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.     

A secreted protein microarray platform for extracellular protein interaction discovery

Characterization of the extracellular protein interactome has lagged far behind that of intracellular proteins, where mass spectrometry and yeast two-hybrid technologies have excelled. Improved methods for identifying receptor-ligand and extracellular matrix protein interactions will greatly accelerate biological discovery in cell signaling and cellular communication. These technologies must be able to identify low-affinity binding events that are often observed between membrane-bound coreceptor molecules during cell-cell or cell-extracellular matrix contact. Here we demonstrate that functional protein microarrays are particularly well-suited for high-throughput screening of extracellular protein interactions. To evaluate the performance of the platform, we screened a set of 89 immunoglobulin (Ig)-type receptors against a highly diverse extracellular protein microarray with 686 genes represented. To enhance detection of low-affinity interactions, we developed a rapid method to assemble bait Fc fusion proteins into multivalent complexes using protein A microbeads. Based on these screens, we developed a statistical methodology for hit calling and identification of nonspecific interactions on protein microarrays. We found that the Ig receptor interactions identified using our methodology are highly specific and display minimal off-target binding, resulting in a 70% true-positive to false-positive hit ratio. We anticipate that these methods will be useful for a wide variety of functional protein microarray users.     

A consolidated approach to analyzing data from high-throughput protein microarrays with an application to immune response profiling in humans.

Motivation: DNA microarrays are a well-known and established technology in biological and pharmaceutical research providing a wealth of information essential for understanding biological processes and aiding drug development. Protein microarrays are quickly emerging as a follow-up technology, which will also begin to experience rapid growth as the challenges in protein to spot methodologies are overcome. Like DNA microarrays, their protein counterparts produce large amounts of data that must be suitably analyzed in order to yield meaningful information that should eventually lead to novel drug targets and biomarkers. Although the statistical management of DNA microarray data has been well described, there is no available report that offers a successful consolidated approach to the analysis of high-throughput protein microarray data. We describe the novel application of a statistical methodology to analyze the data from an immune response profiling assay using human protein microarray with over 5000 proteins on each chip.     

Genes that code for T cell signaling proteins establish transcriptional regulatory networks during thymus ontogeny

Gene expression profiling by cDNA microarrays during murine thymus ontogeny has contributed to dissecting the large-scale molecular genetics of T cell maturation. Gene profiling, although useful for characterizing the thymus developmental phases and identifying the differentially expressed genes, does not permit the determination of possible interactions between genes. In order to reconstruct genetic interactions, on RNA level, within thymocyte differentiation, a pair of microarrays containing a total of 1,576 cDNA sequences derived from the IMAGE MTB library was applied on samples of developing thymuses (14-17 days of gestation). The data were analyzed using the GeneNetwork program. Genes that were previously identified as differentially expressed during thymus ontogeny showed their relationships with several other genes. The present method provided the detection of gene nodes coding for proteins implicated in the calcium signaling pathway, such as Prrg2 and Stxbp3, and in protein transport toward the cell membrane, such as Gosr2. The results demonstrate the feasibility of reconstructing networks based on cDNA microarray gene expression determinations, contributing to a clearer understanding of the complex interactions between genes involved in thymus/thymocyte development.     

Interactome Mapping: Using Protein Microarray Technology to Reconstruct Diverse Protein Networks

A major focus of systems biology is to characterize interactions between cellular components, in order to develop an accurate picture of the intricate networks within biological systems. Over the past decade, protein microarrays have greatly contributed to advances in proteomics and are becoming an important platform for systems biology. Protein microarrays are highly flexible, ranging from large-scale proteome microarrays to smaller customizable microarrays, making the technology amenable for detection of a broad spectrum of biochemical properties of proteins. In this article, we will focus on the numerous studies that have utilized protein microarrays to reconstruct biological networks including protein-DNA interactions, posttranslational protein modifications (PTMs), lectin-glycan recognition, pathogen-host interactions and hierarchical signaling cascades. The diversity in applications allows for integration of interaction data from numerous molecular classes and cellular states, providing insight into the structure of complex biological systems. We will also discuss emerging applications and future directions of protein microarray technology in the global frontier.     

Proteome microarray technology and application: higher,wider, and deeper

ABSTRACT

Introduction: Protein microarray is a powerful tool for both biological study and clinical research. The most useful features of protein microarrays are their miniaturized size (low reagent and sample consumption), high sensitivity and their capability for parallel/high-throughput analysis. The major focus of this review is functional proteome microarray.

Areas covered: For proteome microarray, this review will discuss some recently constructed proteome microarrays and new concepts that have been used for constructing proteome microarrays and data interpretation in past few years, such as PAGES, M-NAPPA strategy, VirD technology, and the first protein microarray database. this review will summarize recent proteomic scale applications and address the limitations and future directions of proteome microarray technology.

Expert opinion: Proteome microarray is a powerful tool for basic biological and clinical research. It is expected to see improvements in the currently used proteome microarrays and the construction of more proteome microarrays for other species by using traditional strategies or novel concepts. It is anticipated that the maximum number of features on a single microarray and the number of possible applications will be increased, and the information that can be obtained from proteome microarray experiments will more in-depth in the future.     

Identification of differentially expressed genes and false discovery rate in microarray studies

PURPOSE OF REVIEW: To highlight the development in microarray data analysis for the identification of differentially expressed genes, particularly via control of false discovery rate. RECENT FINDINGS: The emergence of high-throughput technology such as microarrays raises two fundamental statistical issues: multiplicity and sensitivity. We focus on the biological problem of identifying differentially expressed genes. First, multiplicity arises due to testing tens of thousands of hypotheses, rendering the standard P value meaningless. Second, known optimal single-test procedures such as the t-test perform poorly in the context of highly multiple tests. The standard approach of dealing with multiplicity is too conservative in the microarray context. The false discovery rate concept is fast becoming the key statistical assessment tool replacing the P value. We review the false discovery rate approach and argue that it is more sensible for microarray data. We also discuss some methods to take into account additional information from the microarrays to improve the false discovery rate. SUMMARY: There is growing consensus on how to analyse microarray data using the false discovery rate framework in place of the classical P value. Further research is needed on the preprocessing of the raw data, such as the normalization step and filtering, and on finding the most sensitive test procedure.     

RNA-Seq vs Dual- and Single-Channel Microarray Data: Sensitivity Analysis for Differential Expression and Clustering

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter’s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.     

Microarrays of peptides elevated on the protein layer for efficient protein kinase assay

Peptide microarrays can be used for the high-throughput analysis of protein-peptide interactions. However, current peptide microarrays are rather costly to make and require cumbersome steps of introducing novel polymeric surfaces and/or chemical derivatization of peptides. Here, we report a novel method for manufacturing peptide microarrays by elevating the peptide on the layer of protein by a fusion protein approach. Using two protein kinases and their peptide substrates as examples, we show that elevating peptides on the layer of protein allows sensitive, specific, and efficient detection of peptide-protein interactions without the need for complicated chemical modification of solid supports and peptides. It was found that kinase activity could be detected with as low as 0.09 fmol of kemptide, which is about 1000-fold more sensitive than the 0.1 pmol obtained with other microarray systems. Furthermore, peptides can be produced as fusion proteins by fermentation of recombinant Escherichia coli and thus the expensive peptide synthesis process can be avoided. Therefore, this new strategy will not only be useful in high-throughput and cost-effective screening of kinase substrate peptides but also be generally applicable in studying various protein-peptide interactions.     

Profiling of differential protein expression in angiogenin-induced HUVECs using antibody-arrayed ProteoChip

ProteoChip has been developed as a novel protein microarray technology. So far it has been applied in new lead screening and molecular diagnostics and we expect its role to grow in the field of biology. Here, we investigated the application of ProteoChip for the study of differential protein expression profiles in angiogenin-induced human umbilical vein endothelial cells (HUVECs). Antibody microarrays constructed by immobilizing 60 distinct antibodies against signal-transducing proteins on ProteoChip base plates were used to analyze the expression pattern of cell-signaling proteins in HUVECs treated with angiogenin. The antibody microarray approach showed that angiogenin induced the up- and down-regulation of several cellular regulators related with cell proliferation. Changes in the expression of signaling proteins determined by antibody microarray were validated by Western blot analysis. In this experiment, ten up-regulated proteins and six down-regulated proteins were identified and confirmed by immunoblot analysis. Taken together, these data suggest that antibody microarrays using ProteoChip technology can be a powerful tool for high-throughput analysis of proteomes in biological samples.     

Development of peptide aptamer microarrays for detection of HPV16 oncoproteins in cell extracts

Protein microarrays represent an emerging technology that promises to facilitate high-throughput proteomics. The major goal of this technology is to employ peptides, full-length proteins, antibodies, and small molecules to simultaneously screen thousands of targets for potential protein–protein interactions or modifications of the proteome. This article describes the performance of a set of peptide aptamers specific for the human papillomavirus (HPV) type 16 oncoproteins E6 and E7 in a microarray format. E6 and E7 peptide aptamer microarrays were probed with fluorescence-labeled lysates generated from HPV-infected cervical keratinocytes expressing both E6 and E7 oncoproteins. Peptide aptamer microarrays are shown to detect low levels of E6 and E7 proteins. Peptide aptamers specific for cellular proteins included on these microarrays suggested that expression of CDK2, CDK4, and BCL-6 may be affected by HPV infection and genome integration. We conclude that peptide aptamer microarrays represent a promising tool for proteomics and may be of value in biological and clinical investigations of cervical carcinogenesis.     

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.     

Prediction of gene expression in embryonic structures of Drosophila melanogaster

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.     

In vitro protein microarrays for detecting protein-protein interactions: application of a new method for fluorescence labeling of proteins

Protein microarrays or proteome chips are potentially powerful tools for comprehensive analysis of protein-protein interactions. In interaction analysis, a set of immobilized proteins is arrayed on slides and each slide is probed with a set of fluorescently labeled proteins. Here we have developed and tested an in vitro protein microarray, in which both arraying and probing proteins were prepared by cell-free translation. The in vitro synthesis of fluorescently labeled proteins was accomplished by a new method: a fluorophore-puromycin conjugate was incorporated into a protein at the C-terminus on the ribosome. The resulting fluorescently labeled proteins were confirmed to be useful for probing protein-protein interactions on protein microarrays in model experiments. Since the in vitro protein microarrays can easily be extended to a high-throughput format and also combined with in vitro display technologies such as the streptavidin-biotin linkage in emulsions method (Doi and Yanagawa, FEBS Lett. 1999, 457, 227-230), our method should be useful for large-scale analysis of protein-protein interactions.