microscopy, we used Dictyostelium producing the LCV marker calnexin-GFP and L. pneumophila labeled with the red fluorescent protein DsRed. A proteome analysis of purified LCVs by liquid chromatography coupled to tandem mass spectrometry revealed 566 host proteins, including known LCV components, such as the small GTPases Arf1, Rab1 and Rab7. Rab8, an endosomal regulator of the late secretory pathway originating from the trans Golgi network, and the endosomal GTPase Rab14 were identified as novel LCV components, which were found to be present on vacuoles harboring wild-type but not Icm/Dot-deficient L. pneumophila. Thus, LCVs also communicate with the late secretory and endosomal pathways. Depletion of Rab8 or Arf1 by RNA interference reduced the amount of SidC on LCVs, indicating that the GTPases promote the recruitment of Legionella effectors by regulating the level of PtdIns(4)P.

Identification of cross-linked peptides from large sequence databases.

We describe a method to identify cross-linked peptides from complex samples and large protein sequence databases by combining isotopically tagged cross-linkers, chromatographic enrichment, targeted proteomics and a new search engine called xQuest. This software reduces the search space by an upstream candidate-peptide search before the recombination step. We showed that xQuest can identify cross-linked peptides from a total Escherichia coli lysate with an unrestricted database search.

Analysis of cell surface proteome changes via label-free, quantitative mass spectrometry.

We present a mass spectrometry-based strategy for the specific detection and quantification of cell surface proteome changes. The method is based on the label-free quantification of peptide patterns acquired by high mass accuracy mass spectrometry using new software tools and the cell surface capturing technology that selectively enriches glycopeptides exposed to the cell exterior. The method was applied to monitor dynamic protein changes in the cell surface glycoproteome of Drosophila melanogaster cells. The results led to the construction of a cell surface glycoprotein atlas consisting of 202 cell surface glycoproteins of D. melanogaster Kc167 cells and indicated relative quantitative changes of cell surface glycoproteins in four different cellular states. Furthermore we specifically investigated cell surface proteome changes upon prolonged insulin stimulation. The data revealed insulin-dependent cell surface glycoprotein dynamics, including insulin receptor internalization, and linked these changes to intracellular signaling networks.

Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry.

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300-1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

An integrated mass spectrometric and computational framework for the analysis of protein interaction networks.

Biological systems are controlled by protein complexes that associate into dynamic protein interaction networks. We describe a strategy that analyzes protein complexes through the integration of label-free, quantitative mass spectrometry and computational analysis. By evaluating peptide intensity profiles throughout the sequential dilution of samples, the MasterMap system identifies specific interaction partners, detects changes in the composition of protein complexes and reveals variations in the phosphorylation states of components of protein complexes. We use the complexes containing the human forkhead transcription factor FoxO3A to demonstrate the validity and performance of this technology. Our analysis identifies previously known and unknown interactions of FoxO3A with 14-3-3 proteins, in addition to identifying FoxO3A phosphorylation sites and detecting reduced 14-3-3 binding following inhibition of phosphoinositide-3 kinase. By improving specificity and sensitivity of interaction networks, assessing post-translational modifications and providing dynamic interaction profiles, the MasterMap system addresses several limitations of current approaches for protein complexes.

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures.

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

BACKGROUND: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. RESULTS: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. CONCLUSION: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

PhosphoPep--a phosphoproteome resource for systems biology research in Drosophila Kc167 cells.

The ability to analyze and understand the mechanisms by which cells process information is a key question of systems biology research. Such mechanisms critically depend on reversible phosphorylation of cellular proteins, a process that is catalyzed by protein kinases and phosphatases. Here, we present PhosphoPep, a database containing more than 10 000 unique high-confidence phosphorylation sites mapping to nearly 3500 gene models and 4600 distinct phosphoproteins of the Drosophila melanogaster Kc167 cell line. This constitutes the most comprehensive phosphorylation map of any single source to date. To enhance the utility of PhosphoPep, we also provide an array of software tools that allow users to browse through phosphorylation sites on single proteins or pathways, to easily integrate the data with other, external data types such as protein-protein interactions and to search the database via spectral matching. Finally, all data can be readily exported, for example, for targeted proteomics approaches and the data thus generated can be again validated using PhosphoPep, supporting iterative cycles of experimentation and analysis that are typical for systems biology research.

An integrated chemical, mass spectrometric and computational strategy for (quantitative) phosphoproteomics: application to Drosophila melanogaster Kc167 cells.

Current methods for phosphoproteome analysis have several limitations. First, most methods for phosphopeptide enrichment lack the specificity to truly purify phosphopeptides. Second, fragmentation spectra of phosphopeptides, in particular those of phosphoserine and phosphothreonine containing peptides, are often dominated by the loss of the phosphate group(s) and therefore lack the information required to identify the peptide sequence and the site of phosphorylation, and third, sequence database search engines and statistical models for data validation are not optimized for the specific fragmentation properties of phosphorylated peptides. Consequently, phosphoproteomic data are characterized by large and unknown rates of false positive and false negative phosphorylation sites. Here we present an integrated chemical, mass spectrometric and computational strategy to improve the efficiency, specificity and confidence in the identification of phosphopeptides and their site(s) of phosphorylation. Phosphopeptides were isolated with high specificity through a simple derivatization procedure based on phosphoramidate chemistry. Identification of phosphopeptides, their site(s) of phosphorylation and the corresponding phosphoproteins was achieved by the optimization of the mass spectrometric data acquisition procedure, the computational tools for database searching and the data post processing. The strategy was applied to the mapping of phosphorylation sites of a purified transcription factor, dFOXO and for the global analysis of protein phosphorylation of Drosophila melanogaster Kc167 cells.

Analysis of a marine phototrophic biofilm by confocal laser scanning microscopy using the new image quantification software PHLIP.

BACKGROUND: Confocal laser scanning microscopy (CLSM) is the method of choice to study interfacial biofilms and acquires time-resolved three-dimensional data of the biofilm structure. CLSM can be used in a multi-channel modus where the different channels map individual biofilm components. This communication presents a novel image quantification tool, PHLIP, for the quantitative analysis of large amounts of multichannel CLSM data in an automated way. PHLIP can be freely downloaded from http://phlip.sourceforge.net. RESULTS: PHLIP is an open source public license Matlab toolbox that includes functions for CLSM imaging data handling and ten image analysis operations describing various aspects of biofilm morphology. The use of PHLIP is here demonstrated by a study of the development of a natural marine phototrophic biofilm. It is shown how the examination of the individual biofilm components using the multi-channel capability of PHLIP allowed the description of the dynamic spatial and temporal separation of diatoms, bacteria and organic and inorganic matter during the shift from a bacteria-dominated to a diatom-dominated phototrophic biofilm. Reflection images and weight measurements complementing the PHLIP analyses suggest that a large part of the biofilm mass consisted of inorganic mineral material. CONCLUSION: The presented case study reveals new insight into the temporal development of a phototrophic biofilm where multi-channel imaging allowed to parallel monitor the dynamics of the individual biofilm components over time. This application of PHLIP presents the power of biofilm image analysis by multi-channel CLSM software and demonstrates the importance of PHLIP for the scientific community as a flexible and extendable image analysis platform for automated image processing.

An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data.

Over the past decade, a series of experimental strategies for mass spectrometry based quantitative proteomics and corresponding computational methodology for the processing of the resulting data have been generated. We provide here an overview of the main quantification principles and available software solutions for the analysis of data generated by liquid chromatography coupled to mass spectrometry (LC-MS). Three conceptually different methods to perform quantitative LC-MS experiments have been introduced. In the first, quantification is achieved by spectral counting, in the second via differential stable isotopic labeling, and in the third by using the ion current in label-free LC-MS measurements. We discuss here advantages and challenges of each quantification approach and assess available software solutions with respect to their instrument compatibility and processing functionality. This review therefore serves as a starting point for researchers to choose an appropriate software solution for quantitative proteomic experiments based on their experimental and analytical requirements.

Reproducible isolation of distinct, overlapping segments of the phosphoproteome.

The ability to routinely analyze and quantitatively measure changes in protein phosphorylation on a proteome-wide scale is essential for biological and clinical research. We assessed the ability of three common phosphopeptide isolation methods (phosphoramidate chemistry (PAC), immobilized metal affinity chromatography (IMAC) and titanium dioxide) to reproducibly, specifically and comprehensively isolate phosphopeptides from complex mixtures. Phosphopeptides were isolated from aliquots of a tryptic digest of the cytosolic fraction of Drosophila melanogaster Kc167 cells and analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry. Each method reproducibly isolated phosphopeptides. The methods, however, differed in their specificity of isolation and, notably, in the set of phosphopeptides isolated. The results suggest that the three methods detect different, partially overlapping segments of the phosphoproteome and that, at present, no single method is sufficient for a comprehensive phosphoproteome analysis.

SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling.

Label-free quantification of high mass resolution LC-MS data has emerged as a promising technology for proteome analysis. Computational methods are required for the accurate extraction of peptide signals from LC-MS data and the tracking of these features across the measurements of different samples. We present here an open source software tool, SuperHirn, that comprises a set of modules to process LC-MS data acquired on a high resolution mass spectrometer. The program includes newly developed functionalities to analyze LC-MS data such as feature extraction and quantification, LC-MS similarity analysis, LC-MS alignment of multiple datasets, and intensity normalization. These program routines extract profiles of measured features and comprise tools for clustering and classification analysis of the profiles. SuperHirn was applied in an MS1-based profiling approach to a benchmark LC-MS dataset of complex protein mixtures with defined concentration changes. We show that the program automatically detects profiling trends in an unsupervised manner and is able to associate proteins to their correct theoretical dilution profile.