Tissue microarrays: fast-tracking protein expression at the cellular level

Tissue microarrays maximize returns in cellular pathology whilst minimizing the use of cells and tissues. They are made by arraying cores of tissue taken from multiple donor blocks into a single recipient block. Accordingly, the histology and pathology of several hundred tissues can be represented in one tissue microarray that, when stained by immunohistochemistry, provides comprehensive topographic information on protein expression. Used with complimentary techniques, such as complementary DNA microarray analysis, tissue microarrays are providing valuable data for the identification of new markers of disease and assisting in the discovery of therapeutic targets. They are also leading a revolution in cellular pathology as high-throughput technology is introduced to maximize the information provided.     

Disease signatures are robust across tissues and experiments

Meta‐analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta‐analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large‐scale analysis of disease‐associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta‐analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi‐tissue systems models of disease pathology using public data.     

Microarrays as validation strategies in clinical samples: tissue and protein microarrays

The widespread use of DNA microarrays has led to the discovery of many genes whose expression profile may have significant clinical relevance. The translation of this data to the bedside requires that gene expression be validated as protein expression, and that annotated clinical samples be available for correlative and quantitative studies to assess clinical context and usefulness of putative biomarkers. We review two microarray platforms developed to facilitate the clinical validation of candidate biomarkers: tissue microarrays and reverse-phase protein microarrays. Tissue microarrays are arrays of core biopsies obtained from paraffin-embedded tissues, which can be assayed for histologically-specific protein expression by immunohistochemistry. Reverse-phase protein microarrays consist of arrays of cell lysates or, more recently, plasma or serum samples, which can be assayed for protein quantity and for the presence of post-translational modifications such as phosphorylation. Although these platforms are limited by the availability of validated antibodies, both enable the preservation of precious clinical samples as well as experimental standardization in a high-throughput manner proper to microarray technologies. While tissue microarrays are rapidly becoming a mainstay of translational research, reverse-phase protein microarrays require further technical refinements and validation prior to their widespread adoption by research laboratories.     

Tissue microarrays as a platform for proteomic investigation

Tissue microarrays have become an essential tool in translational pathology. They are used to confirm results from other experimental platforms, such as expression microarrays, as well as a primary tool to explore the expression profile of proteins by immunohistochemical analysis. Tissue microarrays are routinely used molecular epidemiology, drug development and determining the diagnostic, prognostic and predictive value of new biomarkers. By applying traditional protein based assays, as well as novel assays to the platform, tissue microarrays have gained a new utility as a proteomic tool for both basic science as well as clinical investigation. This article will explore the new approaches that are being applied to tissue microarrays to, characterize the human proteome, and new technologies that allow tissue microarrays to function as a protein array. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged     

Tissue microarrays enabling high-throughput molecular pathology

The tissue microarray has enabled high-throughput pathology. Rather than the laborious review of individual slides and issues of assay reproducibility across large series of specimens, tissue microarrays allow the review of a single stain on a single slide containing tens to hundreds of samples. This is a paradigm shift in pathology, away from histomorphology and toward molecular characterization by immunohistochemistry. This platform allows large retrospective clinical studies of biomarkers for correlation with outcome and can equally well be applied toward high-throughput analysis of cell lines and xenografts. Tissue microarrays encourage novel approaches to assaying tissue with retained histomorphology and have enabled image analysis in pathology. The reduction of tissue to an analyte for high-throughput analysis has highlighted the importance of a high quality tissue and the impact of tissue handling and processing in the quality of data that can be obtained from analysis of tissue.     

RAKE and LNA-ISH reveal microRNA expression and localization in archival human brain

microRNAs (miRNAs) are small (approximately 22 nucleotide) regulatory RNAs which play fundamental roles in many biological processes. Recent studies have shown that the expression of many miRNAs is altered in various human tumors and some miRNAs may function as oncogenes or tumor suppressor genes. However, with the exception of glioblastoma multiforme, the expression of miRNAs in brain tumors is unknown. Furthermore, methods to profile miRNAs from formalin-fixed, paraffin-embedded (FFPE) archival tissues or to study their cellular and subcellular localization in FFPE tissues have been lacking. Here we report the coordinated miRNA expression analysis from the tissue level to the subcellular level, using the RAKE (RNA-primed, array-based, Klenow Enzyme) miRNA microarray platform in conjunction with Locked Nucleic Acid (LNA)-based in situ hybridization (LNA-ISH) on archival FFPE human brains and oligodendroglial tumors. The ability to profile miRNAs from archival tissues at the tissue level, by RAKE microarrays, and at the cellular level by LNA-ISH, will accelerate studies of miRNAs in human diseases.     

Surface manipulation of biomolecules for cell microarray applications

Many biological events, such as cellular communication, antigen recognition, tissue repair and DNA linear transfer, are intimately associated with biomolecule interactions at the solid-liquid interface. To facilitate the study and use of these biological events for biodevice and biomaterial applications, a sound understanding of how biomolecules behave at interfaces and a concomitant ability to manipulate biomolecules spatially and temporally at surfaces is required. This is particularly true for cell microarray applications, where a range of biological processes must be duly controlled to maximize the efficiency and throughput of these devices. Of particular interest are transfected-cell microarrays (TCMs), which significantly widen the scope of microarray genomic analysis by enabling the high-throughput analysis of gene function within living cells. This article reviews this current research focus, discussing fundamental and applied research into the spatial and temporal surface manipulation of DNA, proteins and other biomolecules and the implications of this work for TCMs.     

Optimized procedures for microarray analysis of histological specimens processed by laser capture microdissection

Analysis of cell-specific gene expression patterns using microarrays can reveal genes that are differentially expressed in diseased and normal tissue, as well as identify genes associated with specialized cellular functions. However, the cellular heterogeneity of the tissues precludes the resolution of expression profiles of specific cell types. While laser capture microdissection (LCM) can be used to obtain purified cell populations, the limited quantity of RNA isolated makes it necessary to perform an RNA amplification step prior to microarray analysis. The linearity and reproducibility of two RNA amplification protocols--the Baugh protocol (Baugh et al., 2001, Nucleic Acids Res 29:E29) and an in-house protocol have been assessed by conducting microarray analyses. Cy3-labeled total RNA from the colorectal cell line Colo-205 was compared to Cy5-labeled Colo-205 amplified RNA (aRNA) generated with each of the two protocols, using a human 10K cDNA array. The correlation of the gene intensities between amplified and total RNA measured in the two channels of each microarray was 0.72 and 0.61 for the Baugh protocol and the in-house protocol, respectively. The two protocols were further evaluated using aRNA obtained from normal colonic crypt cross-sections isolated via LCM. In both cases a microarray profile representative of colonic mucosa was obtained; statistically, the Baugh protocol was superior. Furthermore, a substantial overlap between highly expressed genes in the Colo-205 cells and colonic crypts underscores the reliability of the microarray analysis of LCM-derived material. Taken together, these results demonstrate that LCM-derived tissue from histological specimens can generate abundant amounts of high-quality aRNA for subsequent microarray analysis.     

TAD: a web interface and database for tissue microarrays

Tissue microarrays are increasingly important tools that bring high-throughput technology to traditional pathology laboratories. In many cases, each spot on a tissue microarray is scored by a skilled pathologist and recorded manually. TAD consists of an Active Server Page web interface to a relational database that automates recording scores and linking them with clinical data for future interpretation. TAD is an open source application that can be installed locally.     

Pegylated, steptavidin-conjugated quantum dots are effective detection elements for reverse-phase protein microarrays

Protein microarray technologies provide a means of investigating the proteomic content of clinical biopsy specimens in order to determine the relative activity of key nodes within cellular signaling pathways. A particular kind of protein microarray, the reverse-phase microarray, is being evaluated in clinical trials because of its potential to utilize limited amounts of cellular material obtained through biopsy. Using this approach, cellular lysates are arrayed in dilution curves on nitrocellulose substrates for subsequent probing with antibodies. To improve the sensitivity and utility of reverse-phase microarrays, we tested whether a new reporter technology as well as a new detection instrument could enhance microarray performance. We describe the use of an inorganic fluorescent nanoparticle conjugated to streptavidin, Qdot 655 Sav, in a reverse-phase protein microarray format for signal pathway profiling. Moreover, a pegylated form of this bioconjugate, Qdot 655 Sav, is found to have superior detection characteristics in assays performed on cellular protein extracts over the nonpegylated form of the bioconjugate. Hyperspectral imaging of the quantum dot microarray enabled unamplified detection of signaling proteins within defined cellular lysates, which indicates that this approach may be amenable to multiplexed, high-throughput reverse-phase protein microarrays in which numerous analytes are measured in parallel within a single spot.     

Probing lipid-protein interactions using lipid microarrays

Lipids are central to the regulation and control of several cellular functions. They form many of the important structural features of cells, and are critical members of cellular signal transduction pathways. Cellular dysfunction is often caused by errors in lipid signaling; therefore, the proteins that interact with, synthesize or metabolize the lipids are potential therapeutic targets. Characterizing the contingent of cellular lipids and their abundance and how this is associated with disease will facilitate understanding how to intervene to correct diseases caused by dysfunctional lipid signaling. Since lipid-signaling networks involve several classes of proteins it is essential to determine the identity and role of these proteins in order to understand the networks. These proteins may be receptors, effectors, transporters or enzymes. We present tools, specifically, a lipid microarray platform, to uncover lipid-binding effector proteins that function in lipid signaling pathways. Lipid microarrays will allow researchers to obtain a comparable fingerprint of the proteins from a cell or tissue that bind to lipids, and also enable the identification of functionally important lipid-binding proteins. By applying a systematic approach to the quantification of lipid-protein interactions, lipid microarrays will provide an integrated knowledge base for the human lipidome. These tools have the potential to identify and validate targets to improve personalized medicine and health.     

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

Focus on RNA isolation: obtaining RNA for microRNA (miRNA) expression profiling analyses of neural tissue

MicroRNAs (miRNAs) are present in all known plant and animal tissues and appear to be somewhat concentrated in the mammalian nervous system. Many different miRNA expression profiling platforms have been described. However, relatively little research has been published to establish the importance of 'upstream' variables in RNA isolation for neural miRNA expression profiling. We tested whether apparent changes in miRNA expression profiles may be associated with tissue processing, RNA isolation techniques, or different cell types in the sample. RNA isolation was performed on a single brain sample using eight different RNA isolation methods, and results were correlated using a conventional miRNA microarray and then cross-referenced to Northern blots. Differing results were seen between samples obtained using different RNA isolation techniques and between microarray and Northern blot results. Another complication of miRNA microarrays is tissue-level heterogeneity of cellular composition. To investigate this phenomenon, miRNA expression profiles were determined and compared between highly-purified primary cerebral cortical cell preparations of rat primary E15-E18 neurons versus rat primary E15-E18 astrocytes. Finally, to assess the importance of dissecting human brain gray matter from subjacent white matter in cerebral cortical studies, miRNA expression profiles were compared between gray matter and immediately contiguous white matter. The results suggest that for microarray studies, cellular composition is important, and dissecting white matter from gray matter improves the specificity of the results. Based on these data, recommendations for miRNA expression profiling in neural tissues, and considerations worthy of further study, are discussed.     

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.     

Characterization of the expression of HTm4 (MS4A3), a cell cycle regulator,in human peripheral blood cells and normal and malignant tissues

HTm4 (MS4A3) is a member of a family of four‐transmembrane proteins designated MS4A. MS4A proteins fulfil diverse functions, acting as cell surface signalling molecules and intracellular adapter proteins. Early reports demonstrated that HTm4 is largely restricted to the haematopoietic lineage, and is involved in cell cycle control, via a regulatory interaction with the kinase‐associated phosphatase, cyclin A and cyclin‐dependent kinase 2 (CDK2). Here we describe the expression pattern of HTm4 in peripheral blood cells using gene expression microarray technology, and in normal foetal and adult human tissues, as well as adult human cancers, using tissue microarray technology. Using oligonucleotide microarrays to evaluate HTm4 mRNA, all peripheral blood cell types demonstrated very low levels of HTm4 expression; however, HTm4 expression was greatest in basophils compared to eosinophils, which showed lower levels of HTm4 expression. Very weak HTm4 expression is found in monocytes, granulocytes and B cells, but not in T cells, by lineage specific haematopoietic cell flow cytometry analysis. Interestingly, phytohaemagglutinin stimulation increases HTm4 protein expression in peripheral blood CD4‐T‐lymphocytes over nearly undetectable baseline levels. Western blotting and immunohistochemical studies show strong HTm4 expression in the developing haematopoietic cells of human foetal liver. Immunohistochemical studies on normal tissue microarrays confirmed HTm4 expression in a subset of leucocytes in nodal, splenic tissues and thymic tissue, and weak staining in small numbers of cell types in non‐haematopoietic tissues. Human foetal brain specimens from 19 to 31 gestational weeks showed that the strongest‐staining cells are ventricular zone cells and the earliest‐born, earliest‐differentiating ‘pioneer’ neurons in the cortical plate, Cajal‐Retzius and, to a lesser extent, subplate‐like neurons. Malignant tissue microarray analysis showed HTm4 expression in a wide variety of adenocarcinomas, including breast, prostate and ovarian. These findings warrant the further study of the role of HTm4 in the cell cycle of both haematopoietic and tumour cells.     

Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.