BIOINFORMATICS MEETS PROTEOMICS — BRIDGING THE GAP BETWEEN MASS SPECTROMETRY DATA ANALYSIS AND CELL BIOLOGY

Analysing the nanoparticle-protein corona for potential molecular target identification

When nanoparticles are introduced into biological systems, host proteins tend to associate on the particle surface to form a protein layer termed the "protein corona" (PC). Identifying the proteins that constitute the PC can yield useful information about nanoparticle processing, bio-distribution, toxicity and clearance. Similarly, characterizing and identifying proteins within the PC from patient samples provides opportunities to probe disease proteomes and identify molecules that influence the disease process. Thus, nanoparticles represent unique probing tools for discovery of molecular targets for diseases. Here, we report a first review on target identification using nanoparticles in biological samples based on analysing physico chemical interactions. We also summarize the evolution of the PC surrounding various nano-systems, comment on PC signature, address PC complexity in fluids, and outline challenges associated with analysing the PC. In addition, the influence on PC formation of various nanoparticle parameters is summarized; nanoparticle characteristics considered include size, charge, temperature, and surface modifications for both organic and inorganic nanomaterials. We also discuss the advantages of nanotechnology, over other more invasive and laborious methods, for identifying potential diagnostic and therapeutic targets.

Characterization of the NPC1L1 gene and proteome from an exceptional responder to ezetimibe

BACKGROUND

Strategies to reduce LDL-cholesterol involve reductions in cholesterol synthesis or absorption. We identified a familial hypercholesterolemia patient with an exceptional response to the cholesterol absorption inhibitor, ezetimibe. Niemann-Pick C 1-like 1 (NPC1L1) is the molecular target of ezetimibe.

METHODS AND RESULTS

Sequencing identified nucleotide changes predicted to change amino acids 52 (L52P), 300 (I300T) and 489 (S489G) in exceptional NPC1L1. In silico analyses identified increased stability and cholesterol binding affinity in L52P-NPC1L1 versus WT-NPC1L1. HEK293 cells overexpressing WT-NPC1L1 or NPC1L1 harboring amino acid changes singly or in combination (Comb-NPC1L1) had reduced cholesterol uptake in Comb-NPC1L1 when ezetimibe was present. Cholesterol uptake was reduced by ezetimibe in L52P-NPC1L1, I300T-NPC1L1, but increased in S489G-NPC1L1 overexpressing cells. Immunolocalization studies found preferential plasma membrane localization of mutant NPC1L1 independent of ezetimibe. Flotillin 1 and 2 expression was reduced and binding to Comb-NPC1L1 was reduced independent of ezetimibe exposure. Proteomic analyses identified increased association with proteins that modulate intermediate filament proteins in Comb-NPC1L1 versus WT-NPC1L1 treated with ezetimibe.

CONCLUSION

This is the first detailed analysis of the role of NPC1L1 mutations in an exceptional responder to ezetimibe. The results point to a complex set of events in which the combined mutations were shown to affect cholesterol uptake in the presence of ezetimibe. Proteomic analysis suggests that the exceptional response may also lie in the nature of interactions with cytosolic proteins.

Proteomic-coupled-network analysis of T877A-androgen receptor interactomes can predict clinical prostate cancer outcomes between White (non-Hispanic) and African-American groups

The androgen receptor (AR) remains an important contributor to the neoplastic evolution of prostate cancer (CaP). CaP progression is linked to several somatic AR mutational changes that endow upon the AR dramatic gain-of-function properties. One of the most common somatic mutations identified is Thr877-to-Ala (T877A), located in the ligand-binding domain, that results in a receptor capable of promiscuous binding and activation by a variety of steroid hormones and ligands including estrogens, progestins, glucocorticoids, and several anti-androgens. In an attempt to further define somatic mutated AR gain-of-function properties, as a consequence of its promiscuous ligand binding, we undertook a proteomic/network analysis approach to characterize the protein interactome of the mutant T877A-AR in LNCaP cells under eight different ligand-specific treatments (dihydrotestosterone, mibolerone, R1881, testosterone, estradiol, progesterone, dexamethasone, and cyproterone acetate). In extending the analysis of our multi-ligand complexes of the mutant T877A-AR we observed significant enrichment of specific complexes between normal and primary prostatic tumors, which were furthermore correlated with known clinical outcomes. Further analysis of certain mutant T877A-AR complexes showed specific population preferences distinguishing primary prostatic disease between white (non-Hispanic) vs. African-American males. Moreover, these cancer-related AR-protein complexes demonstrated predictive survival outcomes specific to CaP, and not for breast, lung, lymphoma or medulloblastoma cancers. Our study, by coupling data generated by our proteomics to network analysis of clinical samples, has helped to define real and novel biological pathways in complicated gain-of-function AR complex systems.

The coming of age of phosphoproteomics--from large data sets to inference of protein functions

Protein phosphorylation is one of the most common post-translational modifications used in signal transduction to control cell growth, proliferation, and survival in response to both intracellular and extracellular stimuli. This modification is finely coordinated by a network of kinases and phosphatases that recognize unique sequence motifs and/or mediate their functions through scaffold and adaptor proteins. Detailed information on the nature of kinase substrates and site-specific phosphoregulation is required in order for one to better understand their pathophysiological roles. Recent advances in affinity chromatography and mass spectrometry (MS) sensitivity have enabled the large-scale identification and profiling of protein phosphorylation, but appropriate follow-up experiments are required in order to ascertain the functional significance of identified phosphorylation sites. In this review, we present meaningful technical details for MS-based phosphoproteomic analyses and describe important considerations for the selection of model systems and the functional characterization of identified phosphorylation sites.

Using a spike-in experiment to evaluate analysis of LC-MS data

BACKGROUND

Recent advances in liquid chromatography-mass spectrometry (LC-MS) technology have led to more effective approaches for measuring changes in peptide/protein abundances in biological samples. Label-free LC-MS methods have been used for extraction of quantitative information and for detection of differentially abundant peptides/proteins. However, difference detection by analysis of data derived from label-free LC-MS methods requires various preprocessing steps including filtering, baseline correction, peak detection, alignment, and normalization. Although several specialized tools have been developed to analyze LC-MS data, determining the most appropriate computational pipeline remains challenging partly due to lack of established gold standards.

RESULTS

The work in this paper is an initial study to develop a simple model with "presence" or "absence" condition using spike-in experiments and to be able to identify these "true differences" using available software tools. In addition to the preprocessing pipelines, choosing appropriate statistical tests and determining critical values are important. We observe that individual statistical tests could lead to different results due to different assumptions and employed metrics. It is therefore preferable to incorporate several statistical tests for either exploration or confirmation purpose.

CONCLUSIONS

The LC-MS data from our spike-in experiment can be used for developing and optimizing LC-MS data preprocessing algorithms and to evaluate workflows implemented in existing software tools. Our current work is a stepping stone towards optimizing LC-MS data acquisition and testing the accuracy and validity of computational tools for difference detection in future studies that will be focused on spiking peptides of diverse physicochemical properties in different concentrations to better represent biomarker discovery of differentially abundant peptides/proteins.

DESIGN AND ANALYSIS OF QUANTITATIVE DIFFERENTIAL PROTEOMICS INVESTIGATIONS USING LC-MS TECHNOLOGY

Liquid chromatography–mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS–based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS–based differential quantitation. Supplementary material is available at .

Spectrin-like repeats 11-15 of human dystrophin show adaptations to a lipidic environment

Dystrophin is essential to skeletal muscle function and confers resistance to the sarcolemma by interacting with cytoskeleton and membrane. In the present work, we characterized the behavior of dystrophin 11-15 (DYS R11-15), five spectrin-like repeats from the central domain of human dystrophin, with lipids. DYS R11-15 displays an amphiphilic character at the liquid/air interface while maintaining its secondary α-helical structure. The interaction of DYS R11-15 with small unilamellar vesicles (SUVs) depends on the lipid nature, which is not the case with large unilamellar vesicles (LUVs). In addition, switching from anionic SUVs to anionic LUVs suggests the lipid packing as a crucial factor for the interaction of protein and lipid. The monolayer model and the modulation of surface pressure aim to mimic the muscle at work (i.e. dynamic changes of muscle membrane during contraction and relaxation) (high and low surface pressure). Strikingly, the lateral pressure modifies the protein organization. Increasing the lateral pressure leads the proteins to be organized in a regular network. Nevertheless, a different protein conformation after its binding to monolayer is revealed by trypsin proteolysis. Label-free quantification by nano-LC/MS/MS allowed identification of the helices in repeats 12 and 13 involved in the interaction with anionic SUVs. These results, combined with our previous studies, indicate that DYS R11-15 constitutes the only part of dystrophin that interacts with anionic as well as zwitterionic lipids and adapts its interaction and organization depending on lipid packing and lipid nature. We provide strong experimental evidence for a physiological role of the central domain of dystrophin in sarcolemma scaffolding through modulation of lipid-protein interactions.

Use of colloidal silica-beads for the isolation of cell-surface proteins for mass spectrometry-based proteomics

Chaney and Jacobson first introduced the colloidal silica-bead protocol for the coating of cellular plasma membranes in the early 1980s. Since then, this method has been successfully incorporated into a wide range of in vitro and in vivo applications for the isolation of cell-surface proteins. The principle is simple - cationic colloidal silica microbeads are introduced to a suspension or monolayer of cells in culture. Electrostatic interactions between the beads and the negatively charged plasma membrane, followed by cross-linking to the membrane with an anionic polymer, ensure attachment and maintain the native protein conformation. Cells are subsequently ruptured, and segregation of the resulting plasma membrane sheets from the remaining- cell constituents is achieved by ultracentrifugation through density gradients. The resulting membrane-bead pellet is treated with various detergents or chaotropic agents (i.e., urea) to elute bound proteins. If proteomic profiling by mass spectrometry is desired, proteins are denatured, carbamidomethylated, and digested into peptides prior to chromatography.

Subcellular phosphoproteomics

Protein phosphorylation represents one of the most extensively studied post-translational modifications, primarily due to the emergence of sensitive methods enabling the detection of this modification both in vitro and in vivo. The availability of enrichment methods combined with sensitive mass spectrometry instrumentation has played a crucial role in uncovering the dynamic changes and the large expanding repertoire of this reversible modification. The structural changes imparted by the phosphorylation of specific residues afford exquisite mechanisms for the regulation of protein functions by modulating new binding sites on scaffold proteins or by abrogating protein-protein interactions. However, the dynamic interplay of protein phosphorylation is not occurring randomly within the cell but is rather finely orchestrated by specific kinases and phosphatases that are unevenly distributed across subcellular compartments. This spatial separation not only regulates protein phosphorylation but can also control the activity of other enzymes and the transfer of other post-translational modifications. While numerous large-scale phosphoproteomics studies highlighted the extent and diversity of phosphoproteins present in total cell lysates, the further understanding of their regulation and biological activities require a spatio-temporal resolution only achievable through subcellular fractionation. This review presents a first account of the emerging field of subcellular phosphoproteomics where cell fractionation approaches are combined with sensitive mass spectrometry methods to facilitate the identification of low abundance proteins and to unravel the intricate regulation of protein phosphorylation.

Proteomics: challenges, techniques and possibilities to overcome biological sample complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.

LC-MS Based Detection of Differential Protein Expression

While several techniques are available in proteomics, LC-MS based analysis of complex protein/peptide mixtures has turned out to be a mainstream analytical technique for quantitative proteomics. Significant technical advances at both sample preparation/separation and mass spectrometry levels have revolutionized comprehensive proteome analysis. Moreover, automation and robotics for sample handling process permit multiple sampling with high throughput.For LC-MS based quantitative proteomics, sample preparation turns out to be critical step, as it can significantly influence sensitivity of downstream analysis. Several sample preparation strategies exist, including depletion of high abundant proteins or enrichment steps that facilitate protein quantification but with a compromise of focusing on a smaller subset of a proteome. While several experimental strategies have emerged, certain limitations such as physiochemical properties of a peptide/protein, protein turnover in a sample, analytical platform used for sample analysis and data processing, still imply challenges to quantitative proteomics. Other aspects that make analysis of a proteome a challenging task include dynamic nature of a proteome, need for efficient and fast analysis of protein due to its constant modifications inside a cell, concentration range of proteins that exceed dynamic range of a single analytical method, and absence of appropriate bioinformatics tools for analysis of large volume and high dimensional data.This paper gives an overview of various LC-MS methods currently used in quantitative proteomics and their potential for detecting differential protein expression. Fundamental steps such as sample preparation, LC separation, mass spectrometry, quantitative assessment and protein identification are discussed.For quantitative assessment of protein expression, both label and label free approaches are evaluated for their set of merits and demerits. While most of these methods edge on providing "relative abundance" information, absolute quantification is achieved with limitation as it caters to fewer proteins. Isotope labeling is extensively used for quantifying differentially expressed proteins, but is severely limited by successful incorporation of its heavy label. Lengthy labeling protocols restrict the number of samples that can be labeled and processed. Alternatively, label free approach appears promising as it can process many samples with any number of comparisons possible but entails reproducible experimental data for its application.

Enhanced sensitivity in proteomics experiments using FAIMS coupled with a hybrid linear ion trap/Orbitrap mass spectrometer

We describe the use and application of high-field asymmetric waveform ion mobility spectrometry combined with nanoscale liquid chromatography mass spectrometry (nanoLC-FAIMS-MS) to improve the sensitivity and dynamic range of proteomics analyses on a hybrid LTQ-Orbitrap mass spectrometer. The ability of FAIMS to enrich multiply protonated peptides against background ions confers a marked advantage in proteomics analyses by decreasing the limits of detection to facilitate the identification of low-abundance peptide ions. These multiply charged ions are recorded into separate acquisition channels to enhance the overall population of detectable peptide ions from a single analysis. NanoLC-FAIMS-MS experiments performed on peptides spiked into complex proteins digests provided more than 10-fold improvement in limits of detection compared to conventional nanoelectrospray mass spectrometry. This enhancement of sensitivity is reflected by a 55% increase in the number of assigned MS/MS spectra contributing to an overall improvement in protein identification and sequence coverage. The application of FAIMS in label-free quantitative proteomics is demonstrated for the identification of differentially abundant proteins from human U937 monocytic cells exposed to phorbol ester.

The MHC class I peptide repertoire is molded by the transcriptome

Under steady-state conditions, major histocompatibility complex (MHC) I molecules are associated with self-peptides that are collectively referred to as the MHC class I peptide (MIP) repertoire. Very little is known about the genesis and molecular composition of the MIP repertoire. We developed a novel high-throughput mass spectrometry approach that yields an accurate definition of the nature and relative abundance of unlabeled peptides presented by MHC I molecules. We identified 189 and 196 MHC I-associated peptides from normal and neoplastic mouse thymocytes, respectively. By integrating our peptidomic data with global profiling of the transcriptome, we reached two conclusions. The MIP repertoire of primary mouse thymocytes is biased toward peptides derived from highly abundant transcripts and is enriched in peptides derived from cyclins/cyclin-dependent kinases and helicases. Furthermore, we found that approximately 25% of MHC I-associated peptides were differentially expressed on normal versus neoplastic thymocytes. Approximately half of those peptides are derived from molecules directly implicated in neoplastic transformation (e.g., components of the PI3K-AKT-mTOR pathway). In most cases, overexpression of MHC I peptides on cancer cells entailed posttranscriptional mechanisms. Our results show that high-throughput analysis and sequencing of MHC I-associated peptides yields unique insights into the genesis of the MIP repertoire in normal and neoplastic cells.

Improved model-based, platform-independent feature extraction for mass spectrometry

MOTIVATION

Mass spectrometry (MS) is increasingly being used for biomedical research. The typical analysis of MS data consists of several steps. Feature extraction is a crucial step since subsequent analyses are performed only on the detected features. Current methodologies applied to low-resolution MS, in which features are peaks or wavelet functions, are parameter-sensitive and inaccurate in the sense that peaks and wavelet functions do not directly correspond to the underlying molecules under observation. In high-resolution MS, the model-based approach is more appealing as it can provide a better representation of the MS signals by incorporating information about peak shapes and isotopic distributions. Current model-based techniques are computationally expensive; various algorithms have been proposed to improve the computational efficiency of this paradigm. However, these methods cannot deal well with overlapping features, especially when they are merged to create one broad peak. In addition, no method has been proven to perform well across different MS platforms.

RESULTS

We suggest a new model-based approach to feature extraction in which spectra are decomposed into a mixture of distributions derived from peptide models. By incorporating kernel-based smoothing and perceptual similarity for matching distributions, our statistical framework improves existing methodologies in terms of computational efficiency and the accuracy of the results. Our model is parameterized by physical properties and is therefore applicable to different MS instruments and settings. We validate our approach on simulated data, and show that the performance is higher than commonly used tools on real high- and low-resolution MS, and MS/MS data sets.

Improvement in peptide detection for proteomics analyses using NanoLC-MS and high-field asymmetry waveform ion mobility mass spectrometry

Sensitive and selective detection of multiply charged peptide ions from complex tryptic digests was achieved using high-field asymmetric waveform ion mobility spectrometry (FAIMS) combined with nanoscale liquid chromatography-mass spectrometry (nanoLC-FAIMS-MS). The combination of FAIMS provided a marked advantage over conventional nanoLC-MS experiments by reducing the extent of chemical noise associated with singly charged ions and enhancing the overall population of detectable tryptic peptides. Such advantages were evidenced by a 6-12-fold improvement in signal-to-noise ratio measurements for a wide range of multiply charged peptide ions. An increase of 20% in the number of detected peptides compared to conventional nanoelectrospray was achieved by transmitting ions of different mobilities at high electric field vs low field while simultaneously recording each ion population in separate mass spectrometry acquisition channels. This method provided excellent reproducibility across replicate nanoLC-FAIMS-MS runs with more than 90% of all detected peptide ions showing less than 30% variation in intensity. The application of this technique in the context of proteomics research is demonstrated for the identification of trace-level proteins showing differential expression in U937 monocyte cell extracts following incubation with phorbol ester.

A comparison of the HUPO Brain Proteome Project pilot with other proteomics studies

The pilot phase of the Brain Proteome Project (BPP), the Human Proteome Organization (HUPO) initiative that focuses on studies of the brain of both humans and mice, has now been completed. Participating laboratories studied the proteomes of two human samples derived from biopsy and autopsy as well as three mouse samples from various developmental stages. With the combined and centrally reprocessed data now available, a comparison in terms of protein identifications and project organization is made between the HUPO BPP pilot and three other proteomics studies: the HUPO Plasma Proteome Project (PPP) pilot, a proteome of human blood platelets and a recently published comprehensive mouse proteome. Finally, as any comparison between large-scale proteomics datasets is decidedly non-trivial, we also evaluate and discuss several ways to go about comparing such different result sets.

Informatics solutions for high-throughput proteomics

The success of mass-spectrometry-based proteomics as a method for analyzing proteins in biological samples is accompanied by challenges owning to demands for increased throughput. These challenges arise from the vast volume of data generated by proteomics experiments combined with the heterogeneity in data formats, processing methods, software tools and databases that are involved in the translation of spectral data into relevant and actionable information for scientists. Informatics aims to provide answers to these challenges by transferring existing solutions from information management to proteomics and/or by generating novel computational methods for automation of proteomics data processing.

Robust Algorithm for Alignment of Liquid Chromatography−Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline

Liquid chromatography coupled to mass spectrometry (LC-MS) and tandem mass spectrometry (LC-MS/MS) has become a standard technique for analyzing complex peptide mixtures to determine composition and relative abundance. Several high-throughput proteomics techniques attempt to combine complementary results from multiple LC-MS and LC-MS/MS analyses to provide more comprehensive and accurate results. To effectively collate and use results from these techniques, variations in mass and elution time measurements between related analyses need to be corrected using algorithms designed to align the various types of data: LC-MS/MS versus LC-MS/MS, LC-MS versus LC-MS/MS, and LC-MS versus LC-MS. Described herein are new algorithms referred to collectively as liquid chromatography-based mass spectrometric warping and alignment of retention times of peptides (LCMSWARP), which use a dynamic elution time warping approach similar to traditional algorithms that correct for variations in LC elution times using piecewise linear functions. LCMSWARP is compared to the equivalent approach based upon linear transformation of elution times. LCMSWARP additionally corrects for temporal drift in mass measurement accuracies. We also describe the alignment of LC-MS results and demonstrate their application to the alignment of analyses from different chromatographic systems, showing the suitability of the present approach for more complex transformations.

Proteomic analysis of cartilage- and bone-associated samples

The skeleton of the human body is built of cartilage and bone, which are tissues that contain extensive amounts of extracellular matrix (ECM). In bone, inorganic mineral hydroxyapatite forms 50-70% of the whole weight of the tissue. Although the organic matrix of bone consists of numerous proteins, 90% of it is composed of type I collagen. In cartilage, ECM forms a major fraction of the tissue, type II collagen and aggrecans being the most abundant macromolecules. It is obvious that the high content of ECM components causes analytical problems in the proteomic analysis of cartilage and bone, analogous to those in the analysis of low-abundance proteins present in serum. The massive contents of carbohydrates present in cartilage proteoglycans, and hydroxyapatite in bone, further complicate the situation. However, the development of proteomic tools makes them more and more tempting also for research of musculoskeletal tissues. Application of proteomic techniques to the research of chondrocytes, osteoblasts, osteocytes, and osteoclasts in cell cultures can immediately benefit from the present knowledge. Here we make an overview to previous proteomic research of cartilage- and bone-associated samples and evaluate the future prospects of applying proteomic techniques to investigate key events, such as cellular signal transduction, in cartilage- and bone-derived cells.

Challenges and opportunities in proteomics data analysis

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.

Integrated microfluidic devices with enhanced separation performance: Application to phosphoproteome analyses of differentiated cell model systems

This work reports on the application of a microfluidic device integrating nanoscale LC to nanoelectrospray MS (nano-LC-chip-MS) for the analysis of complex protein digests. Peak profile analyses of more than 700 peptide ions, reproducibly detected across replicate nano-LC-chip-MS runs (n = 5), indicated that the system provided RSD values of 0.24% on retention time, +/- 30 ppm on m/z measurement and +/- 30% variation on intensity over three orders of magnitude. RP adsorbant media with different alkyl chains and particle size packed in both trapping and separation channels were investigated to improve the chromatographic performance of this system. A two-fold improvement in chromatographic peak capacity was achieved using microfluidic devices comprising a 5 mircrom C3 trap with 2.5 microm C18 trap separation channel compared to the traditional 5 microm C18 stationary phase. Enhanced sample selectivity for the identification of phosphopeptides was obtained by combining immobilized metal affinity media prior to peptide separation on the RP microfluidic device. This system was evaluated in the context of differential phosphoproteome analyses to identify changes in signaling events and protein expression of human monocytes following the administration of phorbol ester.

High-throughput proteomics using matrix-assisted laser desorption/ ionization mass spectrometry

It has become evident that the mystery of life will not be deciphered just by decoding its blueprint, the genetic code. In the life and biomedical sciences, research efforts are now shifting from pure gene analysis to the analysis of all biomolecules involved in the machinery of life. One area of these postgenomic research fields is proteomics. Although proteomics, which basically encompasses the analysis of proteins, is not a new concept, it is far from being a research field that can rely on routine and large-scale analyses. At the time the term proteomics was coined, a gold-rush mentality was created, promising vast and quick riches (i.e., solutions to the immensely complex questions of life and disease). Predictably, the reality has been quite different. The complexity of proteomes and the wide variations in the abundances and chemical properties of their constituents has rendered the use of systematic analytical approaches only partially successful, and biologically meaningful results have been slow to arrive. However, to learn more about how cells and, hence, life works, it is essential to understand the proteins and their complex interactions in their native environment. This is why proteomics will be an important part of the biomedical sciences for the foreseeable future. Therefore, any advances in providing the tools that make protein analysis a more routine and large-scale business, ideally using automated and rapid analytical procedures, are highly sought after. This review will provide some basics, thoughts and ideas on the exploitation of matrix-assisted laser desorption/ ionization in biological mass spectrometry - one of the most commonly used analytical tools in proteomics - for high-throughput analyses.

Advances in proteomics data analysis and display using an accurate mass and time tag approach

Proteomics has recently demonstrated utility for increasing the understanding of cellular processes on the molecular level as a component of systems biology approaches and for identifying potential biomarkers of various disease states. The large amount of data generated by utilizing high efficiency (e.g., chromatographic) separations coupled with high mass accuracy mass spectrometry for high-throughput proteomics analyses presents challenges related to data processing, analysis, and display. This review focuses on recent advances in nanoLC-FTICR-MS-based proteomics approaches and the accompanying data processing tools that have been developed to display and interpret the large volumes of data being produced.

Mass spectrometry and protein analysis

Mass spectrometry is a central analytical technique for protein research and for the study of biomolecules in general. Driven by the need to identify, characterize, and quantify proteins at ever increasing sensitivity and in ever more complex samples, a wide range of new mass spectrometry-based analytical platforms and experimental strategies have emerged. Here we review recent advances in mass spectrometry instrumentation in the context of current and emerging research strategies in protein science.

SELDI-TOF-MS proteomics of breast cancer

The detection, diagnosis, and management of breast cancer rely on an integrated approach using clinical history, physical examination, imaging, and histopathology. The discovery and validation of novel biomarkers will aid the physician in more effectively achieving this integration. This review discusses efforts in surface-enhanced laser desorption/ionization (SELDI)-based proteomics to address various clinical questions surrounding breast cancer, including diagnosis, monitoring, and stratification for treatment. Emphasis is placed on examining how study design and execution influence the discovery and validation process, which is critical to the proper development of potential clinical tests.

Multiplex multidimensional nanoLC-MS system for targeted proteomic analyses

The present work describes a dual-column and dual-sprayer LC-MS system for high-throughput proteomic analyses. This system consists of two precolumns for sample desalting and two analytical columns. Each column is terminated by a nanoelectrospray emitter mounted on a robotic arm enabling their sequential positioning in front of the sampling cone of the mass spectrometer. The effluent from each emitter is recorded in separate acquisition channels without detectable crosstalk. Gradient elution to both nanoLC columns is delivered by a single HPLC system via a flow splitter. The reproducibility of retention time and peak intensity of the present multiplex system were comparable to those obtainable using a single emitter configuration. Replicate injections of complex tryptic digests (n = 10) indicated that this system provided good reproducibility of retention time and peak intensity on both columns with RSD values of less than 0.9 and 18.6%, respectively. The application of this system is demonstrated for the monitoring of protein expression changes in U937 human monocyte cells with and without phorbol ester administration. Furthermore, we also demonstrated the use of this multiplex system in a 2-D LC configuration to increase sample loading and throughput for the analysis of biomarker samples of higher complexity. Variations in peptide abundance down to two-fold change were identified across salt fractions for spiked tryptic digests present at a level of 50 fmol in 1.5 microg of plasma samples.

An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis

MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false-positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed-sequence search as demonstrated in this study based on a validated data set. The availability of open-source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of "consensus scoring", i.e., the use of multiple (at least two) search algorithms to reduce the number of FPs. complement.

DBToolkit: processing protein databases for peptide-centric proteomics

DBToolkit is a user-friendly, easily extensible tool that allows the processing of protein sequence databases to peptide-centric sequence databases. This processing is primarily aimed at enhancing the useful information content of these databases for use as optimized search spaces for efficient identification of peptide fragmentation spectra obtained by mass spectrometry. In addition, DBToolkit can be used to reliably solve a range of other typical tasks in processing sequence databases.

AVAILABILITY

DBToolkit is open source under the GNU GPL license. The source code, full user and developer documentation and cross-platform binaries are freely downloadable from the project website at http://genesis.UGent.be/dbtoolkit/

CONTACT

lennart.martens@UGent.be

Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry

The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection, peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing, and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits and limitations, and propose areas deserving of further research.

Integrated Microfluidic Device for Mass Spectrometry-Based Proteomics and Its Application to Biomarker Discovery Programs

The present investigation describes the analytical performances of a microfluidic device comprising an enrichment column, a reversed-phase separation channel, and a nanoelectrospray emitter embedded altogether in polyimide layers. This configuration minimizes transfer lines and connections and reduces postcolumn peak broadening and dead volumes. This compact and versatile modular nanoLC-chip system was interfaced to both ion trap and time-of-flight mass spectrometers, and its analytical potentials were evaluated in the context of proteomics applications. The figures of merit of this system in terms of peak capacity, reproducibility, sensitivity, and linear dynamic range of peptide detection were determined using tryptic digests of complex protein extracts including albumin- and immunoglobulin-depleted rat plasma samples. The analysis of peak profiles for more than 600 peptide ions reproducibly detected across replicate nanoLC-chip-MS runs (n = 10) indicated that this system provided good reproducibility of retention time and peak intensity with RSD values of less than 0.5 and 9.1%, respectively. Variation in peptide abundance as low as 2-fold changes was identified for spiked tryptic digests present at levels of 2-5 fmol in plasma samples. Sensitivity measurements were performed on dilution series of protein digests spiked into rat plasma samples and provided a detection limit of 1-5 fmol. The modular concept of the microfluidic systems also facilitated the integration of two-dimensional chromatography (strong cation exchange/C18) thereby increasing the sample loading and selectivity of the nanoLC-chip-MS system. The application of this integrated device was evaluated for complex rat plasma samples to compare the number of protein identifications obtained using one- and two-dimensional nanoLC-chip-MS/MS.

The potentials of MS-based subproteomic approaches in medical science: the case of lysosomes and breast cancer

Because of the great number of women who are diagnosed with breast cancer each year, and though this disease presents the lowest mortality rate among cancers, breast cancer remains a major public health problem. As for any cancer, the tumorigenic and metastatic processes are still hardly understood, and the biochemical markers that allow either a precise monitoring of the disease or the classification of the numerous forms of breast cancer remain too scarce. Therefore, great hopes are put on the development of high-throughput genomic and proteomic technologies. Such comprehensive techniques should help in understanding the processes and in defining steps of the disease by depicting specific genes or protein profiles. Because techniques dedicated to the current proteomic challenges are continuously improving, the probability of the discovery of new potential protein biomarkers is rapidly increasing. In addition, the identification of such markers should be eased by lowering the sample complexity; e.g., by sample fractionation, either according to specific physico-chemical properties of the proteins, or by focusing on definite subcellular compartments. In particular, proteins of the lysosomal compartment have been shown to be prone to alterations in their localization, expression, or post-translational modifications (PTMs) during the cancer process. Some of them, such as the aspartic protease cathepsin D (CatD), have even been proven as participating actively in the disease progression. The present review aims at giving an overview of the implication of the lysosome in breast cancer, and at showing how subproteomics and the constantly refining MS-based proteomic techniques may help in making breast cancer research progress, and thus, hopefully, in improving disease treatment.

Integration of Jacobson's Pellicle Method into Proteomic Strategies for Plasma Membrane Proteins

A modified form of the cationic colloidal silica technique for plasma membrane isolation has been combined with SDS-PAGE, mass spectrometry, and bioinformatics for evaluation as a proteomics strategy with human multiple myeloma cells and human breast cancer cells. On the basis of Western blots, half of the protein isolated is estimated to come from the plasma membrane. Forty-three percent of the 366 proteins identified by mass spectrometry had been previously classified as plasma membrane proteins. Thirty proteins previously categorized as hypothetical membrane proteins are now reported to be expressed.

Linking protein fractionation with multidimensional monolithic reversed-phase peptide chromatography/mass spectrometry enhances protein identification from complex mixtures even in the presence of abundant proteins

Recently, multidimensional shotgun proteomics has proven to be an alternative technology able to identify hundreds of proteins from single samples. Two major limitations of the technology are the presence of high abundance proteins (e.g. RUBISCO in plant leaf tissue) and the enormous number of co-eluting peptides that overstrain the loading and resolving capacity of conventional particle-packed columns as well as the capacity of electrospray ionisation due to ion suppression. Here, the coupling of fast performance liquid chromatography (FPLC) pre-fractionation of an Arabidopsis leaf protein extract and subsequent two-dimensional liquid chromatography/mass spectrometry with improved resolution using a monolithic silica C18 capillary column allowed the identification of 1032 unique proteins in a single 4 mg total protein plant leaf tissue sample. The reassignment of peptide IDs to distinct FPLC protein fractions enhances the identification procedure, especially in the case of present protein isoforms. The proposed strategy is useful to detect proteins otherwise not seen in conventional multidimensional chromatography/mass spectrometry approaches.