Quantitative Proteomics Reveals a Dynamic Association of Proteins to Detergent-resistant Membranes upon Elicitor Signaling in Tobacco

A large body of evidence from the past decade supports the existence, in membrane from animal and yeast cells, of functional microdomains playing important roles in protein sorting, signal transduction, or infection by pathogens. In plants, as previously observed for animal microdomains, detergent-resistant fractions, enriched in sphingolipids and sterols, were isolated from plasma membrane. A characterization of their proteic content revealed their enrichment in proteins involved in signaling and response to biotic and abiotic stress and cell trafficking suggesting that these domains were likely to be involved in such physiological processes. In the present study, we used ¹⁴N/¹⁵N metabolic labeling to compare, using a global quantitative proteomics approach, the content of tobacco detergent-resistant membranes extracted from cells treated or not with cryptogein, an elicitor of defense reaction. To analyze the data, we developed a software allowing an automatic quantification of the proteins identified. The results obtained indicate that, although the association to detergent-resistant membranes of most proteins remained unchanged upon cryptogein treatment, five proteins had their relative abundance modified. Four proteins related to cell trafficking (four dynamins) were less abundant in the detergent-resistant membrane fraction after cryptogein treatment, whereas one signaling protein (a 14-3-3 protein) was enriched. This analysis indicates that plant microdomains could, like their animal counterpart, play a role in the early signaling process underlying the setup of defense reaction. Furthermore proteins identified as differentially associated to tobacco detergent-resistant membranes after cryptogein challenge are involved in signaling and vesicular trafficking as already observed in similar studies performed in animal cells upon biological stimuli. This suggests that the ways by which the dynamic association of proteins to microdomains could participate in the regulation of the signaling process may be conserved between plant and animals.The plasma membrane of eukaryotes delineates the interface between the cell and the environment. Thus it is particularly involved in environmental signal recognition and their transduction into intracellular responses, playing a crucial role in many essential functions such as cell nutrition (involving transport of solutes in and out of the cell) or response to environmental modifications (including defense against pathogens).Over the last 10 years, a new aspect of the plasma membrane organization has arisen from biophysical and biochemical studies performed with animal cells. Evidence has been given that the various types of lipids forming this membrane are not uniformly distributed inside the bilayer but rather spatially organized (1). This leads in particular to the formation of specialized phase domains, also called lipid rafts (2, 3). Recently a consensus emerged on the characteristics of these domains. Both proteins and lipids contribute to the formation and the stability of membrane domains that should be called “membrane rafts” and are envisaged as small (10–200-nm), heterogeneous, highly dynamic, sterol- and sphingolipid-enriched domains that compartmentalize cellular processes (4). Small rafts can sometimes be stabilized to form larger platforms through protein-protein and protein-lipid interactions (5). Because of their particular lipidic composition (enrichment in sterol, sphingolipids, and saturated fatty acids), these domains form a liquid ordered phase inside the membrane. This structural characteristic renders them resistant to solubilization by non-ionic detergents, and this property has been widely used to isolate lipid rafts as detergent-resistant membranes (DRMs)¹ for further analysis (1). The most important hypothesis to explain the function of these domains is that they provide for lateral compartmentalization of membrane proteins and thereby create a dynamic scaffold to organize certain cellular processes (5). This ability to temporally and spatially organize protein complexes while excluding others conceivably allows for efficiency and specificity of cellular responses. In yeasts and animal cells, the association of particular proteins with these specialized microdomains has emerged as an important regulator of crucial physiological processes such as signal transduction, polarized secretion, cytoskeletal organization, generation of cell polarity, and entry of infectious organisms in living cells (6). Much of the early evidence for a functional role of lipid rafts came from studies of hematopoietic cells in which multichain immune receptors including the high affinity IgE receptor (FcεRI), the T cell receptor, and the B cell receptor (BCR) translocate to lipid rafts upon cross-linking (7). Moreover this signaling involves the relocalization of several proteins; for instance the ligation of the B cell antigen receptor with antigen induced a dissociation of the adaptor protein ezrin from lipid rafts (8). This release of ezrin acts as a critical trigger that regulates lipid raft dynamics during BCR signaling.In plants, the investigations of the presence of such microdomains are very recent and limited to a reduced number of publications (for a review, see Ref. 9). A few years ago, Peskan et al. (10) reported for the first time the isolation of Triton X-100-insoluble fractions from tobacco plasma membrane. Mongrand et al. (11) provided a detailed analysis of the lipidic composition of such a detergent-resistant fraction indicating that it was highly enriched in a particular species of sphingolipid (glycosylceramide) and in several phytosterols (stigmasterol, sitosterol, 24-methylcholesterol, and cholesterol) compared with the whole plasma membrane from which it originates. Similar results were then obtained with DRMs prepared from Arabidopsis thaliana cell cultures (12) and from Medicago truncatula roots (13). So the presence in plant plasma membrane of domains sharing with animal rafts a particular lipidic composition, namely strong enrichment in sphingolipids together with free sterols and sterol conjugates, the latter being specific to the plant kingdom (11, 13), now seems established. In plant only a few evidences suggest in vivo the role of dynamic clustering of plasma membrane proteins, and they refer to plant-pathogen interaction. A cell biology study reported the pathogen-triggered focal accumulation of components of the plant defense pathway in the plasma membrane (PM), a process reminiscent of lipid rafts (14). Consistently a proteomics study of tobacco DRMs led to the identification of 145 proteins among which a high proportion were linked to signaling in response to biotic stress, cellular trafficking, and cell wall metabolism (15). This suggests that these domains are likely to constitute, as in animal cells, signaling platforms involved in such physiological functions.Cryptogein belongs to a family of low molecular weight proteins secreted by many species of the oomycete Phytophthora named elicitins that induce a hypersensitivity-like response and an acquired resistance in tobacco (16). To understand molecular processes triggered by cryptogein, its effects on tobacco cell suspensions have been studied for several years. Early events following cryptogein treatment include fixation of a sterol molecule (17, 18); binding of the elicitor to a high affinity site located on the plasma membrane (19); alkalinization of the extracellular medium (20); efflux of potassium, chloride, and nitrate (20, 21); fast influx of calcium (22); mitogen-activated protein kinases activation (23, 24); nitric oxide production (25, 26); and development of an oxidative burst (27, 28). We previously identified NtrbohD, an NADPH oxidase located on the plasma membrane, as responsible for the reactive oxygen species (ROS) production occurring a few minutes after challenging tobacco Bright Yellow 2 (BY-2) cells with cryptogein (29). The fact that most of these very early events involve proteins located on the plasma membrane and that one of them, NtrbohD, has been demonstrated as exclusively associated to DRMs in a sterol-dependent manner (30) prompted us to analyze the modifications of DRM proteome after cryptogein treatment. In the present study, we aimed to confirm the hypothesis that, as observed in animal cells, the dynamic association to or exclusion of proteins from lipid rafts could participate in the signaling process occurring during biotic stress in plants.To achieve this goal, we had to set up a quantitative assay allowing a precise comparison of the amounts of each protein in DRMs extracted from either control or cells treated with cryptogein. Among several technologies, we excluded DIGE (31), recently used to analyze whole cell proteome variations in plants (32–34), because membrane proteins are poorly soluble in the detergents used for two-dimensional electrophoresis; this limitation is all the more marked for proteins selected on the basis of their insolubility in non-ionic detergent, the criteria for DRMs isolation. Stable isotope labeling of proteins or peptides combined with MS analysis represents alternative strategies for accurate, relative quantification of proteins on a global scale (35, 36). In these approaches, proteins or peptides of two different samples are differentially labeled with stable isotopes, combined in an equal ratio, and then jointly processed for subsequent MS analysis. Relative quantification of proteins is based on the comparison of signal intensities or peak areas of isotope-coded peptide pairs extracted from the respective mass spectra. Stable isotopes can be introduced either chemically into proteins/peptides via derivatization of distinct functional groups of amino acids or metabolically during protein biosynthesis (for a review, see Ref. 37). Metabolic labeling strategies are based on the in vivo incorporation of stable isotopes during growth of organisms. Nutrients or amino acids in a defined medium are replaced by their isotopically labeled (¹⁵N, ¹³C, or ²H) counterparts eventually resulting in uniform labeling of proteins during the processes of cell growth and protein turnover (38). As a consequence, differentially labeled cells or organisms can be combined directly after harvesting. This minimizes experimental variations due to separate sample handling and thus allows a relative protein quantification of high accuracy.¹⁴N/¹⁵N labeling has been recently proved to be suitable for comparative experiments performed with whole plants (39–42) and in plant suspension cells where the level of incorporation is equal to the isotopic purity of the salt precursor (43, 44). It has been used successfully to analyze some variations induced in A. thaliana plasma membrane proteome following heat shock (45) or cadmium exposure (46) and to compare phosphorylation levels of plasma membrane proteins after challenge of Arabidopsis cells with elicitors of defense reaction (47). In the present study, we used a mineral ¹⁴N/¹⁵N metabolic labeling of tobacco BY-2 cells before treatment with cryptogein and subsequent isolation of DRMs. The DRM proteins were further analyzed by one-dimensional SDS-PAGE and digested by trypsin, and peptides were subjected to microcapillary high performance LC-MS/MS (nano-LC-MS/MS). This metabolic method allowed a complete labeling of the proteome, and consequently a major drawback of this method is probably the difficulty to perform an exhaustive analysis of the very large amount of data generated. To solve this problem, a new quantification module of the MFPaQ software (48) was developed, allowing the automatic quantification of the identified peptides. The results derived from the program were validated through a comparison with manual quantification. Thus, we achieved the complete analysis of the DRM proteome variation and identified four proteins whose abundance in DRMs was decreased and one that was enriched in DRMs upon elicitation. The biological relevance of these results, which indicate that, in plant as in animals, the dynamic association of proteins to membrane domains is part of a signaling pathway, will be further discussed.     

The Fidelity of Dynamic Signaling by Noisy Biomolecular Networks

Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system''s fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.     

Global Effects of Kinase Inhibitors on Signaling Networks Revealed by Quantitative Phosphoproteomics

Aberrant signaling causes many diseases, and manipulating signaling pathways with kinase inhibitors has emerged as a promising area of drug research. Most kinase inhibitors target the conserved ATP-binding pocket; therefore specificity is a major concern. Proteomics has previously been used to identify the direct targets of kinase inhibitors upon affinity purification from cellular extracts. Here we introduce a complementary approach to evaluate the effects of kinase inhibitors on the entire cell signaling network. We used triple labeling SILAC (stable isotope labeling by amino acids in cell culture) to compare cellular phosphorylation levels for control, epidermal growth factor stimulus, and growth factor combined with kinase inhibitors. Of thousands of phosphopeptides, less than 10% had a response pattern indicative of targets of U0126 and SB202190, two widely used MAPK inhibitors. Interestingly, 83% of the growth factor-induced phosphorylation events were affected by either or both inhibitors, showing quantitatively that early signaling processes are predominantly transmitted through the MAPK cascades. In contrast to MAPK inhibitors, dasatinib, a clinical drug directed against BCR-ABL, which is the cause of chronic myelogenous leukemia, affected nearly 1,000 phosphopeptides. In addition to the proximal effects on ABL and its immediate targets, dasatinib broadly affected the downstream MAPK pathways. Pathway mapping of regulated sites implicated a variety of cellular functions, such as chromosome remodeling, RNA splicing, and cytoskeletal organization, some of which have been described in the literature before. Our assay is streamlined and generic and could become a useful tool in kinase drug development.The advent of Gleevec® (imatinib) less than 10 years ago was a landmark for utilizing small molecule compounds as kinase inhibitor drugs (1–3). This type of drug is usually directed against one specific kinase whose malfunctioning plays a key role in the given disease. Generally these drugs are thought to be selective, easy to modify, and effective. As the molecular principles of various diseases are better understood, kinase inhibitors are being developed in various fields with cancer remaining the predominant one (4). Kinase inhibitor compounds constitute about 30% of all drug development programs in the pharmaceutical industry (5).Kinase inhibitor drugs are typically developed with a targeted and “rational” strategy, often focusing on a kinase known to be involved in the etiology of a disease. Large libraries of chemical compounds, for example ATP analogs, are screened in vitro against the activity of this kinase, and their effects on a panel of manually selected kinases with similar sequences or structures are evaluated to assess specificity (6, 7). A few promising leads are then selected for further improvement. In recent years, high throughput technologies have been introduced to speed up these enzyme assays. Innovations include the phage display assay (8, 9), yeast three-hybrid assay (10), and chemical proteomics assay (11, 12). These methods achieve better coverage of the kinome and thus provide less biased results.Although these in vitro assays are very informative, they have several limitations. First, chemical or genetic modifications are often required, such as generating fusion proteins or adding chemical linkers to the inhibitor, which may change the binding properties of the kinases and the inhibitor compounds. Second, these methods investigate the direct binding targets of the inhibitor compounds but do not determine their influence on the entire cellular signaling network. As more and more kinases are proven to function in multiple signaling pathways, inhibitor compounds may influence cellular functions that are not easily predicted. Third, cancer cells are notoriously known to evolve point mutations or to activate alternative signaling proteins to escape drug inhibition (13, 14). Therefore, the concept of utilizing multiple kinase inhibitors is increasingly established in the clinic (15, 16). This has complicated drug evaluation as different inhibitor compounds can generate synergistic or counteracting effects. Certainly a whole cell-based approach, which allows a systems-wide elucidation of inhibitor function, should improve the target evaluation process and help to monitor drug effects in vivo.Increasingly powerful imaging methods can, in principle, provide a comprehensive assessment of signaling pathways. Multiplexed fluorescence provides direct visualization of localization and activities of the selected pathway molecules in vivo after kinase inhibition (17–20). However, imaging methods require hundreds or thousands of experiments to cover all molecules of interest. In contrast, quantitative mass spectrometry is able to measure protein expression and modification events in single experiments at a global level and in a simultaneous manner. Stable isotope labeling by amino acids in cell culture (SILAC)1 generates completely labeled cell populations that are otherwise equal to non-labeled cells (21, 22). This system enables a direct and large-scale comparison of several cell populations with different biological or chemical treatments (23–25). When SILAC was used to study the effect of the HER2 kinase inhibitor PD168393, changes of the tyrosine phosphorylated proteins could be quantified (26). In recent years, studies of phosphorylation at a site-specific level have been greatly enhanced by progress in MS instrumentation and algorithms. Combined with key advances in phosphopeptide enrichment methods, such as immobilized metal ion affinity chromatography (IMAC) and titanium dioxide (TiO₂) chromatography, this has enabled detection and quantitation of thousands of phosphorylation sites, completely changing the capabilities of the phosphoproteomics field (27–34).Chronic myelogenous leukemia (CML) is one of the diseases caused by constitutively active signaling and is characterized by overproliferation of myeloid cells. The fundamental principle of its etiology is the fusion of chromosomes 9 and 22 to produce the so-called Philadelphia chromosome and the constitutively activated tyrosine kinase BCR-ABL fusion protein (35). Treatment of CML has been greatly advanced by small inhibitor compounds that selectively inhibit the kinase activity of BCR-ABL. The striking success of the first BCR-ABL inhibitor drug Gleevec, or imatinib, proved the concept of using small kinase inhibitor compounds as drugs (36). Later, a second generation of drugs was developed to inhibit Gleevec-resistant, point-mutated versions of BCR-ABL. Among these is dasatinib, a highly potent, orally active inhibitor for both inactive and active BCR-ABL that inhibits most BCR-ABL variants found in CML patients (37, 38).Dasatinib binds to the kinase domain of ABL kinase. It has similar potency toward SRC family kinases and the platelet-derived growth factor receptor family (39). Research into the mechanism of action of dasatinib focuses on two major themes: the direct binding targets and more downstream signaling molecules. Mass spectrometry has played an important role in these investigations. Recently Goss et al. (40) analyzed immunoprecipitated proteins by tandem MS and derived a common phosphotyrosine signature for BCR-ABL in six different CML cell lines. Hantschel et al. (41) and Bantscheff et al. (12) combined affinity purification techniques with quantitative MS to screen for binding targets of dasatinib. In the study of Bantscheff et al. (12) several broad band kinase inhibitors were combined and immobilized on one affinity resin (kinobeads). Different kinase inhibitor compounds were then used to compete with the unspecific interaction used in immobilization. The kinase beads covered 65% of the phylogenetic human kinome tree.We reasoned that the combination of SILAC and state of the art phosphoproteomics techniques should provide an excellent tool to explore the effects of kinase inhibitors on individual phosphorylation sites and on the entire cellular network of signal transduction. We first examined two inhibitor compounds widely used in signal transduction laboratories: U0126 inhibits MEK1/2, and SB202190 inhibits p38α/β MAPK. We then applied the same technique to determine the effects of dasatinib, a clinical drug for inhibition of mutated BCR-ABL in CML, on the phosphoproteome.     

微生物蛋白质组学的定量分析

越来越多的微生物基因组序列数据为系统地研究基因的调节和功能创造了有利条件.由于蛋白质是具有生物功能的分子,蛋白质组学在微生物基因组的功能研究中异军突起、蓬勃发展.微生物蛋白质组学的基本原则是,用比较研究来阐明和理解不同微生物之间或不同生长条件下基因的表达水平.显而易见,定量分析技术是比较蛋白质组学中急需发展的核心技术.对蛋白质组学定量分析技术在微生物蛋白质组研究中的进展进行了综述.     

A Quantitative Image Cytometry Technique for Time Series or Population Analyses of Signaling Networks

Background

Modeling of cellular functions on the basis of experimental observation is increasingly common in the field of cellular signaling. However, such modeling requires a large amount of quantitative data of signaling events with high spatio-temporal resolution. A novel technique which allows us to obtain such data is needed for systems biology of cellular signaling.

Methodology/Principal Findings

We developed a fully automatable assay technique, termed quantitative image cytometry (QIC), which integrates a quantitative immunostaining technique and a high precision image-processing algorithm for cell identification. With the aid of an automated sample preparation system, this device can quantify protein expression, phosphorylation and localization with subcellular resolution at one-minute intervals. The signaling activities quantified by the assay system showed good correlation with, as well as comparable reproducibility to, western blot analysis. Taking advantage of the high spatio-temporal resolution, we investigated the signaling dynamics of the ERK pathway in PC12 cells.

Conclusions/Significance

The QIC technique appears as a highly quantitative and versatile technique, which can be a convenient replacement for the most conventional techniques including western blot, flow cytometry and live cell imaging. Thus, the QIC technique can be a powerful tool for investigating the systems biology of cellular signaling.     

Making Large-Scale Networks from fMRI Data

Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.     

Stimulus-dependent Maximum Entropy Models of Neural Population Codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME) model—a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.     

定量蛋白质组学分析方法及应用

蛋白质组学经历了近10年的发展,现在已经初具规模。但是由于它是动态地观察生物体不断变化的所有蛋白质,所以技术难度非常之大。为使研究简化并更具针对性,人们着重进行比较蛋白质组学的研究。为了具体量化这些蛋白质的变化产生了定量蛋白质组学,近几年各种标记技术的进步使得该学科得以迅猛发展。     

Inferring Correlation Networks from Genomic Survey Data

High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world''s oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60% of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.     

Quantitative Proteomics by Metabolic Labeling of Model Organisms

In the biological sciences, model organisms have been used for many decades and have enabled the gathering of a large proportion of our present day knowledge of basic biological processes and their derailments in disease. Although in many of these studies using model organisms, the focus has primarily been on genetics and genomics approaches, it is important that methods become available to extend this to the relevant protein level. Mass spectrometry-based proteomics is increasingly becoming the standard to comprehensively analyze proteomes. An important transition has been made recently by moving from charting static proteomes to monitoring their dynamics by simultaneously quantifying multiple proteins obtained from differently treated samples. Especially the labeling with stable isotopes has proved an effective means to accurately determine differential expression levels of proteins. Among these, metabolic incorporation of stable isotopes in vivo in whole organisms is one of the favored strategies. In this perspective, we will focus on methodologies to stable isotope label a variety of model organisms in vivo, ranging from relatively simple organisms such as bacteria and yeast to Caenorhabditis elegans, Drosophila, and Arabidopsis up to mammals such as rats and mice. We also summarize how this has opened up ways to investigate biological processes at the protein level in health and disease, revealing conservation and variation across the evolutionary tree of life.Well before the genomics era, the foundation for our current understanding of genetics was largely established by biological research performed using model organisms. Early genetics discoveries such as the chromosome theory of heredity and bacterial conjugation were first described in the fruit fly Drosophila melanogaster (1) and the bacterium Escherichia coli (2), respectively. Apart from these organisms, most of the current knowledge of development, evolution, and genetics originates from other classical model organisms including the bakers'' yeast Saccharomyces cerevisiae, the nematode Caenorhabditis elegans, and the mouse Mus musculus. Nowadays, they hold a primary position in the analysis of biological, disease, and pharmaceutical processes in modern biology and probably claim an even more promising position in future biological research. With increasing numbers of completed genome annotations, however, the focus is also shifting somewhat from these classical model organisms toward organisms that have unique genetic properties, are economically interesting, or are more directly related to human disease such as puffer fish, rice, and Plasmodium, respectively. Consequently, the definition of a model organism has broadened over the past decade, and today model organisms are found in nearly all branches of the “tree of life,” providing extensive means to further investigate conservation or diversification of biological principles through evolution (3). This has gained momentum tremendously by the completion of genome sequencing efforts in hundreds of organisms. In relatively simple organisms (bacteria and yeast), this has allowed the systematic investigation of multiple basic biological processes conserved through evolution (e.g. apoptosis (4) and vacuolar transport (5)). Higher organisms are highly useful for the study of complex traits, which is facilitated by large collections of mutant strains (6, 7). This is of particular relevance where model systems of human physiology, either in a healthy or diseased state, are studied. Fruit flies and C. elegans, the “classical” model organisms, have been used as models for a variety of diseases (8) but also for natural processes like aging (9, 10), sleep (11, 12), and olfaction (13). Mouse and rat models have been a long-standing model for human biology (14), especially in cancer (15). Particularly the availability of strains engineered to represent human diseases has increased our understanding of pathological processes tremendously (16).So far, the focus has primarily been on genetic and genomic aspects of these processes and disorders, but with the maturation of proteomics techniques, ways to study these at the protein level in a meaningful way are coming within reach. Over the last decade, proteomics research has experienced significant advances and has evolved into an indispensable technology to investigate the proteomic composition of biological samples. Proteomics has shifted from the analysis of small sets of proteins toward the comprehensive investigation of a much larger number of proteins expressed in a cell, tissue, or organism (17). Nowadays, a typical proteomics experiment is peptide-centric and starts with the enzymatic digestion of a protein mixture followed by fractionation using one or more chromatographic steps to reduce sample complexity (18, 19) as illustrated in Fig.1. Peptides are fragmented in the mass spectrometer as they elute, and subsequent matching of fragmentation profiles against a protein database leads to peptide and protein identification. When performed at a large scale, this can be used for the identification of thousands of proteins in cells or subcellular structures (20–23). Although such qualitative approaches are fruitful in providing information on proteins present in cells or tissues, they largely ignore the dynamics of protein expression when different conditions are to be investigated. This is highly relevant because in general only the proteins that differ between biological states (e.g. healthy/diseased) are likely to be of primary interest. Because mass spectrometry is not inherently quantitative, it is beneficial to add an internal standard as a reference for the peptide of interest. For large scale experiments, often all proteins or peptides in one sample are modified with a stable isotope-coded mass label. After mixing the labeled sample with an unmodified sample, the intensity ratio between the modified peptide and the unlabeled peptide accurately reflects the change in expression level.Open in a separate windowFig. 1.Qualitative proteomics work flow. Proteins are extracted, digested, and separated by strong cation exchange. Each strong cation exchange fraction is then analyzed by nano-LC-MS/MS. Peptide fragment spectra are used in a database search to identify the peptide sequence and the corresponding protein.Various approaches have been developed for the incorporation of stable isotopes into proteins that can be divided into in vivo and in vitro methods. In the former, isotope-enriched compounds (salts or amino acids) are added to the growth media that can be metabolized by the cell and incorporated into proteins. In vitro labeling can be established using chemical derivatization of proteins or peptides after protein extraction. The choice for either approach depends on the biological system under investigation, but there are a few considerations that should be taken into account because of their impact on the experimental work flow. In