Arrays of arrays for high-throughput gene expression profiling

Inter-Laboratory Variability in Array-Based RNA Quantification Methods

Ribonucleic acids (RNA) are hypothesized to have preceded their derivatives, deoxyribonucleic acids (DNA), as the molecular media of genetic information when life emerged on earth. Molecular biologists are accustomed to the dramatic effects a subtle variation in the ribose moiety composition between RNA and DNA can have on the stability of these molecules. While DNA is very stable after extraction from biological samples and subsequent treatment, RNA is notoriously labile. The short half-life property, inherent to RNA, benefits cells that do not need to express their entire repertoire of proteins. The cellular machinery turns off the production of a given protein by shutting down the transcription of its cognate coding gene and by either actively degrading the remaining mRNA or allowing it to decay on its own. The steady-state level of each mRNA in a given cell varies continuously and is specified by changing kinetics of synthesis and degradation. Because it is technically possible to simultaneously measure thousands of nucleic acid molecules, these quantities have been studied by the life sciences community to investigate a range of biological problems. Since the RNA abundance can change according to a wide range of perturbations, this makes it the molecule of choice for exploring biological systems; its instability, on the other hand, could be an underestimated source of technical variability. We found that a large fraction of the RNA abundance originally present in the biological system prior to extraction was masked by the RNA labeling and measurement procedure. The method used to extract RNA molecules from cells and to label them prior to hybridization operations on DNA arrays affects the original distribution of RNA. Only if RNA measurements are performed according to the same procedure can biological information be inferred from the assay read out.

Potentiometric Detection of DNA Molecules Hybridization Using Gene Field Effect Transistor and Intercalator

We propose a new concept of a gene field effect transistor (FET) for detection of allele specific oligonucleotide hybridization, which is in principle based on charge density change at the gate insulator. The electrical characteristics of the gene FET were found to shift after specific binding of biomolecules at the surface of the gate insulator. Allele specific oligonucleotide hybridization and reaction between double-stranded DNA and intercalator were successfully detected with gene FETs because they have intrinsic charges in an aqueous solution. Ability to discriminate single nucleotide polymorphism (SNP) was also examined using the gene FET. Our results show that control of hybridization temperature and utilization of intercalator lead to more precise SNP analysis using the gene FET.

Detection of pathogens in foods: the current state-of-the-art and future directions

Over the last fifty years, microbiologists have developed reliable culture-based techniques to detect food borne pathogens. Although these are considered to be the "gold-standard," they remain cumbersome and time consuming. Despite the advent of rapid detection methods such as ELISA and PCR, it is clear that reduction and/or elimination of cultural enrichment will be essential in the quest for truly real-time detection methods. As such, there is an important role for bacterial concentration and purification from the sample matrix as a step preceding detection, so-called pre-analytical sample processing. This article reviews recent advancements in food borne pathogen detection and discusses future methods with a focus on pre-analytical sample processing, culture independent methods, and biosensors.

A genome-scale map of expression for a mouse brain section obtained using voxelation

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

Identification of rat lung--prominent genes by a parallel DNA microarray hybridization

Background

The comparison of organ transcriptomes is an important strategy for understanding gene functions. In the present study, we attempted to identify lung-prominent genes by comparing the normal transcriptomes of rat lung, heart, kidney, liver, spleen, and brain. To increase the efficiency and reproducibility, we first developed a novel parallel hybridization system, in which 6 samples could be hybridized onto a single slide at the same time.

Results

We identified the genes prominently expressed in the lung (147) or co-expressed in lung-heart (23), lung-liver (37), lung-spleen (203), and lung-kidney (98). The known functions of the lung-prominent genes mainly fell into 5 categories: ligand binding, signal transducer, cell communication, development, and metabolism. Real-time PCR confirmed 13 lung-prominent genes, including 5 genes that have not been investigated in the lung, vitamin D-dependent calcium binding protein (Calb3), mitogen activated protein kinase 13 (Mapk13), solute carrier family 29 transporters, member 1 (Slc29a1), corticotropin releasing hormone receptor (Crhr1), and lipocalin 2 (Lcn2).

Conclusion

The lung-prominent genes identified in this study may provide an important clue for further investigation of pulmonary functions.

Mass Spectrometry: Uncovering the Cancer Proteome for Diagnostics

Despite impressive scientific achievements over the past few decades, cancer is still a leading cause of death. One of the major reasons is that most cancer patients are diagnosed with advanced disease. This is clearly illustrated with ovarian cancer in which the overall 5-year survival rates are only 20-30%. Conversely, when ovarian cancer is detected early (stage 1), the 5-year survival rate increases to 95%. Biomarkers, as tools for preclinical detection of cancer, have the potential to revolutionize the field of clinical diagnostics. The emerging field of clinical proteomics has found applications across a wide spectrum of cancer research. This chapter will focus on mass spectrometry as a proteomic technology implemented in three areas of cancer: diagnostics, tissue imaging, and biomarker discovery. Despite its power, it is also important to realize the preanalytical, analytical, and postanalytical limitations currently associated with this methodology. The ultimate endpoint of clinical proteomics is individualized therapy. It is essential that research groups, the industry, and physicians collaborate to conduct large prospective, multicenter clinical trials to validate and standardize this technology, for it to have real clinical impact.

Applications of DNA microarrays in biology

DNA microarrays have enabled biology researchers to conduct large-scale quantitative experiments. This capacity has produced qualitative changes in the breadth of hypotheses that can be explored. In what has become the dominant mode of use, changes in the transcription rate of nearly all the genes in a genome, taking place in a particular tissue or cell type, can be measured in disease states, during development, and in response to intentional experimental perturbations, such as gene disruptions and drug treatments. The response patterns have helped illuminate mechanisms of disease and identify disease subphenotypes, predict disease progression, assign function to previously unannotated genes, group genes into functional pathways, and predict activities of new compounds. Directed at the genome sequence itself, microarrays have been used to identify novel genes, binding sites of transcription factors, changes in DNA copy number, and variations from a baseline sequence, such as in emerging strains of pathogens or complex mutations in disease-causing human genes. They also serve as a general demultiplexing tool to sort spatially the sequence-tagged products of highly parallel reactions performed in solution. A brief review of microarray platform technology options, and of the process steps involved in complete experiment workflows, is included.

Potentiometric Detection of Single Nucleotide Polymorphism by Using a Genetic Field-effect transistor

Potentiometric measurement of allele-specific oligonucleotide hybridization based on the principle of detection of charge-density change at the surface of a gate insulator by using of a genetic field-effect transistor has been demonstrated. Since DNA molecules are negatively charged in aqueous solution, a hybridization event at the gate surface leads to a charge-density change in the channel of the FET and can be directly transduced into an electrical signal without any labeling of target DNA molecules. One of the unique features of our method is to utilize DNA binders such as intercalators as charged species for double-stranded DNA after hybridization, since these are ionized and carry positive charges in aqueous solution. Single-base mismatch of the target DNA could be successfully detected both with the wild-type and with the mutant genetic FETs by controlling the hybridization temperatures and introducing Hoechst 33258 as DNA binder. The genetic FET platform is suitable as a simple, accurate, and inexpensive system for SNP typing in clinical diagnostics.

Prediction of chemotherapeutic response in ovarian cancer with DNA microarray expression profiling

Ovarian carcinoma is a leading cause of gynecologic cancer death in women. Despite treatment, a large number of women with ovarian cancer eventually relapse and die of the disease. Hence, recurrent ovarian cancer continues to be a therapeutic dilemma, possibly a result of the emergence of drug resistance during relapse. Recent advances in expression genomics enable global transcript analysis that leads to molecular classification of cancers and prediction of outcome and treatment response. We did a cDNA microarray examination of the expression profiles of eight primary ovarian cancers stratified into two groups based on their chemotherapeutic response. We applied a voice-speech-pattern recognition algorithm for microarray data analysis and were able to model and predict the response of these patients to chemotherapy from their expression profiles. Hence, gene expression profiling by means of DNA microarray may be applied diagnostically for predicting treatment response in ovarian cancer.

Transporter and ion channel gene expression after Caco-2 cell differentiation using 2 different microarray technologies

mRNA expression profiles had previously been measured in Caco-2 cells (human colonic carcinoma cells) using either custom-designed spotted oligonucleotide arrays or Affymetrix GeneChip oligonucleotide arrays. The Caco-2 cells used were from different clones and were examined under slightly different culture conditions commonly encountered when Caco-2 cells are used as a model tissue for studying intestinal transport and metabolism in different laboratories. In this study, we compared gene expression profiles of Caco-2 cells generated with different arrays to assess the validity of conclusions derived from the 2 independent studies, with a focus on changes in transporter and ion channel mRNA expression levels on Caco-2 cell differentiation. Significant changes in expression levels upon differentiation were observed with 78 genes, with probes common to both arrays. Of these, 18 genes were upregulated and 36 genes were downregulated. The 2 arrays yielded discrepant results for 24 genes, showing significant changes upon differentiation. The results from the 2 arrays correlated well for genes expressed above average levels (r = 0.75, P < 0.01, n = 25) and poorly for genes expressed at low levels (r = 0.08, P > 0.05, n = 25). Overall correlation across the 2 platforms was r = 0.45 (P < 0.01) for the 78 genes, with similar results from both arrays. Despite differences in experimental conditions and array technology, similar results were obtained for most genes.

Gene expression patterns in ovarian carcinomas

We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers.

Analysis of gene expression in carbon tetrachloride-treated rat livers using a novel bioarray technology

The present study successfully utilizes a new ADME Rat Expression Bioarray, containing 1040 metabolism- and toxicology-linked genes, to monitor gene expression from the livers of rats treated with carbon tetrachloride (CCl(4)). Histopathological analysis, hierarchical clustering methods, and gene expression profiling are compared between the control and CCl(4)-treated animals. A total of 44 transcripts were found to be altered in response to the hepatotoxin, 19 of which were upregulated and 25 were downregulated. Some of these gene expression changes were expected and concurred with previously published data while others were novel findings.

Global analysis of gene expression using GeneChip microarrays

DNA microarray technology, especially the use of GeneChip microarrays, has become a standard tool for parallel gene expression analysis. Recent improvements in GeneChip microarrays enable whole-genome expression analysis, and thus open a new avenue for studies of the composition, dynamics, and regulation of the transcriptome in plants.

Genotyping on a thermal gradient DNA chip

Silicon-based chips with discrete, independently temperature-controlled islands have been developed for use in DNA microarray hybridization studies. Each island, containing a heater made of a diffusion layer and a temperature sensor based on a p-n junction, is created on a silicon dioxide/nitride surface by anisotropic etching. Different reactive groups are subsequently added to the surface of the islands, and allele-specific oligonucleotide probes are attached to discrete spots on the chip. Hybridization is performed with Cy5-tagged single-stranded targets derived by PCR from genomic DNA. Results are assessed by measuring fluorescence of bound dye-tagged targets after hybridization and washing. Temperatures at each island can be set at different values to obtain optimal distinction between perfect matches and mismatches. This approach facilitates definition of optimal temperatures for probe/target annealing and for distinction between perfectly matched versus mismatched solution-phase targets. The thermal gradient DNA chips were then tested for genotyping, and the results for four different loci in two genes are presented. Unambiguous typing was achieved for clinically relevant loci within the factor VII and hemochromatosis genes.

DNA microarray technology: devices, systems, and applications

In this review, recent advances in DNA microarray technology and their applications are examined. The many varieties of DNA microarray or DNA chip devices and systems are described along with their methods for fabrication and their use. This includes both high-density microarrays for high-throughput screening applications and lower-density microarrays for various diagnostic applications. The methods for microarray fabrication that are reviewed include various inkjet and microjet deposition or spotting technologies and processes, in situ or on-chip photolithographic oligonucleotide synthesis processes, and electronic DNA probe addressing processes. The DNA microarray hybridization applications reviewed include the important areas of gene expression analysis and genotyping for point mutations, single nucleotide polymorphisms (SNPs), and short tandem repeats (STRs). In addition to the many molecular biological and genomic research uses, this review covers applications of microarray devices and systems for pharmacogenomic research and drug discovery, infectious and genetic disease and cancer diagnostics, and forensic and genetic identification purposes. Additionally, microarray technology being developed and applied to new areas of proteomic and cellular analysis are reviewed.

Analysis of subcellularly localized mRNAs using in situ hybridization, mRNA amplification, and expression profiling

Targeting of mRNAs to distinct subcellular regions occurs in all polarized cells. The mechanisms by which RNA transport occurs are poorly understood. With the advent of RNA amplification methodologies and expression profiling it is now possible to catalogue the RNAs that are targeted to particular subcellular regions. In particular, neurons are polarized cells in which dendrites receive signals from presynaptic neurons. Upon stimulation (information receipt) the dendrite processes the information such that an immediate dendritic response is generated as well as a longer-term somatic response. The integrated cellular response results in a signal that can be propagated through the axon to the next post-synaptic neuron. Much previous work has shown that mRNAs can be localized in dendrites and that local translation in dendrites can occur. In this chapter the methods for analysis of RNAs that are localized to dendrites are reviewed and a partial list of dendritically localized RNAs is presented. This information may be useful in identifying RNA regulatory regions that are responsible for specifying rate of RNA transport and the dendritic sites at which targeted RNAs dock so that they can be translated.

A highly reproducible, linear, and automated sample preparation method for DNA microarrays

DNA microarrays are powerful tools to detect changes in transcript abundance in multiple samples in parallel. However, detection of differential transcript levels requires a reproducible sample (target) preparation method in addition to a high-performance microarray. Therefore, we optimized a target-preparation method that converts the poly(A)(+) RNA fraction of total RNA into complementary DNA, then generates biotin-labeled complementary RNA from the cDNA. We measured the efficiency of incorporation of biotin-containing nucleotides by an enzymatic digestion, followed by resolution via analytical high-performance liquid chromatography (HPLC). When the target was hybridized to a sensitive and reproducible microarray platform, low coefficients of variation in both hybridization intensities and differential expression ratios across target preparations were observed. Nearly identical hybridization intensities and expression ratios are observed regardless of whether poly(A)(+)-enriched RNA or total RNA is used as the starting material. We show the ability to discern biological and production variability through the use of different lots of commercial samples as visualized by hierarchical clustering. Automation of the target-preparation procedure shows equivalence to the manual procedure, reproducible yields of target, and low variability as measured by hybridization to microarrays. Most importantly, RNA mixing experiments show a linear and quantitative amplification in probe hybridization signals for >6000 genes across the entire signal range.

Perspective: micoarrays and differential display PCR-tools for studying transcript levels of genes in neuroendocrine systems

A central goal of neuroendocrinology is the understanding of how hormones modulate a variety of neurobiological functions including releasing factors for anterior pituitary secretions and behavior. We know that mechanisms of hormone actions clearly include the activation and repression of genes either directly through nuclear hormone receptors or indirectly, through a series of transduced signals originating from membrane receptors. Until recently, identification of the differentially expressed genes has been a "gene-at-a-time" proposition. With the advent of the completion of sequencing of several genomes including those of the human and mouse, new methods for the simultaneous assessment of many genes' expression are proving especially timely. Two such methods, differential display PCR and gene microarrays, are based on the well-established principles of DNA amplification and nucleic acid hybridization, respectively. With properly designed and well-executed experiments, these methods are powerful tools in the assessment of differentially expressed genes yielding results both expected and unanticipated.