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REVIEW ARTICLE Antibody-based proteomics Analysis of signaling networks using reverse protein arrays Hans Voshol 1 , Markus Ehrat 2 , Jens Traenkle 2 , Eric Bertrand 1 and Jan van Oostrum 2 1 Novartis Institutes for BioMedical Research, Basel, Switzerland 2 Zeptosens [a division of Bayer (Schweiz) AG], Witterswil, Switzerland Introduction Significant progress has been made during the last dec- ade in linking pathological conditions to defects in molecular pathway components. Most prominent has been the linkage of signalling pathway dysregulation to conditions such as cancer [1] and inflammatory dis- orders [2]. Understanding the information flow through the various pathways within a signaling network, and how these pathways can best be manipu- lated to redirect signal transduction, is a challenging endeavor. A first step would be to describe the full complexity of signaling networks at a molecular level, including activities specific to a particular cell type, dynamic feedback mechanisms, pathway cross-talk, signaling kinetics and, of course, pathway activation states in normal and disease situations [3]. Even though both kinases and phosphatases are key regula- tors in signaling pathways, across the pharmaceutical industry it is primarily kinases on which a substantial percentage of drug-discovery efforts are currently focused. For a ‘kinase pathway’, the information flow (or pathway flux) mostly depends on the ratio of phos- phorylated and nonphosphorylated protein species, reflecting the activation state of the biological system. Comparing cellular activity over time, at various stages of disease progression or before or after drug treat- ment, provides an opportunity to find a correlation between the activation state, on the one hand, and the biological or disease state, on the other hand. Small molecules that modulate the activity of signal- ing proteins are useful tools for dissecting the func- tional roles and connections of the individual nodes in a pathway [4]. Using such a ‘systems approach’, one can begin to build a model that will not only provide Keywords antibodies; pathways; phosphoproteomics; protein arrays; signalling networks Correspondence J. van Oostrum, Zeptosens, Benkenstrasse 254, CH-4108 Witterswil, Switzerland Fax: +41 61 726 81 70 Tel: +41 61 726 81 87 E-mail: jan.van_oostrum@zeptosens.com (Received 11 June 2009, revised 16 September 2009, accepted 22 September 2009) doi:10.1111/j.1742-4658.2009.07395.x Protein kinases drive the cellular signal transduction networks that underlie the regulation of growth, survival and differentiation. To repair the deregu- lations of signaling cascades that are associated with numerous disease states, therapeutic strategies, based on controlling aberrant protein kinase activity, are emerging. To develop such therapies it is crucial to have knowledge of the full complexity of signaling networks at a molecular level in order to understand the information flow through signaling cascades and their cell and tissue specificity. Antibody-based proteomic approaches (such as reverse-phase protein microarrays) are a powerful tool for using to obtain those signaling maps, through the study of phosphorylation states of pathway components using antibodies that specifically recognize the phosphorylated form of kinase substrates. Abbreviations ERK, extracellular signal-regulated kinase; MEK, mitogen-activated protein kinase/ERK kinase; RPA, reverse (phase) protein (micro)array. FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS 6871 a contextual understanding of the molecular mecha- nisms of disease, but also has the potential to facilitate the validation of therapeutic modulation of regulatory networks [5,6]. A direct benefit of such an approach would be the early recognition of ‘off target’ and side effects of drug candidates [7], as well as the identifica- tion of putative biomarkers. The phosphorylation status of signaling pathway components can be measured using anti-phosphopro- tein Igs that specifically recognize their phosphorylated isoforms. Thus, the activity status of multiple signaling pathways or networks can be probed through parallel phospho-specific analyses. While producing thousands of western blots could (in theory) accomplish this, only protein microarrays enable truly multiplexed analysis by replicating the same sample many times on separate arrays. This type of array, in which a protein extract is immobilized and queried with antibodies or other reagents that bind to a specific protein in the sample, is often referred to as a reverse (phase) protein micro- array [8–10]. Signaling pathways in health and disease Fundamental cellular processes are under tight control of signaling pathways, many of which are highly con- served across species [11]. Prominent players in these signaling cascades are protein kinases and phosphata- ses, which control the activation state of signaling pathways by the modulation of phosphorylation on Tyr, Ser and Thr residues on each other and on a vari- ety of downstream effectors. Aberrant cellular signal- ing is a hallmark of many diseases, and consequently there is a substantial interest in developing drugs that can modulate and ⁄ or repair defects in signaling path- ways. In general, drug discovery faces many challenges, with perhaps the failure of drug candidates during the development process (e.g. as a result of adverse effects or lack of efficacy) being the most prominent one. This high attrition rate may reflect the fact that we are only just beginning to understand the complexity of the response of a biological system to perturbations like a disease state or drug treatment. Hence, a deeper insight into the molecular mechanisms underlying both disease processes and drug action will ultimately contribute to an increased productivity of the drug-dis- covery process [11–13]. In many compound development projects, different assay systems are used for the selection and character- ization of kinase inhibitors. Primarily, compounds are selected with biochemical, cell-free assays using puri- fied recombinant kinases and artificial peptide sub- strates. Cell-based assays are used as a secondary screening step to validate the biological activity of selected compounds against the native kinases in their natural environment. For kinase inhibitors, these screens usually comprise the detection of one or a few phosphorylated proteins that are directly related to the action of the targeted kinase. Because of the inherent potential promiscuity of kinase inhibitors, a more extensive characterization of compound activities across a wide range of signaling pathways and their components is desirable to select the inhibitors with the appropriate profile [7,14]. Drugs that inhibit kinases have recently entered the market, the most spectacular example being imatinib (Gleevec ⁄ Glivec), which inhibits the constitutive kinase activity of the Bcr–Abl fusion protein, the product of a chromosomal translocation in patients suffering from chronic myelogenous leukemia. Several other kinase inhibitors are already on the market, for exam- ple erlotinib (Tarceva) and gefitinib (Iressa), both of which block the epidermal growth factor receptor tyrosine kinase. Tyrosine kinase inhibitors act by inhibiting the activation of intracellular signal trans- duction, typically by blockage of the ATP-binding site in the catalytic domain of the kinase. As the majority of kinase inhibitors that are discovered by high- throughput screening methods target the ATP-binding pocket of the kinase, there is an inherent selectivity problem. Even though these ATP-binding sites, and the surrounding areas, differ between kinases, there is enough similarity to make it very difficult, if not impossible, to develop a fully selective compound for each target. While at first glance this lack of specific- ity, or ‘promiscuity’ [15], appears to be an obstacle for developing a successful drug, in the case of imatinib it has resulted in approval for the treatment of gastroin- testinal stromal tumors, owing to its inhibitory activity against c-kit, the protein tyrosine kinase that is dys- regulated in that cancer. Nevertheless, profiling kinase inhibitors for potential secondary targets is an essen- tial and integral part of drug development so that compounds with a desired (usually narrow) target pro- file can be prioritized over those that are less ‘clean’ and potentially more risky. From biochemical assays to pathway readouts for kinase activity Profiling of kinase inhibitors can be useful to assess the promiscuity of the inhibitor and how much of a problem that might present. The first published screens of that kind emphasized the importance of Analysis of signaling networks using protein arrays H. Voshol et al. 6872 FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS such assays, showing that many commercial inhibitors were everything but specific for the target they claimed to hit [16,17]. The availability of the human genome and the subsequent prediction of the ‘kinome’, the complete set of some 500 human kinases [18], have further accelerated the development of kinase panels. Currently, such collections comprise over 250 kinases; in other words, around half of the human kinome. While the interaction with, and inhibition of, kinases in vitro is one important aspect of profiling com- pounds, it does not take into account the complexity of the in vivo situation, where compounds encounter a wide variety of potential interactors, both kinases and nonkinases. Consequently, cellular assays, preferably in a cell type that is a reasonable proxy for the targeted popu- lation, are preferred. Here, two basic approaches have emerged. On the one hand there are the ‘chemical proteomics’ (or chemoproteomics) approaches, which determine protein–compound interaction rather than measuring actual inhibition of the enzymatic activity. In chemical proteomics, the kinase inhibitor in ques- tion is immobilized and used as ‘bait’ to fish for interacting proteins in cell or tissue lysates [19]. Alternatively, tracing the functional effects of compounds in cellular systems can be studied using pathway proteomics approaches (Fig. 1). The last, and undoubtedly most important, piece of the puzzle when it comes to determining selectivity and specificity of kinase inhibitors, or ultimately any class of drugs, is to develop a meaningful assay for the physiological effect of a compound as early as possible in drug discovery. Ideally such an assay would com- bine readouts for the two most relevant parameters that determine the potential of a compound as a drug, namely (a) the on-target activity as the primary mea- sure for pharmacological efficacy and (b) the off-target effects, which should be minimized in order to obtain a ‘clean’ compound with the smallest risk of side effects. As the example of imatinib has shown, on-tar- get and off-target effects do not always correlate so strictly with beneficial and adverse effects as one would like [20]. Nevertheless, understanding and discriminat- ing the physiological changes that are inevitable (i.e. because of interaction with the primary target) from those that might be undesirable and avoidable is a key asset in selecting the best possible compound. To achieve that, the choice of a suitable set of readouts with the appropriate resolution or granularity is criti- cal. One could resort to monitoring as many individual cellular components – genes, proteins, metabolites – as possible. Arguably, signaling pathways are currently the best practical translation of ‘physiology’ because they allow a sufficient level of granularity while inte- grating the basic organizing principles of the cellular machinery at the same time [11]. Multiple assay formats have evolved for measuring pathway activity, varying in information content and throughput. The principle that all these assays have in common is that they attempt to measure the relative activation state of proteins in pathways. Because in the majority of cases these proteins are kinases and their activity is regulated by phosphorylation, most assay formats are based on multiplexed or parallel detection of specific phosphorylation sites. The phosphorylation status of signaling pathway components can be measured using anti-phosphopro- tein Igs that specifically recognize the phosphorylated isoforms of such kinase substrates [5,21]. For example, quantitative data sets measuring the effects of kinase inhibition on the phosphorylation status of pathway components may be obtained using a compound titra- tion series (Fig. 1). A concentration–response analysis allows the calculation of the half maximum effective concentration (EC 50 ) as a reliable quantitative descrip- tion of the phosphorylation changes. Thus, the activity status of multiple signaling pathways can be probed through parallel phosphospecific analysis. Besides the laborious western blot, which allows only a limited throughput, the current gold standard for this purpose is the sandwich ELISA, which is available in many custom or commercial formats. Sandwich ELISAs have the disadvantage that a carefully matched pair of antibodies must be developed if one aims for site-spe- cific analysis. Moreover, the (peptide) epitopes that have been used for the generation of antibodies are not always accessible in the native (nondenatured) protein. Recently, lysate arrays have emerged as an alterna- tive to the sandwich (or forward) assay format [10]. This type of array, in which a protein extract is immo- bilized and queried with antibodies or other reagents that bind to a specific protein in the sample, is often referred to as reverse (phase) protein (micro)array (RPA). The term ‘reverse’ serves to contrast the lysate array format with ‘forward arrays’, namely those in which the capture reagent (e.g. the antibody) is immo- bilized [22,23]. Among the first applications of RPA were microarrays of tissue lysates to study many proteins in microdissected biopsies [8]. RPAs have been used for several years in their most basic form, the dot-blot, in which drops of cell or tis- sue extract are applied to a membrane or a coated glass slide. Among the different proteomics technolo- gies that are suitable for that purpose, we employ a reverse array platform utilizing the planar waveguide H. Voshol et al. Analysis of signaling networks using protein arrays FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS 6873 technology that allows the detection of a minimum of 1000–2000 molecules present in a single spot, where the total protein content of a single spot corresponds to that of a single cell [9]. Cells or tissue samples are subjected to a one-step extraction using denaturing conditions, under which the potentially labile protein phosphorylations are effectively ‘frozen’, rendering most peptide epitopes accessible and making it rela- tively easy to translate antibody validation by western blotting into an array format. Although RPAs have gained popularity as an alter- native to classical western blots because of a dramati- cally increased throughput, the major bottleneck, as with most antibody-based proteomics approaches, remains the validation of antibodies, which should be highly specific and should not cross-react with any other protein in the cell lysate. As the quality of the antibodies is key to the successful application of RPAs, significant effort is required to ensure their validation before they are applied. Analysis of signaling pathways using RPAs In our view, RPAs currently provide the best array- based pathway analysis platform when information content and flexibility are the primary criteria. In con- trast to classical (forward) arrays, reverse arrays are, in fact, high-throughput dot-blots: small droplets of the complete protein extract are spotted onto a hydro- phobic surface, which retains the proteins by adhesion. Usually, whole tissue or cell extracts, corresponding to the whole cellular proteome, are spotted. RPA approaches often utilize denaturing extraction buffer systems [24,25], which have the important advantage that artifacts caused by inappropriate enzyme activity during extraction can be largely avoided [26]. In this regard, forward array systems are more challenging because the capture step is typically carried out under native conditions, with the inherent risks of artificial changes in the extracts, particularly in labile phospho- MEK Erk Ras/Raf MEKMEK p90RSK Erk p90RSK EGFR AB Fig. 1. Monitoring of downstream cell sig- naling effects upon kinase inhibition. A375 cells were treated with increasing concen- trations of a Raf inhibitor. Cells were lysed and the phosphorylation levels of the down- stream pathway elements, mitogen-acti- vated protein kinase/ERK kinase (MEK), extracellular signal-regulated kinase (ERK) and p90RSK were monitored using phos- phospecific antibodies on reverse protein arrays. By plotting the percentage activity (inhibition of phosphorylation) versus inhibi- tor concentration one can derive half maxi- mum effective concentration (EC 50 )-like data for each pathway element from such experi- ments. Western blots of replicate samples are shown together with the quantitative EC 50 curves. RFI, referenced fluorescence intensity. Analysis of signaling networks using protein arrays H. Voshol et al. 6874 FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS rylations. Analogous to western blots and dot-blots, nitrocellulose was originally used as the carrier for RPAs, albeit in the form of nitrocellulose-coated chips [8], to facilitate handling and scanning. In efforts to obtain the ideal surface, which should combine high protein binding with low intrinsic background [24], other materials and techniques have been explored, including methodology derived from semiconductor fabrication [24]. Arguably the most advanced, commer- cially available, substrate for reverse arrays is the ZeptoCHIP. This device combines a hydrophobic coat- ing (to ensure firm adhesion of proteins) with planar waveguide technology, which allows for enhanced sen- sitivity compared with conventional scanners [27]. The principle of the planar waveguide is to propagate the laser light of the scanner along the chip surface, result- ing in minimal background from nonsurface-bound molecules (Fig. 2A). Because proteins interact with this reverse array surface in a noncovalent manner, the actual composition of the spots depends on the com- position of the lysates, the lysis buffer and the ‘affinity’ of the individual proteins for the solid phase. Varia- tions in lysis and spotting buffers, and in protein concentrations, will influence the make-up of the immobilized ‘proteome’ [25,26]. Consequently, samples with a few highly abundant proteins, such as serum or plasma, are more challenging to deal with than cell or tissue lysates, where the dynamic range is less extreme. Even though limited multiplexing is possible (e.g. using antibodies with different fluorescent dyes), reverse arrays are typically not used in a multiplexing mode but mostly with only a single antibody per array. This circumvents some of the issues inherent to multiplexing as it is often used in forward arrays [24]. Because it is straightforward to print many identical arrays, RPAs nevertheless allow testing of a practically unlimited number of antibodies in a parallel set up. ZeptoCHIPs, which use the planar waveguide tech- nology for improved sensitivity, are made of planar waveguides consisting of a thin film (150 nm) of mate- rial with a high refractive index (e.g. Ta 2 O 5 ), which is deposited on a transparent support with a lower refractive index, typically glass. A laser light beam is coupled into the waveguiding film by a diffractive grat- ing that is etched into the glass. The light propagates within this film and creates a strong evanescent field, Separation of excitation and detection directions Signal/noise = 2/1 Signal/noise = 200/1 Confocal excitation Evanescent excitation A B Evanescent excitation intensity Confocal excitation intensity PWG principle C Fig. 2. (A) Planar waveguide (PWG) principle; optical scheme of excitation and detection of surface confined fluorescence on planar wave- guide chips. (B) Fluorescence excitation schemes for conventional, confocal scanning leading to background contributions, and for evanes- cent, surface-confined illumination using planar waveguides resulting in reduced background signals. (C) Comparison of signal-to-noise ratios from the same microarray obtained by confocal scanning and surface-confined PWG-based fluorescent detection. H. Voshol et al. Analysis of signaling networks using protein arrays FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS 6875 perpendicular to the direction of propagation, into the adjacent medium (Fig. 2A). Upon fluorescence excita- tion by the evanescent field, excitation and detection of fluorophores is restricted to the sensing surface, while signals from unbound molecules in the bulk solution are not detected (Fig. 2b). This results in a significant increase in the signal-to-noise ratio compared with conventional optical detection methods (Fig. 2c). A typical microarray is a ZeptoCHIP, which has space for six arrays, each comprising 352 spots (Fig. 3). Each array has four columns of spots that are used to calibrate the energy loss when the light travels across the waveguide. Typically, 32 samples are spotted in four dilutions (ensuring one remains always within the linear part of the binding curve, see above) and in duplicate. Each array is probed with a single antibody. Each spot will have a volume of around 0.5 nL (with a diameter of 100 lm) and will contain the amount of protein contained in a single cell. Spotting is performed using a noncontact piezo-electric spotter (inkjet tech- nology), with a spotting capacity of about 360 arrays (enabling probing with 360 antibodies) in one overnight spotting run. After printing, the chips are blocked with albumin (as done for western blots) and can be stored in this blocked state for longer than 1 year at 4 °C. RPA applications The RPA approach was pioneered, among others, by Liotta and Petricoin [8], driven by the need for an analytical tool to identify signaling proteins in minute tissue samples, such as those derived from laser-cap- ture microdissection. Hence, RPA followed an atypical trajectory, in a sense the opposite of most bioanalytical tools, which are usually extensively used in model systems before being applied on animal or human tissue samples. Initial clinical applications were aiming particularly at screening pathway-activation states in tumor tissue samples, using antibodies specific for phosphorylated proteins. The underlying hypothesis of those experiments was that differences in these path- way profiles could help to stratify patients and predict response to drug treatment. Several studies have indeed provided indications that this could be a prom- ising avenue. For example, when studying fine needle aspirates from breast tumors using RPA, Rapkiewicz et al. [28] observed modulation of survival ⁄ apoptosis and growth factor pathways as a function of previous chemotherapy treatment. However, elucidation of signaling pathways (i.e. understanding how the signal is transmitted through the cell and what the critical ‘nodes’ are), requires the inves- tigation of (cellular) model systems. RPA applications in model systems (e.g. compound profiling in cell-based models) are only just starting to emerge [7]. Whereas a cell-based (in vitro) system can easily be manipulated to increase the amplitude of the signal, in many in vivo situations the effects of altered signaling will be attenuated by steady-state compensatory mechanisms. Molecular profiling with gene arrays has provided insight into the expression levels for genes in a variety of normal and diseased tissue specimens. However, gene transcript levels do not necessarily correlate with protein expression levels and, more importantly, do e.g sample #1 Controls Column with reference spots A B 0 50 100 150 Reference Reference Fluorescence Distance (pixel) Line profile (as depicted in image) Fig. 3. (A) Illustration of one of the six arrays on a ZeptoCHIP. On a single array, 32 samples are spotted in four different concentra- tions and in duplicate. (B) Scan of an array cross-section showing the signal linearity for the four different sample concentrations. Analysis of signaling networks using protein arrays H. Voshol et al. 6876 FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS not provide insight into the levels of post-translational modifications of proteins, such as phoshorylation events, that play a significant role in cellular signaling networks. Investigations into the protein and phospho- protein expression levels in normal tissue would be a prerequisite to understand the alterations in signaling that are observed in diseased tissues and ⁄ or are caused by in vivo treatment with specific compounds. Mouse models are particularly useful for studying the effects of compounds in complex organisms, helping to gain new knowledge about the molecular mechanisms of disease. The creation of a pathway activity atlas for canonical signaling events would begin with the estab- lishment of the expression levels of proteins involved in various organs or tissues. Figure 4A represents a small excerpt from data derived from quantitative pro- tein expression profiling using RPAs in a collection of tissues derived from C57 ⁄ Bl6 mice. Tissues were extracted in a 10-fold excess of Zeptosens CLB1 buffer using a Teflon Potter (8–10 strokes at 800 rpm) fol- lowed by centrifugation (20 min at 100 000 g)to remove insoluble material. Tissue lysates were stored at )80 °C before performing RPA analysis, as described previously [8]. The range of protein expression values within a group of animals will provide a range of normal values for protein expression for the corresponding organs and tissues. Figure 4B underlines the extent of biologi- cal variation in the expression levels of proteins and their phosphorylated forms between two different tis- pAkt (Ser473) 0 200 400 600 800 1000 1200 1400 Muscle Heart Liver Pancreas Kidney Colon Intestine Stomach Prostate Frontal cortex Hippocampus Amygdala Hypothalamus Cerebellum Thalamus Intensity P44/42 MAP kinase 0 2000 4000 6000 8000 10 000 12 000 14 000 Intensity pMEK1/2 (Ser 217/221) 0 1000 2000 3000 4000 5000 6000 Intensity Glycogen synthase 0 1000 2000 3000 4000 5000 6000 7000 8000 Intensity A B 0 500 1000 1500 2000 2500 3000 3500 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 GSK-3β 0 200 400 600 800 1000 1200 1400 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 GSK-3α 0 500 1000 1500 2000 2500 3000 3500 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 p70 S6 Kinase 0 500 1000 1500 2000 2500 3000 3500 4000 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 TSC-2 0 500 1000 1500 2000 2500 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 Phospho-PTEN 0 500 1000 1500 2000 2500 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 Phospho-mTOR 0 1000 2000 3000 4000 5000 6000 7000 8000 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 Phospho-p44/42 MAPK 0 500 1000 1500 2000 2500 3000 Heart 1 Heart 2 Heart 3 Heart 4 Heart 5 Heart 6 Heart 7 Heart 8 Pancreas 1 Pancreas 2 Pancreas 3 Pancreas 4 Pancreas 5 Pancreas 6 Pancreas 7 Pancreas 8 Phospho-β-Catenin Fig. 4. Towards a protein expression atlas. (A) Relative expression levels of selected signaling network components in mouse organs and brain areas. (B) Relative expression levels of selected signaling network components from the heart and pancreas derived from eight C57 ⁄ B6 mice. H. Voshol et al. Analysis of signaling networks using protein arrays FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS 6877 sues within a group of eight different C57 ⁄ Bl6 mice. A second step would involve the measurement of the activity levels of signaling pathways, indicated by, for example, the phospho- ⁄ nonphospho ratios of pathway components. We envisage that ultimately building such activation maps across organs will be highly valuable in the development of drugs that target cellular signal- ing networks. The idea behind this concept, which we refer to as predictive pharmacodynamics, is that the activation map of the targeted pathway could serve to select the optimal intervention point, which is actually likely to differ depending on the target tissue. At the very least, this approach would deliver useful pharma- codynamic markers to monitor the effects of different compounds in the organism. Moreover, at least in the- ory, this methodology for selecting the correct target would have both the benefit of maximizing the thera- peutic effect as well as minimizing adverse events. Conclusion Owing to the high sensitivity and high throughput capability of the RPA approach, it will be feasible to obtain protein expression profiles and signaling path- way information on a wide variety of cell lines and tissue samples. Interesting applications include (a) the comparative analysis of signaling pathway(s) events in normal versus diseased tissue, (b) the comparative analysis of protein expression in various systems, (c) elucidation of the dynamic aspects of pathway events and (d) the profiling of compounds to reveal signaling and cross-pathway effects of drug candidates. In addi- tion, analysis of healthy versus diseased tissue (includ- ing animal models) will provide insights into the underlying pathologies of the pathways and provide a platform for molecular diagnostics. In a future approach, the screening of body fluids with a reverse array approach may enable the investigation of a large number of individual body fluid samples for a limited set of proteins contained within them, to establish variations in protein expression levels. Acknowledgements The authors would like to acknowledge Dr A. van Gool (Schering-Plough, Translational Medicine Research Centre, Singapore) for providing the western blot results shown in Fig. 1. References 1 Blume-Jensen P & Hunter T (2001) Oncogenic kinase signaling. Nature 411, 355–365. 2 Ohashi PS (2002) T-cell signaling and autoimmunity; molecular mechanisms of disease. Nat Rev Immunol 2, 427–438. 3 Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4, 682–690. 4 Sevecka M and MacBeath G (2006) State-based discov- ery: a multidimensional screen for small-molecule mod- ulators of EGF signaling. Nat Methods 3, 825–831. 5 Cho C, Labow M, Reinhardt M, van Oostrum J & Pei- tsch MC (2006) The application of systems biology to drug discovery. Curr Opin Chem Biol 10, 294–302. 6 Jia J, Zhu F, Ma X, Cao ZW, Li YX & Chen YZ (2009) Mechanisms of drug combinations:interactions and network perspectives. Nat Rev Drug Discov 8, 111–128. 7 Van Oostrum J, Calonder C, Rechsteiner D, Ehrat M, Mestan J, Fabbro D & Voshol H (2009) Tracing path- way activities with kinase inhibitors and reverse phase protein arrays. Proteomics Clin Appl 3, 412–422. 8 Pawaletz CP, Charboneau L, Bichsel VE, Simone NL, Chen T, Gillespie JW, Emmert-Buck MR, Roth MJ, Petricoin EF III & Liotta LA (2001) Reverse phase pro- tein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989. 9 Pawlak M, Schick E, Bopp MA, Schneider MJ, Orozlan P & Ehrat M (2002) Zeptosens’ protein microarrays: a novel high performance microarray platform for low abundance protein analysis. Proteomics 2, 383–393. 10 Sheehan KM, Calvert VS, Kay EW, Lu Y, Fishman D, Espina V, Aquino J, Speer R, Araujo R, Mills GB et al. (2005) Use of reverse phase protein microarrays and reference standard develoment for moilecular network analysis of metastatic ovarian carcinoma. Mol Cell Proteomics 4, 346–355. 11 Fishman MC & Porter JA (2005) A new grammar for drug discovery. Nature 437, 491–493. 12 Butcher EC, Berg EL & Kunkel EJ (2004) Systems biology in drug discovery. Nat Biotechnol 22, 1253–1259. 13 Apic G, Ignjatovic T, Boyer S & Russel RB (2005) Illu- minating drug discovery with biological pathways. FEBS Lett 579, 1872–1877. 14 Araujo RP, Liotta LA & Petricoin EF (2007) Proteins, drug targets and the mechanisms they control: the sim- ple truth about complex analysis. Nat Rev Drug Discov 6, 871–880. 15 Zhang J, Yang PL & Gray NS (2009) Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer 9, 28–39. 16 Davies SP, Reddy H, Caivano M & Cohen P (2000) Specificity and mechanism of action of some commonly used protein kinase inhibitors. Biochem J 351, 95–105. 17 Bain J, Plater L, Elliot M, Shpiro N, Hastie CJ, McLauchlan H, Klevernic I, Arthur JSC, Alessi DR & Analysis of signaling networks using protein arrays H. Voshol et al. 6878 FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS Cohen P (2007) The selectivity of protein kinase inhibi- tors. a further update. Biochem J 408, 297–315. 18 Manning G, Whyte DB, Martinez R, Hunter T & Sudarsanam S (2002) The protein kinase complement of the human genome. Science 298, 1912–1934. 19 Daub H, Godl K, Brehmer D, Klebl B & Mu ¨ ller G (2004) Evaluation of kinase inhibitor selectivity by chemical proteomics. Assay Drug Dev Technol 2, 215– 224. 20 Petrelli A & Giordano S (2008) From single- to multi- target drugs in cancer therapy: when aspecificity becomes an advantage. Curr Med Chem 15, 422–432. 21 Mendes KN, Nicorici D, Cogdell D, Tabus I, Yli-Harja O, Guerra R, Hamilton SR & Zhang W (2007) Analysis of signaling pathways in 90 cancer cell lines by protein lysate arrays. J Proteome Res 6, 2753–2767. 22 Borrebaeck CAK & Wingren C (2007) High throughput proteomics using antibody microarrays: an update. Expert Rev Mol Diagn 7, 673–686. 23 Haab BB (2005) Antibody arrays in cancer research. Mol Cell Proteomics 4, 377–383. 24 VanMeter A, Signore M, Pierobon M, Espina V, Liotta LA & Petricoin EF III (2007) Reverse-phase protein microarrays: application to biomarker discovery and translational medicine. Expert Rev Mol Diagn 7, 625–633. 25 Winters M, Dabir B, Yu M & Kohn EC (2007) Constitution and quantity of lysis buffer alters outcome of reverse phase protein microarrays. Proteomics 22, 4066–4068. 26 Gromov P, Celis JE, Gromova I, Rank F, Timmer- mans-Wielinga V & Moreira JMA (2008) A single lysis solution for the analysis of tissue samples by different proteomic technologies. Mol Oncol 2, 368–379. 27 Ghatnekar-Nilsson S, Dexlin L, Wingren C, Montelius L & Borrebaeck CAK (2007) Design of atto-vial based recombinant antibody arrays combined with a planar waveguide detection system. Proteomics 7, 540–547. 28 Rapkiewicz A, Espina V, Zujewski JA, Lebowitz PF, Filie A, Wulfkuhle J, Camphausen K, Petricoin EF III, Liotta LA & Abati A (2007) The needle in the haystack:application of breast fine-needle aspirate samples to quantitative protein microarray technology. Cancer Cytopathol 111, 173–184. H. Voshol et al. Analysis of signaling networks using protein arrays FEBS Journal 276 (2009) 6871–6879 ª 2009 The Authors Journal compilation ª 2009 FEBS 6879 . REVIEW ARTICLE Antibody-based proteomics Analysis of signaling networks using reverse protein arrays Hans Voshol 1 , Markus Ehrat 2 ,. present. The first published screens of that kind emphasized the importance of Analysis of signaling networks using protein arrays H. Voshol et al. 6872 FEBS