Portal de Periódicos da CAPES

Lysate Microarrays Enable High-throughput, Quantitative Investigations of Cellular Signaling

2011; Elsevier BV; Volume: 10; Issue: 4 Linguagem: Inglês

10.1074/mcp.m110.005363

1535-9484

Mark Sevecka, Alejandro Wolf‐Yadlin, Gavin MacBeath,

Gene expression and cancer classification

Lysate microarrays (reverse-phase protein arrays) hold great promise as a tool for systems-level investigations of signaling and multiplexed analyses of disease biomarkers. To date, however, widespread use of this technology has been limited by questions concerning data quality and the specificity of detection reagents. To address these concerns, we developed a strategy to identify high-quality reagents for use with lysate microarrays. In total, we tested 383 antibodies for their ability to quantify changes in protein abundance or modification in 20 biological contexts across 17 cell lines. Antibodies yielding significant differences in signal were further evaluated by immunoblotting and 82 passed our rigorous criteria. The large-scale data set from our screen revealed that cell fate decisions are encoded not just by the identities of proteins that are activated, but by differences in their signaling dynamics as well. Overall, our list of validated antibodies and associated protocols establish lysate microarrays as a robust tool for systems biology. Lysate microarrays (reverse-phase protein arrays) hold great promise as a tool for systems-level investigations of signaling and multiplexed analyses of disease biomarkers. To date, however, widespread use of this technology has been limited by questions concerning data quality and the specificity of detection reagents. To address these concerns, we developed a strategy to identify high-quality reagents for use with lysate microarrays. In total, we tested 383 antibodies for their ability to quantify changes in protein abundance or modification in 20 biological contexts across 17 cell lines. Antibodies yielding significant differences in signal were further evaluated by immunoblotting and 82 passed our rigorous criteria. The large-scale data set from our screen revealed that cell fate decisions are encoded not just by the identities of proteins that are activated, but by differences in their signaling dynamics as well. Overall, our list of validated antibodies and associated protocols establish lysate microarrays as a robust tool for systems biology. One of the primary goals of systems biology is to uncover and model the complex relationships between proteins in living cells and organisms. Data-driven approaches to addressing this problem require ways to obtain quantitative information on protein abundance and post-translational modifications (PTMs) 1The abbreviations used are:PTMpost-translational modificationRTKreceptor tyrosine kinaseTNFαTumor necrosis factor α. in a systematic and high-throughput fashion. Several different immunoaffinity-based methods have been used in systems-level studies to determine the amounts, subcellular locations, and PTM levels of proteins in complex biological samples. Antibody-based technologies that are compatible with multiplexing include flow cytometry (1Hale M.B. Nolan G.P. Phospho-specific flow cytometry: intersection of immunology and biochemistry at the single-cell level.Curr. Opin. Mol. Ther. 2006; 8: 215-224PubMed Google Scholar), microsphere-based assays (2Kellar K.L. Mahmutovic A.J. Bandyopadhyay K. Multiplexed microsphere-based flow cytometric immunoassays.Curr. Protoc. Cytom. 2006; Crossref PubMed Scopus (15) Google Scholar, 3Wu W. Slåstad H. de la Rosa, Carrillo D. Frey T. Tjønnfjord G. Boretti E. Aasheim H.C. Horejsi V. Lund-Johansen F. Antibody array analysis with label-based detection and resolution of protein size.Mol. Cell Proteomics. 2009; 8: 245-257Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar), immunocytochemistry coupled with automated microscopy (4Carpenter A.E. Image-based chemical screening.Nat. Chem. Biol. 2007; 3: 461-465Crossref PubMed Scopus (118) Google Scholar), miniaturized Western blotting (5Ciaccio M.F. Wagner J.P. Chuu C.P. Lauffenburger D.A. Jones R.B. Systems analysis of EGF receptor signaling dynamics with microwestern arrays.Nat. Methods. 2010; 7: 148-155Crossref PubMed Scopus (177) Google Scholar), and antibody microarray-based methods such as direct-detection microarrays (6Haab B.B. Dunham M.J. Brown P.O. Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions.Genome Biol. 2001; 2RESEARCH0004Crossref PubMed Google Scholar, 7Zhou H. Bouwman K. Schotanus M. Verweij C. Marrero J.A. Dillon D. Costa J. Lizardi P. Haab B.B. Two-color, rolling-circle amplification on antibody microarrays for sensitive, multiplexed serum-protein measurements.Genome Biol. 2004; 5: R28Crossref PubMed Google Scholar), sandwich-style microarrays (8Chen S. LaRoche T. Hamelinck D. Bergsma D. Brenner D. Simeone D. Brand R.E. Haab B.B. Multiplexed analysis of glycan variation on native proteins captured by antibody microarrays.Nat. Methods. 2007; 4: 437-444Crossref PubMed Scopus (86) Google Scholar, 9Gembitsky D.S. Lawlor K. Jacovina A. Yaneva M. Tempst P. A prototype antibody microarray platform to monitor changes in protein tyrosine phosphorylation.Mol. Cell Proteomics. 2004; 3: 1102-1118Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar, 10Knickerbocker T. Chen J.R. Thadhani R. MacBeath G. An integrated approach to prognosis using protein microarrays and nonparametric methods.Mol. Syst. Biol. 2007; 3: 123Crossref PubMed Scopus (24) Google Scholar, 11Nielsen U.B. Cardone M.H. Sinskey A.J. MacBeath G. Sorger P.K. Profiling receptor tyrosine kinase activation by using Ab microarrays.Proc. Natl. Acad. Sci. U.S.A. 2003; 100: 9330-9335Crossref PubMed Scopus (202) Google Scholar), and "reverse-phase" or lysate microarrays (12Paweletz C.P. Charboneau L. Bichsel V.E. Simone N.L. Chen T. Gillespie J.W. Emmert-Buck M.R. Roth M.J. Petricoin III, E. F Liotta L.A. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front.Oncogene. 2001; 20: 1981-1989Crossref PubMed Scopus (842) Google Scholar, 13Sevecka M. MacBeath G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.Nat. Methods. 2006; 3: 825-831Crossref PubMed Scopus (81) Google Scholar). Compared with their low-throughput counterparts, high-throughput technologies are often constrained by smaller sample sizes, lack of separation steps, and an inability to tailor the assay to each antibody. To ensure uniformly high data quality across a large number of analytes and biological samples, careful characterization of each antibody is critical. Studies in which the quantitative data are used to train computational models impose an even higher standard. post-translational modification receptor tyrosine kinase Tumor necrosis factor α. Among high-throughput approaches, lysate microarray technology is particularly well suited for systems-level investigations. Thousands of biological specimens can be arrayed onto hundreds of membrane-coated slides, each of which can be queried with a different detection antibody. This format allows dense sampling of information at a protein level and in a high-throughput fashion. Although several groups have used this technology to study biological systems (12Paweletz C.P. Charboneau L. Bichsel V.E. Simone N.L. Chen T. Gillespie J.W. Emmert-Buck M.R. Roth M.J. Petricoin III, E. F Liotta L.A. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front.Oncogene. 2001; 20: 1981-1989Crossref PubMed Scopus (842) Google Scholar, 14Chan S.M. Ermann J. Su L. Fathman C.G. Utz P.J. Protein microarrays for multiplex analysis of signal transduction pathways.Nat. Med. 2004; 10: 1390-1396Crossref PubMed Scopus (183) Google Scholar) and although standardized protocols have been published (15Spurrier B. Ramalingam S. Nishizuka S. Reverse-phase protein lysate microarrays for cell signaling analysis.Nat. Protoc. 2008; 3: 1796-1808Crossref PubMed Scopus (154) Google Scholar), lysate microarrays have not yet gained wide-spread adoption, largely owing to questions regarding data quality and the limited availability of highly validated detection antibodies. Previous studies have recognized the need for rigorous antibody characterization and have used quantitative immunoblotting (Western blotting) to validate large collections of antibodies (13Sevecka M. MacBeath G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.Nat. Methods. 2006; 3: 825-831Crossref PubMed Scopus (81) Google Scholar, 16Spurrier B. Washburn F.L. Asin S. Ramalingam S. Nishizuka S. Antibody screening database for protein kinetic modeling.Proteomics. 2007; 7: 3259-3263Crossref PubMed Scopus (18) Google Scholar). These studies showed that the reactivity of antibodies on lysate microarrays differs from that on traditional immunoblots, even when the same antibodies and lysates are used under otherwise identical conditions. In our own work (13Sevecka M. MacBeath G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.Nat. Methods. 2006; 3: 825-831Crossref PubMed Scopus (81) Google Scholar), which focused on a single cell line, we started with a set of 61 commercial antibodies and found that only 12 of them yielded data on lysate microarrays that matched those collected by quantitative Western blotting. Whereas our approach was successful at discovering functional detection antibodies, it was time-intensive and not easily scaled. It also resulted in a discouragingly small number of antibodies that were validated for use with a single cell line. This highlighted a need to develop a much more efficient strategy to identify suitable antibodies that could be used across a broad range of cell types. Here, we present a novel and efficient way to systematically identify and validate detection antibodies for use with lysate microarrays (see Fig. 1A). A set of relevant candidate antibodies is first chosen within a broad biological area of interest. These antibodies are then screened against a wide variety of "biological contexts" using lysate microarrays. Each context represents a specific combination of cellular background and treatment conditions. Based on the statistical significance of the resulting measurements, promising antibody-context pairs are further evaluated by quantitative Western blotting. If the two data sets agree, the antibody is considered validated for use with that cellular background. Using this strategy, we screened 383 commercial antibodies and successfully validated 82 of them in one or more biological context. This list of antibodies and the associated protocols represents a valuable resource to the scientific community and should facilitate more widespread use of this technology. Although this study focused on characterizing antibodies for lysate microarrays, our overall strategy is general and can be applied to other high-throughput immunoaffinity assays as well. HMEC cells were cultured in HuMEC basal serum-free medium (Invitrogen) supplemented with HuMEC supplement kit (Invitrogen, Carlsbad, CA), 100 I.U./ml penicillin and 100 μg/ml streptomycin (Mediatech, Herndon, VA), and 1 ng/ml cholera toxin (Sigma-Aldrich, St. Louis, MO). Jurkat cells were cultured in RPMI 1640 (Mediatech) supplemented with 10% fetal bovine serum (FBS, HyClone, Logan, UT), 2 mm glutamine (Mediatech), 100 I.U./ml penicillin, and 100 μg/ml streptomycin. HT-29 cells were cultured in McCoy's 5A Medium (ATCC, Manassas, VA) supplemented with 10% FBS, 100 I.U./ml penicillin, and 100 μg/ml streptomycin. All other cell lines were cultured in Dulbecco's modification of Eagle's medium (Mediatech) supplemented with 10% FBS, 2 mm glutamine, 100 I.U./ml penicillin, and 100 μg/ml streptomycin. Medium for FlpIn-293 cell lines additionally contained 150 μg/ml hygromycin B. To generate lysates, cells were serum-starved for 24 h, stimulated with cytokine or small molecule for the prescribed period of time, washed with ice-cold phosphate-buffered saline (PBS), and lysed in 2% SDS buffer. Cell lysates were cleared by filtration through 0.2 μm filter plates (Pall Corporation, East Hills, NY) and stored at −80 °C. Lysate concentrations were determined using the Micro BCA assay kit (Pierce Biotechnology, Rockford, IL) and lysates from each time course treatment were diluted to the same concentration in 2% SDS buffer. To ensure complete protein denaturation, all lysates were then boiled for 5 min at 95 °C prior to microarraying or Western blotting. Detailed information about all cell lysates generated in this study can be found in supplemental Table S1. Custom lysate microarrays were printed by Aushon Biosystems (Billerica, MA) on 16-pad nitrocellulose-coated glass slides (Grace Bio-Labs, Bend, OR). Lysates were arrayed at 250 μm spacing using solid 110 μm pins, which resulted in an average feature diameter of 180 μm when visualizing spot protein content (data not shown). Lysates from time course treatments and unstimulated cell lines were arrayed in technical duplicates. In addition, each array contained six, eight-point, twofold serial dilutions of control lysates (supplemental Experimental Procedures), as well as six spots containing lysis buffer only, for a total of 306 microarray spots per nitrocellulose pad. Following microarray printing, slides were stored dry, in the dark, and at room temperature until further processing. To remove the buffer and detergent contained in each microarray spot, slides were washed three times for 5 min each with 1× PBS/0.1% Tween-20 (PBST), incubated in Tris/HCl (pH 9) for 24 h, washed again with PBST, and centrifuged dry. Silicon gaskets and bottomless 16-well plates (Grace Bio-Labs) were then attached and slides were blocked with 5% BSA/PBST for 1 h at 4 °C. Microarrays were incubated in a mixture of 1:1000 anti-β-actin antibody and 1:500 pan- or phosphospecific antibody in 5% BSA/PBST at 4 °C for 24 h. Following washing, slides were incubated in a mixture of 1:1000 anti-rabbit-680 and 1:1000 anti-mouse-800 antibodies (17Calvert V.S. Tang Y. Boveia V. Wulfkuhle J. Schutz-Geschwender A. Olive D.M. Liotta L.A. Petricoin E.F. Development of multiplexed protein profiling and detection using near infrared detection of reverse-phase protein microarrays.Clin. Proteomics. 2004; 1: 81-90Crossref Google Scholar) in 5% BSA/PBST for 24 h at 4 °C. Silicon gaskets and bottomless plates were removed, slides were washed again, and centrifuged dry. Microarrays were scanned in the 680 nm and 800 nm channels using an Odyssey imager (LI-COR, Lincoln, NE) at 21-μm resolution. Each slide was scanned at a range of scanner sensitivities to account for the large differences in signal intensity between the 16 antibodies tested on each slide. All data processing and analysis steps were carried out using custom-built code for Matlab® 7.4 (The Mathworks, Natick, MA). We first corrected microarray data for nonlinearity using antibody-specific calibration curves, as described previously (13Sevecka M. MacBeath G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.Nat. Methods. 2006; 3: 825-831Crossref PubMed Scopus (81) Google Scholar). Signal intensities within each microarray were then mean-normalized to enable statistical comparisons across different arrays. Signal intensities from target proteins were subsequently normalized using the β-actin signal intensities from the same microarray spots to account for any differences in lysate concentration or spotting. Lastly, data from duplicate spots were averaged. Data from each antibody and biological context were organized into vectors in two separate ways. For the 20 time course treatments, each data vector consisted of six data points, corresponding to the six different time points of treatment. For comparisons across cell lines, each vector consisted of 17 data points, corresponding to the 17 unstimulated cell lines. We calculated two measures of signal up-regulation for each vector: (1) signal difference (ΔI) was defined as the difference between the highest and lowest signal intensity; and (2) fold-up-regulation was calculated as the ratio of the highest and lowest signal intensities in each data vector. To derive a statistical threshold for significant signal difference, ΔIthreshold, histograms of differences in signal intensity were prepared for either biological or analytical replicates in our data set. For biological noise, we made use of the fact that, in several instances, a given cell line was subjected to more than one stimulation condition, but separate "0-min" samples were prepared in each case (supplemental Table S1). For example, we collected time courses of HeLa cells treated with anisomycin, EGF, insulin, and TNFα, but the 0-min time points remained untreated in all four sets of lysates. Taking into account all pairwise combinations within each cell line, our data set contained a total of 16 sets of biological duplicates. For analytical replicate noise, we used data from all duplicate microarray spots. We then plotted the distribution of differences in signal intensity between duplicates, and fitted these data to an exponential distribution. ΔIthreshold was calculated by solving the cumulative distribution function of this exponential for the value 1- αsingle, where αsingle is the significance level for individual comparisons and relates to the significance level for multiple comparison, αmultiple, according to the following relationship: αmultiple = 1 − (1 − αsingle) (n2) (Dunn-Ŝidák correction; n = 6 for "time courses" data set; n = 17 for "cell lines" data set). Hits within the time courses data set were defined as those vectors exceeding ΔIthreshold at αmultiple = 0.01 and showing greater than a 1.5-fold change in signal. Because ΔIthreshold does not capture the systematic variation in signal across cell lines, hits within the cell lines data set were defined as the 50 top-scoring (highest ΔI) non-PTM-specific antibodies. The SOM analysis was performed in Matlab® using the SOM Toolbox (18Vesanto J. Himberg J. Alhoniemi E. Parkankangas J. SOM toolbox for Matlab 5. Technical Report A57. Helsinki University of Technology, Helsinki, Finland2000Google Scholar). The parameters for the SOM analysis were as follows: topology of the map was chosen to be sheet, distance metric was cosine correlation, and the number of map units was chosen to be 66. We used the batch learning algorithm, and the neighborhood function was chosen to be Gaussian with the parameters given by Vesanto et al. (18Vesanto J. Himberg J. Alhoniemi E. Parkankangas J. SOM toolbox for Matlab 5. Technical Report A57. Helsinki University of Technology, Helsinki, Finland2000Google Scholar). We used the U-matrix method to identify a group of map units that represent a cluster (19Ultsch A. Siemon H. Kohonen's self organizing feature maps for exploratory data analysis.in: Int. Neural Network conf. Dordrecht, Netherlands1990: 305-308Google Scholar). For each cluster, we computed statistical significance using a permutation test method (20Hautaniemi S. Yli-Harja O. Astola J. Kauraniemi P. Kallioniemi A. Wolf M. Ruiz J. Mousses S. Kallioniemi O.P. Analysis and visualization of gene expression microarray data in human cancer using self-organizing maps.Machine Learning. 2003; 52: 45-66Crossref Scopus (41) Google Scholar). First, we computed correlation distances for all combinations of time courses in a cluster. If the two profiles correlated perfectly, their distance was assigned to be zero, whereas perfect negative correlation resulted in the distance value of two. We then computed the mean of these pairwise comparisons. This procedure was followed by choosing an equal number of time courses randomly from the entire data set and computing pairwise correlation distances of all combinations. We repeated this process 5000 times and calculated a p value by counting the number of times a randomly chosen cluster produced a mean distance less than or equal to the mean distance of the original cluster, and dividing this number by 5000. Large p values suggest the original cluster may have arisen simply by chance. An antibody can be used with lysate microarrays if it meets two criteria: (1) it produces a significant difference in signal across the samples of interest, and (2) these differences correspond to changes in the levels of the target antigen. We reasoned that an antibody is likely to satisfy these criteria in some biological contexts, but not in others, as its antigen may be abundant in some cells or tissues, but not in others, or its levels may remain unchanged across the available samples in a given experiment. We therefore assumed that antibody validation efforts would ultimately be context-specific, but that some antibodies might perform well in many different settings. To enable rapid and context-dependent assessment of antibody performance, we designed the following high-throughput screen using lysate microarrays (Fig. 1B). Lysates from many different "biological contexts" are microarrayed onto glass-supported nitrocellulose pads and the resulting arrays are assembled into a microtiter plate format (one array per well). Each biological context constitutes a set of related lysates in which a single cell line has been treated for different lengths of time with a molecular stimulus (growth factor or pharmacological agent). Each well is probed with a candidate detection antibody, chosen only on the basis of vendor-supplied information. Following incubating the arrays with an appropriate dye-labeled secondary antibody, the arrays are scanned for fluorescence and the intensities of the microarray spots are quantified. For each combination of detection antibody and biological context, the maximum difference in signal between individual lysates is calculated and this metric is used to separate "hit" from "nonhit" antibodies. To test antibodies for their ability to detect dynamic changes in antigen levels, we started by generating lysates from 20 different biological contexts of interest, each consisting of unstimulated cells (serum-starved) and cells stimulated for five different lengths of time with either a growth factor or a small molecule. We focused on cell lines and treatment conditions that are commonly used in system-level studies of signal transduction, that are easily reproduced, and that span the broad cellular processes of growth, migration, stress response, and apoptosis. In addition to five commercially available cell lines, our set included six isogenic lines derived from HEK 293 cells that each express a different receptor tyrosine kinase (RTK) (21Gordus A. Krall J.A. Beyer E.M. Kaushansky A. Wolf-Yadlin A. Sevecka M. Chang B.H. Rush J. MacBeath G. Linear combinations of docking affinities explain quantitative differences in RTK signaling.Mol. Syst. Biol. 2009; 5: 235Crossref PubMed Scopus (51) Google Scholar). These cell lines were included to assess the ability of antibodies to capture differential activation of the same signaling proteins within the same genetic background. Altogether, these 20 biological contexts included 11 distinct cell lines and provided us with the "time courses" data set (see below). To identify antibodies that detect variations in protein abundance across different cell lines, we included lysates from six additional, untreated lines. Together with the untreated samples from the first 11 cell lines, this set of lysates provided us with the "cell lines" data set (see below). All 21 sets of lysates (representing 126 independent samples) were printed as technical duplicates on glass-supported nitrocellulose pads and assembled into a microtiter plate format. Additional control spots (lysis buffer and dilution series of selected lysates) were included in each microarray to ensure data quality and to enable data processing (see Experimental Procedures). Detailed information about all lysate sets used in this study can be found in supplemental Table S1 available online. To maximize the likelihood of identifying functional antibodies, we focused on antibodies that recognize proteins involved in the cellular processes induced by our treatment conditions: cell growth, proliferation, stress response, and apoptosis. We also used our prior knowledge of network connectivity to refine our choice of antibodies to screen. For example, as several sets of lysates were derived from cells treated with RTK ligands, we included antibodies that report on the activation of proteins in the canonical Ras/MAPK, PI3K/Akt, PLCγ, and STAT signaling pathways (22Marmor M.D. Skaria K.B. Yarden Y. Signal transduction and oncogenesis by ErbB/HER receptors.Int. J. Radiat. Oncol. Biol. Phys. 2004; 58: 903-913Abstract Full Text Full Text PDF PubMed Scopus (320) Google Scholar). In total, 383 commercially available antibodies were obtained for this study (Fig. 1C), 254 of which are PTM-specific and 129 of which recognize both modified and unmodified proteins ("pan-specific"). Among the PTM-specific antibodies, 90 recognize sites of tyrosine phosphorylation (single or multiple), 157 recognize single or multiple phosphorylation sites that include at least one serine or threonine residue, and 7 detect proteolytic cleavage events. To test if antibody performance depends on the host of origin, we included mono- and polyclonal antibodies derived from rabbits, as well as monoclonal antibodies derived from mice. A complete list of all the antibodies used in this study can be found in supplemental Table S2. To assess antibody performance, we probed our lysate microarrays in single wells of microtiter plates with each of the 383 primary antibodies using a single, standardized set of conditions that had previously been optimized (Experimental Procedures). Although it is possible that some antibodies requiring specialized conditions would be missed using this approach, we expect this to be rare as we have not yet encountered any antibodies that yield high quality data under specialized conditions but fail under the general conditions of our optimized protocol. To correct for variation in lysate concentration or microarray spotting, we pooled each antibody with an anti-β-actin antibody derived from a different host species. This provided a way to measure the amount of lysate deposited in each spot. For signal detection, we incubated the microarrays with a mixture of two infrared dye-labeled secondary antibodies (17Calvert V.S. Tang Y. Boveia V. Wulfkuhle J. Schutz-Geschwender A. Olive D.M. Liotta L.A. Petricoin E.F. Development of multiplexed protein profiling and detection using near infrared detection of reverse-phase protein microarrays.Clin. Proteomics. 2004; 1: 81-90Crossref Google Scholar) and scanned the slides in both fluorescent channels (Fig. 2A). This detection strategy substantially reduces assay nonlinearity that is often introduced by methods that rely on enzyme-driven signal amplification. Our screening approach is both economical and scalable: up to 100 antibodies can be tested in parallel, using only 0.2 μl of a 1 mg/ml stock solution to probe each microarray (100 μl volume). Over 200,000 spot intensities were extracted from the microarray images and all subsequent data processing and analysis steps were carried out in an automated fashion using custom-built code (Experimental Procedures). We corrected the data for nonlinearity using antibody-specific calibration curves derived from serial dilutions of control lysates, and normalized all signals relative to their respective β-actin signal intensities (internal standard). To enable statistical comparisons across different arrays, we divided all signal intensities by the mean intensity of each microarray. Finally, we averaged biological duplicates. To capture the performance of each of the 383 antibodies in each of the 21 biological contexts (lysate sets), we organized the microarray data into vectors that contain signal intensities from either the six time points (time courses data set) or 17 cell lines (cell lines data set) for each antibody and lysate set (supplemental Table S3). Our data thus encompass 383 × 21 = 8043 vectors of either 6 or 17 elements. Each vector represents a different antibody-context pair and hence must be evaluated separately for antibody performance. We previously showed that the signal ratio between two samples as measured by lysate microarrays is often smaller than the actual ratio of antigen levels between the two samples (13Sevecka M. MacBeath G. State-based discovery: a multidimensional screen for small-molecule modulators of EGF signaling.Nat. Methods. 2006; 3: 825-831Crossref PubMed Scopus (81) Google Scholar). This is because the lysate microarray signal comprises an antigen-specific component and a component arising from antibody cross-reactivity. If the component arising from cross-reactivity dominates the overall signal, the antigen-specific component is lost in the noise of the assay. Thus, as a first step in validating an antibody-context pair, we first determined the difference, ΔI, between the highest and lowest signal intensities within each data vector, and used ΔI as a metric to separate hit antibody-context pairs from nonhit pairs. Because the microarray data are subject to variation arising from both analytical and biological noise, we expected to observe nonzero values of ΔI for essentially all data vectors. In addition, we reasoned that microarray signals from different cell lines might be subject to systematic variation, as the degree of antibody cross-reactivity likely differs across cell lines. Indeed, we found that over 80% of vectors (6494/8043) exhibited a ΔI that was >10% of the average microarray intensity, and over 97% of vectors (7831/8043) exhibited a ΔI of >1%. In the following analyses, time courses data and cell lines data are treated separately. We will