New molecular approaches to tissue analysis.

The completion of the Human Genome Project will produce new opportunities for analysis of genes and their products in human tissue. The emergence of new technologies will enable investigators to directly examine human tissues for gene deletion, transposition, and amplification. In addition, we will be able to assess the complete gene expression of a tissue by examining the mRNA species using microarray chips. The emerging technologies of laser capture microdissection and RNA amplification enables these procedures to be carried out on groups of a few hundred cells, which will facilitate the examination of heterogeneous lesions. Finally, the application of tissue arrays and the capability of obtaining protein sequences in samples of only a few femtomoles of protein using desorption mass spectroscopy will revolutionize the analysis of protein expression.     

Gene expression patterns associated with infertility in humans and rodent models

Modern genomic technologies such as DNA arrays provide the means to investigate molecular interactions at an unprecedented level, and arrays have been used to carry out gene expression profiling as a means of identifying candidate genes involved in molecular mechanisms underlying a variety of phenotypes. By comparing gene expression profiles from normal and abnormal human testes with those from comparable infertile mouse models, we endeavored to identify genes and gene networks critical for male fertility. We used commercially available filter-based DNA arrays to analyze testicular gene expression from eight human testis biopsies and three different infertile mouse models (atrichosis mutation, ataxia telangiectasia knockout and CREMtau knockout). Forty-seven mouse genes exhibited differential testicular gene expression (P <0.01) associated with male infertility. These included genes involved in DNA repair (Vim, Rad23A, Rad23B), glutathione metabolism (Gsr, Gstp 1, Mgst1), proteolysis (Ace, Casp1, Ctsd), spermatogenesis (Prlr, Tmsb4 and Zfp-37) and stress response (Hsp 1, Osp94). The expression of 19 human genes was different (P<0.05) between normal and abnormal samples, including those associated with apoptosis (GADD45), gonad development (SOX9), proteolysis (PSMC3, SPINK2, TIMP3, UBE213) and signal transduction (DLK1, NAP4, S100A10). Direct comparison of differentially expressed human and mouse genes identified glucose phosphate isomerase, and the highly similar human tissue inhibitor of metalloproteinase 3 (TIMP3) and mouse Timp2. Using DNA microarrays to profile gene expression in testes from infertile animal models and humans will be useful for understanding congenital infertility, and also infertility caused by environmental exposures where the same genes and molecular mechanisms are involved.     

Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies

MOTIVATION: In a typical gene expression profiling study, our prime objective is to identify the genes that are differentially expressed between the samples from two different tissue types. Commonly, standard analysis of variance (ANOVA)/regression is implemented to identify the relative effects of these genes over the two types of samples from their respective arrays of expression levels. But, this technique becomes fundamentally flawed when there are unaccounted sources of variability in these arrays (latent variables attributable to different biological, environmental or other factors relevant in the context). These factors distort the true picture of differential gene expression between the two tissue types and introduce spurious signals of expression heterogeneity. As a result, many genes which are actually differentially expressed are not detected, whereas many others are falsely identified as positives. Moreover, these distortions can be different for different genes. Thus, it is also not possible to get rid of these variations by simple array normalizations. This both-way error can lead to a serious loss in sensitivity and specificity, thereby causing a severe inefficiency in the underlying multiple testing problem. In this work, we attempt to identify the hidden effects of the underlying latent factors in a gene expression profiling study by partial least squares (PLS) and apply ANCOVA technique with the PLS-identified signatures of these hidden effects as covariates, in order to identify the genes that are truly differentially expressed between the two concerned tissue types. RESULTS: We compare the performance of our method SVA-PLS with standard ANOVA and a relatively recent technique of surrogate variable analysis (SVA), on a wide variety of simulation settings (incorporating different effects of the hidden variable, under situations with varying signal intensities and gene groupings). In all settings, our method yields the highest sensitivity while maintaining relatively reasonable values for the specificity, false discovery rate and false non-discovery rate. Application of our method to gene expression profiling for acute megakaryoblastic leukemia shows that our method detects an additional six genes, that are missed by both the standard ANOVA method as well as SVA, but may be relevant to this disease, as can be seen from mining the existing literature.     

Turning publicly available gene expression data into discoveries using gene set context analysis

Gene Set Context Analysis (GSCA) is an open source software package to help researchers use massive amounts of publicly available gene expression data (PED) to make discoveries. Users can interactively visualize and explore gene and gene set activities in 25,000+ consistently normalized human and mouse gene expression samples representing diverse biological contexts (e.g. different cells, tissues and disease types, etc.). By providing one or multiple genes or gene sets as input and specifying a gene set activity pattern of interest, users can query the expression compendium to systematically identify biological contexts associated with the specified gene set activity pattern. In this way, researchers with new gene sets from their own experiments may discover previously unknown contexts of gene set functions and hence increase the value of their experiments. GSCA has a graphical user interface (GUI). The GUI makes the analysis convenient and customizable. Analysis results can be conveniently exported as publication quality figures and tables. GSCA is available at https://github.com/zji90/GSCA. This software significantly lowers the bar for biomedical investigators to use PED in their daily research for generating and screening hypotheses, which was previously difficult because of the complexity, heterogeneity and size of the data.     

Effect of acute heat shock on gene expression by human peripheral blood mononuclear cells.

We studied the effect of heat shock on gene expression by normal human cells. Peripheral blood mononuclear cells (PBMCs) were obtained from healthy adults. Paired samples from each subject were subjected to either 20 min of heat shock (43 degrees C) or control (37 degrees C) conditions and then returned to 37 degrees C. RNA was isolated 160 min later, and five representative samples were analyzed on Affymetrix gene chip arrays containing approximately 12,600 probes. A biologically meaningful effect was defined as a statistically significant, twofold or greater difference in expression of sequences that were detected in all five experiments under control (downregulated sequences) or heat shock (upregulated sequences) conditions. Changes occurred in 395 sequences (227 increased by heat shock, 168 decreased), representing 353 Unigene numbers, in every functional category previously implicated in the heat shock response. By RT-PCR, we confirmed the findings for one upregulated sequence (Rad, a G protein) and one downregulated sequence (osteopontin, a cytokine). We conclude that heat shock causes extensive gene expression changes in PBMCs, affecting all functional categories of the heat shock response.     

SWISS MADE: Standardized WithIn Class Sum of Squares to Evaluate Methodologies and Dataset Elements

Contemporary high dimensional biological assays, such as mRNA expression microarrays, regularly involve multiple data processing steps, such as experimental processing, computational processing, sample selection, or feature selection (i.e. gene selection), prior to deriving any biological conclusions. These steps can dramatically change the interpretation of an experiment. Evaluation of processing steps has received limited attention in the literature. It is not straightforward to evaluate different processing methods and investigators are often unsure of the best method. We present a simple statistical tool, Standardized WithIn class Sum of Squares (SWISS), that allows investigators to compare alternate data processing methods, such as different experimental methods, normalizations, or technologies, on a dataset in terms of how well they cluster a priori biological classes. SWISS uses Euclidean distance to determine which method does a better job of clustering the data elements based on a priori classifications. We apply SWISS to three different gene expression applications. The first application uses four different datasets to compare different experimental methods, normalizations, and gene sets. The second application, using data from the MicroArray Quality Control (MAQC) project, compares different microarray platforms. The third application compares different technologies: a single Agilent two-color microarray versus one lane of RNA-Seq. These applications give an indication of the variety of problems that SWISS can be helpful in solving. The SWISS analysis of one-color versus two-color microarrays provides investigators who use two-color arrays the opportunity to review their results in light of a single-channel analysis, with all of the associated benefits offered by this design. Analysis of the MACQ data shows differential intersite reproducibility by array platform. SWISS also shows that one lane of RNA-Seq clusters data by biological phenotypes as well as a single Agilent two-color microarray.     

An a posteriori strategy for enhancing gene discovery in anonymous cDNA microarray experiments

MOTIVATION: Because of the high cost of sequencing, the bulk of gene discovery is performed using anonymous cDNA microarrays. Though the clones on such arrays are easier and cheaper to construct and utilize than unigene and oligonucleotide arrays, they are there in proportion to their corresponding gene expression activity in the tissue being examined. The associated redundancy will be there in any pool of possibly interesting differentially expressed clones identified in a microarray experiment for subsequent sequencing and investigation. An a posteriori sampling strategy is proposed to enhance gene discovery by reducing the impact of the redundancy in the identified pool. RESULTS: The proposed strategy exploits the fact that individual genes that are highly expressed in a tissue are more likely to be present as a number of spots in an anonymous library and, as a direct consequence, are also likely to give higher fluorescence intensity responses when present in a probe in a cDNA microarray experiment. Consequently, spots that respond with low intensities will have a lower redundancy and so should be sequenced in preference to those with the highest intensities. The proposed method, which formalizes how the fluorescence intensity of a spot should be assessed, is validated using actual microarray data, where the sequences of all the clones in the identified pool had been previously determined. For such validations, the concept of a repeat plot is introduced. It is also utilized to visualize and examine different measures for the characterization of fluorescence intensity. In addition, as confirmatory evidence, sequencing from the lowest to the highest intensities in a pool, with all the sequences known, is compared graphically with their random sequencing. The results establish that, in general, the opportunity for gene discovery is enhanced by avoiding the pooling of different biological libraries (because their construction will have involved different hybridization episodes) and concentrating on the clones with lower fluorescence intensities.     

Retriever and CompareTable, two informatics tools for data analysis of high-density oligonucleotide arrays.

High-density oligonucleotide arrays are widely employed for detecting global changes in gene expression profiles of cells or tissues exposed to specific stimuli. Presented with large amounts of data, investigators can spend significant amounts of time analyzing and interpreting this array data. In our application of GeneChip arrays to analyze changes in gene expression in viral-infected epithelium, we have needed to develop additional computational tools that may be of utility to other investigators using this methodology. Here, I describe two executable programs to facilitate data extraction and multiple data point analysis. These programs run in a virtual DOS environment on Microsoft Windows 95/98/2K operating systems on a desktop PC. Both programs can be freely downloaded from the BioTechniques Software Library (www.BioTechniques.com). The first program, Retriever, extracts primary data from an array experiment contained in an Affymetrix textfile using user-inputted individual identification strings (e.g., the probe set identification numbers). With specific data retrieved for individual genes, hybridization profiles can be examined and data normalized. The second program, CompareTable, is used to facilitate comparison analysis of two experimental replicates. CompareTable compares two lists of genes, identifies common entries, extracts their data, and writes an output text file containing only those genes present in both of the experiments. The output files generated by these two programs can be opened and manipulated by any software application recognizing tab-delimited text files (e.g., Microsoft NotePad or Excel).     

Post-genomic applications of tissue microarrays: basic research, prognostic oncology, clinical genomics and drug discovery

Tissue microarrays (TMAs) are an ordered array of tissue cores on a glass slide. They permit immunohistochemical analysis of numerous tissue sections under identical experimental conditions. The arrays can contain samples of every organ in the human body, or a wide variety of common tumors and obscure clinical cases alongside normal controls. The arrays can also contain pellets of cultured tumor cell lines. These arrays may be used like any histological section for immunohistochemistry and in situ hybridization to detect protein and gene expression. This new technology will allow investigators to analyze numerous biomarkers over essentially identical samples, develop novel prognostic markers and validate potential drug targets. The ability to combine TMA technology with DNA microarrays and proteomics makes it a very attractive tool for analysis of gene expression in clinically stratified tumor specimens and relate expression of each particular protein with clinical outcome. Public domain software allows researchers to examine digital images of individual histological specimens from TMAs, evaluate and score them and store the quantitative data in a relational database. TMA technology may be specifically applied to the profiling of proteins of interest in other pathophysiological conditions such as congestive heart failure, renal disease, hypertension, diabetes, cystic fibrosis and neurodegenerative disorders. This review is intended to summarize the strengths and weaknesses of TMA technology which will have an increasingly important role in the laboratories of the post-genomic era.     

Genomics and microarray for detection and diagnostics

Genomics provided biomedical scientists an inventory of all genes and sequences present in a living being. This provides an unique opportunity to the scientists to predict and study biological functions of these genes. The changes in the gene expression regulated by genomic sequences therefore reflect changes in the molecular processes working in a cell or tissue in response to external factors including exposure to toxic compounds and pathogens. Microarray offers a biotechnological revolution with the help of DNA chemistry, silicon chip technology and optics to be used to monitor gene expression for thousands of genes in one single experiment. Briefly, 20,000 to 100,000 unique DNA molecules get applied by a robot to the surface of silicon wafers (approximately the size of a microscope slide). Using a single microarray experiment, the expression level of 20,000 to 100,000 genes will be examined in one single experiment. Genomics and microarray have a significant role and impact on the design and development of modern detection and diagnostic tools in several different ways. Microarray tools are now used on regular basis for monitoring gene expression of large number of genes and also frequently applied to DNA sequence analysis, immunology, genotyping, and molecular diagnosing. For diagnostics, these tools can be used to distinguish and differentiate between different DNA fragments that differ by as little as a single nucleotide polymorphism (SNP). These microarrays can be divided based on the gene density spots that will be high density (>10,000 spots) per slide, medium (< 1000 > 100) and low density (< 100). High-density arrays have proven to be very useful in disease diagnosis especially in diagnosis and classification of different types of cancers. These microarray tools hold tremendous potential for pathogen detection, which will be comprised, of unique sets of genes (also referred to as "signatures") able to unambiguously identify the species and strain of pathogens of interest.     

Assessment of gene expression in many samples using vertical arrays

Microarrays and high-throughput sequencing methods can be used to measure the expression of thousands of genes in a biological sample in a few days, whereas PCR-based methods can be used to measure the expression of a few genes in thousands of samples in about the same amount of time. These methods become more costly as the number of biological samples increases or as the number of genes of interest increases, respectively, and these factors constrain experimental design. To address these issues, we introduced ‘vertical arrays’ in which RNA from each biological sample is converted into multiple, overlapping cDNA subsets and spotted on glass slides. These vertical arrays can be queried with single gene probes to assess the expression behavior in thousands of biological samples in a single hybridization reaction. The spotted subsets are less complex than the original RNA from which they derive, which improves signal-to-noise ratios. Here, we demonstrate the quantitative capabilities of vertical arrays, including the sensitivity and accuracy of the method and the number of subsets needed to achieve this accuracy for most expressed genes.