Secretome profiling with antibody microarrays

Following the advances in human genome sequencing, attention has shifted in part toward the elucidation of the encoded biological functions. Since proteins are the driving forces behind very many biological activities, large-scale examinations of their expression variations, their functional roles and regulation have moved to the central stage. A significant fraction of the human proteome consists of secreted proteins. Exploring this set of molecules offers unique opportunities for understanding molecular interactions between cells and fosters biomarker discovery that could advance the detection and monitoring of diseases. Antibody microarrays are among the relatively new proteomic methodologies that may advance the field significantly because of their relative simplicity, robust performance and high sensitivity down to single-molecule detection. In addition, several aspects such as variations in amount, structure and activity can be assayed at a time. Antibody microarrays are therefore likely to improve the analytical capabilities in proteomics and consequently permit the production of even more informative and reliable data. This review looks at recent applications of this novel platform technology in secretome analysis and reflects on the future.     

Protein microarrays for antibody profiling: specificity and affinity determination on a chip

Protein microarray technology facilitates the detection and quantification of hundreds of binding reactions in one reaction from a minute amount of sample. Proof-of-concept studies have shown that the set-up of sensitive assay systems based on protein arrays is possible, however, the lack of specific capture reagents limits their use. Therefore, the generation and characterisation of capture molecules is one of the key topics for the development of protein array based systems. Recombinant antibody technologies, such as HuCAL (human combinatorial antibody library; MorphoSys, Munich, Germany), allow the fast generation of highly specific binders to nearly any given target molecule. Although antibody libraries comprise billions of members, it is not the selection process, but the detailed characterisation of the pre-selected monoclonal antibodies that presents the bottleneck for the production of high numbers of specific binders. In order to obtain detailed information on the properties of such antibodies, a microarray-based method has been developed. We show that it is possible to define the specificity of recombinant Fab fragments by protein and peptide microarrays and that antibodies can be classified by binding patterns. Since the assay uses a miniaturised system for the detection of antibody-antigen interactions, the observed binding occurs under ambient analyte conditions as defined by Ekins (J. Pharm. Biomed. Anal. 1989, 7, 155-168). This allows the determination of a relative affinity value for each binding event, and a ranking according to affinity is possible. The new microarray based approach has an extraordinary potential to speed up the screening process for the generation of recombinant antibodies with pre-defined selection criteria, since it is intrinsically a high-throughput technology.     

Selecting normalization genes for small diagnostic microarrays

Background  

Normalization of gene expression microarrays carrying thousands of genes is based on assumptions that do not hold for diagnostic microarrays carrying only few genes. Thus, applying standard microarray normalization strategies to diagnostic microarrays causes new normalization problems.     

Design of recombinant antibody microarrays for serum protein profiling: targeting of complement proteins

Antibody-based microarrays is a novel technology with great promise for high-throughput proteomics. The process of designing high-performing arrays has, however, turned out to be challenging. Here, we have designed the next generation of a human recombinant scFv antibody microarray platform for protein expression profiling of nonfractionated biotinylated human plasma and serum proteomes. The setup, based on black polymer Maxisorb slides interfaced with a fluorescent-based read-out system, was found to provide specific, sensitive (subpicomolar (pM) range) and reproducible means for protein profiling. Further, a chip-to-chip normalization protocol critical for comparing data generated on different chips was devised. Finally, the microarray data were found to correlate well with clinical laboratory data obtained using conventional methods, as demonstrated for a set of medium abundant (micromolar (microM) to nanomolar (nM) range) protein analytes in serum and plasma samples derived from healthy and complement-deficient individuals.     

A new approach to intensity-dependent normalization of two-channel microarrays

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects. However, MA methods require strong assumptions, which limit their general applicability. We review these assumptions and derive several practical scenarios in which they fail. The "dye-swap" normalization method has been much less frequently used because it requires two arrays per pair of samples. We show that a dye-swap is accurate under general assumptions, even under intensity-dependent dye bias, and that a dye-swap removes dye bias from a single pair of samples in general. Based on a flexible model of the relationship between mRNA amount and single-channel fluorescence intensity, we demonstrate the general applicability of a dye-swap approach. We then propose a common array dye-swap (CADS) method for the normalization of two-channel microarrays. We show that CADS removes both dye bias and array-specific effects, and preserves the true differential expression signal for every gene under the assumptions of the model.     

High-resolution spatial normalization for microarrays containing embedded technical replicates

MOTIVATION: Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited. RESULTS: We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible. AVAILABILITY: CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.     

Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

Background

Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.

Results

Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.

Conclusions

Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.

     

Optimized LOWESS normalization parameter selection for DNA microarray data

Background  

Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail.     

Selection and validation of normalization methods for c-DNA microarrays using within-array replications

MOTIVATION: Normalization of microarray data is essential for multiple-array analyses. Several normalization protocols have been proposed based on different biological or statistical assumptions. A fundamental problem arises whether they have effectively normalized arrays. In addition, for a given array, the question arises how to choose a method to most effectively normalize the microarray data. RESULTS: We propose several techniques to compare the effectiveness of different normalization methods. We approach the problem by constructing statistics to test whether there are any systematic biases in the expression profiles among duplicated spots within an array. The test statistics involve estimating the genewise variances. This is accomplished by using several novel methods, including empirical Bayes methods for moderating the genewise variances and the smoothing methods for aggregating variance information. P-values are estimated based on a normal or chi approximation. With estimated P-values, we can choose a most appropriate method to normalize a specific array and assess the extent to which the systematic biases due to the variations of experimental conditions have been removed. The effectiveness and validity of the proposed methods are convincingly illustrated by a carefully designed simulation study. The method is further illustrated by an application to human placenta cDNAs comprising a large number of clones with replications, a customized microarray experiment carrying just a few hundred genes on the study of the molecular roles of Interferons on tumor, and the Agilent microarrays carrying tens of thousands of total RNA samples in the MAQC project on the study of reproducibility, sensitivity and specificity of the data. AVAILABILITY: Code to implement the method in the statistical package R is available from the authors.     

High-throughput antibody microarrays for quantitative proteomic analysis

The antibody microarray is an intrinsically robust and quantitative system that delivers high-throughput and parallel measurements on particular sets of known proteins. It has become an important proteomics research tool, complementary to the conventional unbiased separation-based and mass spectrometry-based approaches. This review summarizes the technical aspects of production and the application for quantitative proteomic analysis with an emphasis on disease proteomics, especially the identification of biomarkers. Quality control, data analysis methods and the challenges for quantitative assays are also discussed.     

High-throughput antibody microarrays for quantitative proteomic analysis

The antibody microarray is an intrinsically robust and quantitative system that delivers high-throughput and parallel measurements on particular sets of known proteins. It has become an important proteomics research tool, complementary to the conventional unbiased separation-based and mass spectrometry-based approaches. This review summarizes the technical aspects of production and the application for quantitative proteomic analysis with an emphasis on disease proteomics, especially the identification of biomarkers. Quality control, data analysis methods and the challenges for quantitative assays are also discussed.     

Evaluation of surface chemistries for antibody microarrays

Antibody microarrays are an emerging technology that promises to be a powerful tool for the detection of disease biomarkers. The current technology for protein microarrays has been derived primarily from DNA microarrays and is not fully characterized for use with proteins. For example, there are a myriad of surface chemistries that are commercially available for antibody microarrays, but there are no rigorous studies that compare these different surfaces. Therefore, we have used a sandwich enzyme-linked immunosorbent assay (ELISA) microarray platform to analyze 17 different commercially available slide types. Full standard curves were generated for 23 different assays. We found that this approach provides a rigorous and quantitative system for comparing the different slide types based on spot size and morphology, slide noise, spot background, lower limit of detection, and reproducibility. These studies demonstrate that the properties of the slide surface affect the activity of immobilized antibodies and the quality of data produced. Although many slide types produce useful data, glass slides coated with aldehyde silane, poly-l-lysine, or aminosilane (with or without activation with a crosslinker) consistently produce superior results in the sandwich ELISA microarray analyses we performed.     

Efficiency clustering for low-density microarrays and its application to QPCR

Background  

Pathway-targeted or low-density arrays are used more and more frequently in biomedical research, particularly those arrays that are based on quantitative real-time PCR. Typical QPCR arrays contain 96-1024 primer pairs or probes, and they bring with it the promise of being able to reliably measure differences in target levels without the need to establish absolute standard curves for each and every target. To achieve reliable quantification all primer pairs or array probes must perform with the same efficiency.     

Applications of functional gene microarrays for profiling microbial communities

Functional gene arrays (FGAs) have been considered as a specific, sensitive, quantitative, and high throughput metagenomic tool to detect, monitor and characterize microbial communities. Especially GeoChips, the most comprehensive FGAs have been applied to analyze the functional diversity, composition, structure, and metabolic potential or activity of a variety of microbial communities from different habitats, such as aquatic ecosystems, soils, contaminated sites, extreme environments, and bioreactors. FGAs are able to address fundamental questions related to global change, bioremediation, land use, human health, and ecological theories, and link the microbial community structure to environmental properties and ecosystem functioning. This review focuses on applications of FGA technology for profiling microbial communities, including target preparation, hybridization and data processing, and data analysis. We also discuss challenges and future directions of FGA applications.     

Antigen arrays for antibody profiling

Antigen array technologies enable large-scale profiling of the specificity of antibody responses against autoantigens, tumor antigens and microbial antigens. Antibody profiling will provide insights into pathogenesis, and will enable development of novel tests for diagnosis and guiding therapy in the clinic. Recent advances in the field include development of antigen array-based approaches to examine immune responses against antigens encoded in genetic libraries, post-translationally modified proteins, and other biomolecules such as lipids. A promising application is the use of antibody profiling to guide development and selection of antigen-specific therapies to treat autoimmune disease. This review discusses these advances and the challenges ahead for development and refinement of antibody profiling technologies for use in the research laboratory and the clinic.     

The 4G10, pY20 and p-TYR-100 antibody specificity: profiling by peptide microarrays

The reversible phosphorylation of tyrosine residues is one of the most frequent post-translational modifications regulating enzymatic activities and protein-protein interactions in eukaryotic cells. Cells responding to internal or external regulatory inputs modify their phosphorylation status and diseased cells can often be diagnosed by observing alterations in their qualitative or quantitative phosphorylation profile. As a consequence the ability to describe the phosphorylation profile of a cell is central to many approaches aiming at the characterisation of signalling pathways. Anti-phosphotyrosine (pY) antibodies are widely used as experimental tools to monitor the phosphorylation status of a cell. By using peptide microarray technology we have characterised the substrate specificity of three widely used pY antibodies. We report that they are more sensitive to sequence context than is generally assumed and that their sequence preferences differ.     

Protein microarrays for gene expression and antibody screening.

Proteins translate genomic sequence information into function, enabling biological processes. As a complementary approach to gene expression profiling on cDNA microarrays, we have developed a technique for high-throughput gene expression and antibody screening on chip-size protein microarrays. Using a picking/spotting robot equipped with a new transfer stamp, protein solutions were gridded onto polyvinylidene difluoride filters at high density. Specific purified protein was detected on the filters with high sensitivity (250 amol or 10 pg of a test protein). On a microarray made from bacterial lysates of 92 human cDNA clones expressed in a microtiter plate, putative protein expressors could be reliably identified. The rate of false-positive clones, expressing proteins in incorrect reading frames, was low. Product specificity of selected clones was confirmed on identical microarrays using monoclonal antibodies. Cross-reactivities of some antibodies with unrelated proteins imply the use of protein microarrays for antibody specificity screening against whole libraries of proteins. Because this application would not be restricted to antigen-antibody systems, protein microarrays should provide a general resource for high-throughput screens of gene expression and receptor-ligand interactions.