Expression profiling of proteins inL-threonine biosynthetic pathway ofEscherichia coli by using antibody microarray

High-throughput proteomics using antibody microarrays: an update

Antibody-based microarrays are a rapidly emerging technology that has advanced from the first proof-of-concept studies to demanding serum protein profiling applications during recent years, displaying great promise within disease proteomics. Miniaturized micro- and nanoarrays can be fabricated with an almost infinite number of antibodies carrying the desired specificities. While consuming only minute amounts of reagents, multiplexed and ultrasensitive assays can be performed targeting high- as well as low-abundance analytes in complex nonfractionated proteomes. The microarray images generated can then be converted into protein expression profiles or protein atlases, revealing a detailed composition of the sample. The technology will provide unique opportunities for fields such as disease diagnostics, biomarker discovery, patient stratification, predicting disease recurrence and drug target discovery. This review describes an update of high-throughput proteomics, using antibody-based microarrays, focusing on key technological advances and novel applications that have emerged over the last 3 years.

Graph-based deconvolution analysis of multiplex sandwich microarray immunoassays: applications for environmental monitoring

The sandwich microarray immunoassay (SMI) is a powerful technique for the analysis and characterization of environmental samples, from the identification of microorganisms to specific bioanalytes. As the number of antibodies increases, however, unspecific binding and cross-reactivity can become a problem. To cope with such difficulties, we present here the concept of antibody graph associated to a sandwich antibody microarray. Antibody graphs give valuable information about the antibody cross-reactivity network and all the players involved in the sandwich format: capturing and tracer antibodies, the antigenic sample and the degree of cross-reactivity between antibodies. Making use of the information contained in the antibody graph, we have developed a deconvolution method that disentangles the antibody cross-reactivity events and gives qualitative information about the composition of the experimental sample under study. We have validated the method by using a 66 antibody-containing microarray to describe known antigenic mixtures as well as natural environmental samples characterized by 16S-RNA gene phylogenetic analysis. The application of our antibody graph and deconvolution method allowed us to discriminate between true specific antigen-antibody reactions and spurious signals on a microarray designed for environmental monitoring.

Protein expression profiling

Protein expression profiling is defined in general as identifying the proteins expressed in a particular tissue, under a specified set of conditions and at a particular time, usually compared to expression in reference samples. This information is useful in drug discovery and diagnosis as well as in understanding response mechanisms at the protein level. We may identify all the proteins responding to a particular stimulus and select those whose expression changes most. Or we may isolate significant protein variables and then identify them. These definitive sets of proteins (protein expression signatures; PES) are specific to diseases, toxicants, physical stresses, and to degrees of stress severity. Here we describe a method, based on machine learning, for isolating the sets of proteins, before identifying them by name, which classify accurately the treatment classes in a study. The principle in this chapter is that if proteins associated with known classes of interest can be used to identify unknown classes then the proteins are definitive for diagnosis.The proteins in each class, including controls, are converted to digital data and serve as input to artificial neural network (ANN) models. Multiple two-dimensional electrophoresis (2DE) gel patterns are included in each treatment class. A training subset of digitized individual, not composite, gel images is used to construct an ANN model which is then applied to a test set of images. Successful classification of the unknown (test) data confirms that the variables included in the model are indeed significant in discrimination among the classes. In the study described here the misclassifications were 5% or less using the ANN models. The ANN method seems to be a useful complement to image analysis, described in Chapter "Troubleshooting Image Analysis in 2DE". The reduction in protein variables permits multivariable statistics such as cluster and discriminant analyses.

Antibody-based microarrays

Antibody-based microarrays are a new powerful proteomic technology that can be used to generate rapid and detailed expression profiles of defined sets of protein analytes in complex samples as well as high-resolution portraits of entire proteomes. Miniaturized micro- and nanoarrays can be printed with numerous antibodies carrying the desired specificities. Multiplexed and ultra-sensitive assays, specifically targeting several analytes in a single experiment, can be performed, while consuming only minute amounts of the sample. The array images generated can then be converted into protein expression profiles, or maps, revealing the detailed composition of the sample. This promising proteomic research tool will thus provide unique opportunities for e.g. disease proteomics, biomarker discovery, disease diagnostics, and patient stratification. This review describes the antibody-based microarray technology and applications thereof.

Use of high density antibody arrays to validate and discover cancer serum biomarkers

Perhaps the greatest barrier to translation of serum biomarker discoveries is the inability to evaluate putative biomarkers in high throughput validation studies. Here we report on the development, production, and implementation of a high-density antibody microarray used to evaluate large numbers of candidate ovarian cancer serum biomarkers. The platform was shown to be useful for evaluation of individual antibodies for comparative analysis, such as with disease classification, and biomarker validation and discovery. We demonstrate its performance by showing that known tumor markers behave as expected. We also identify several promising biomarkers from a candidate list and generate hypotheses to support new discovery studies.

Optimization of critical factors affecting the performance of an allergen chip for the analysis of an allergen-specific human IgE in serum

A sensitive and multiplexed assay of allergen-specific human immunoglobulin E (IgE) is of great significance in the precise diagnosis of allergies. We report on the optimization of critical factors for chip-based analysis of IgE in human serum with a high reliability. Extracts of two mite species were used as model allergens, and were spotted onto a glass slide for the construction of an allergen chip. Respective allergen-specific IgE in human serum was analyzed by using biotinylated anti-human IgE and a streptavidin-Cy3 conjugate. Factors affecting the performance of the allergen chip were investigated and optimized. Especially, the effect of additives, the concentrations of biotinylated anti-human IgE and the streptavidin-Cy3 conjugate, the serum dilution factor, and the concentration of allergen extract as a capturing agent were examined in detail. Under the optimized conditions, a chip-based analysis for sera from 43 patients revealed a reliable and reproducible diagnosis of respective allergies, showing a good correlation with a conventional CAP assay.