Impact of DNA chips on haematological oncology

As the Human Genome Project hurtles towards completion, DNA microarray technology offers the potential to open wide new windows into the study of genome complexity. DNA chips can be used for many different purposes, most prominently to measure levels of gene expression (messenger RNA abundance) for tens of thousands of genes simultaneously. But how much of this data is useful and is some superfluous? Can array data be used to identify a handful of critical genes that will lead to a more detailed taxonomy of haematological malignancies and can this or similar array data be used to predict clinical outcome? It is still too early to predict what the ultimate impact of DNA chips will be on our understanding of cancer biology. There are many critically important questions about this new field that are yet unaddressed. By the publication of this article, it is hoped that the technology of DNA chips will be opened up and demystified, and that additional opportunities for creative exploration will be catalysed.     

SuperSAGE array: the direct use of 26-base-pair transcript tags in oligonucleotide arrays

We developed a new platform for genome-wide gene expression analysis in any eukaryotic organism, which we called SuperSAGE array. The SuperSAGE array is a microarray onto which 26-bp oligonucleotides corresponding to SuperSAGE tag sequences are directly synthesized. A SuperSAGE array combines the advantages of the highly quantitative SuperSAGE expression analysis with the high-throughput microarray technology. We demonstrated highly reproducible gene expression profiling by the SuperSAGE array for 1,000 genes (tags) in rice. We also applied this technology to the detailed study of expressed genes identified by SuperSAGE in Nicotiana benthamiana, an organism for which sufficient genome sequence information is not available. We propose that the SuperSAGE array system represents a new paradigm for microarray construction, as no genomic or cDNA sequence data are required for its preparation.     

New tissue microarray technology for analyses of gene expression in frozen pathological samples

Tissue microarrays (TMAs) are widely used to analyze gene expression in multiple pathological samples on a single slide. Currently, most TMA slides are made by coring paraffin-embedded tissues and arraying them into a paraffin block, from which TMA sections are cut. However paraffin-based TMA technology may not be compatible with frozen clinical tissue samples, which have a higher quality of RNAs and proteins for preparing TMAs than paraffin-embedded tissue samples. In this study, we developed an alternative TMA technology that is applicable to a broader range of frozen tissue samples. Our method takes advantage of a newly developed array recipient block that can be used to array small tissue cores. After arraying tissue cores, the tissue block can be immediately sectioned on a cryostat microtome to make TMA slides. TMAs made using this method have well-defined array configurations and good tissue/cell morphology. Immunohistochemistry and in situ hybridization study also revealed well-preserved proteins and mRNAs on TMA slides. Our method significantly simplifies TMA preparation and assembly when frozen pathological tissues are used. Our technology provides an alternative tool for creating high-quality TMAs for the general research community to study gene expressions in pathological samples.     

Survey of the 2009 commercial optical biosensor literature

We took a different approach to reviewing the commercial biosensor literature this year by inviting 22 biosensor users to serve as a review committee. They set the criteria for what to expect in a publication and ultimately decided to use a pass/fail system for selecting which papers to include in this year's reference list. Of the 1514 publications in 2009 that reported using commercially available optical biosensor technology, only 20% passed their cutoff. The most common criticism the reviewers had with the literature was that "the biosensor experiments could have been done better." They selected 10 papers to highlight good experimental technique, data presentation, and unique applications of the technology. This communal review process was educational for everyone involved and one we will not soon forget.     

Normalization of cDNA microarray data

Normalization means to adjust microarray data for effects which arise from variation in the technology rather than from biological differences between the RNA samples or between the printed probes. This paper describes normalization methods based on the fact that dye balance typically varies with spot intensity and with spatial position on the array. Print-tip loess normalization provides a well-tested general purpose normalization method which has given good results on a wide range of arrays. The method may be refined by using quality weights for individual spots. The method is best combined with diagnostic plots of the data which display the spatial and intensity trends. When diagnostic plots show that biases still remain in the data after normalization, further normalization steps such as plate-order normalization or scale-normalization between the arrays may be undertaken. Composite normalization may be used when control spots are available which are known to be not differentially expressed. Variations on loess normalization include global loess normalization and two-dimensional normalization. Detailed commands are given to implement the normalization techniques using freely available software.     

Using DNA microarrays to study gene expression in closely related species

MOTIVATION: Comparisons of gene expression levels within and between species have become a central tool in the study of the genetic basis for phenotypic variation, as well as in the study of the evolution of gene regulation. DNA microarrays are a key technology that enables these studies. Currently, however, microarrays are only available for a small number of species. Thus, in order to study gene expression levels in species for which microarrays are not available, researchers face three sets of choices: (i) use a microarray designed for another species, but only compare gene expression levels within species, (ii) construct a new microarray for every species whose gene expression profiles will be compared or (iii) build a multi-species microarray with probes from each species of interest. Here, we use data collected using a multi-primate cDNA array to evaluate the reliability of each approach. RESULTS: We find that, for inter-species comparisons, estimates of expression differences based on multi-species microarrays are more accurate than those based on multiple species-specific arrays. We also demonstrate that within-species expression differences can be estimated using a microarray for a closely related species, without discernible loss of information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Potency assays for therapeutic live whole cell cancer vaccines.

Therapeutic cancer vaccines are under development with the goal of enhancing the body's immune response to cancer cells sufficient to arrest cancer cell growth. Among the various approaches being used are those based on whole tumor cells. Developing a suitable measure of the potency of such vaccines presents a significant challenge because neither cellular associated markers nor in vivo biological responses that are correlated with efficacy have been identified; nevertheless, manufacturers and regulatory agencies will need to develop methods to evaluate these products. At this moment, the challenge for manufacturers who are developing whole cell vaccines is to demonstrate batch-to-batch consistency for the vaccine used in clinical studies and to show that comparable vaccine batches have the same capacity to achieve an acceptable level of biological activity that may be related to efficacy. This is particularly challenging in that animal models to test that activity do not exist and direct serological or immunological correlates of clinical protection are not available because protection has not yet been established in clinical trials. In the absence of well-defined biological markers and tests for manufacturing consistency, manufacturers and regulators will need to rely heavily on a highly reproducible manufacturing process--the consistency of the process therefore becomes critical. In developing regulatory approaches to whole cell cancer vaccines, the experience from the field of infectious disease vaccines should be examined for general guidance. A framework that draws heavily on the field of infectious disease vaccines is presented and suggests that at this point in the development of this new class of products, it is reasonable to develop data on quantitative antigen expression as a measure of potency with the expectation that when clinical efficacy has been established it will confirm the appropriateness of this approach. But because this will not be known until the end of a pivotal trial, a bioassay should be considered and run in parallel. Several examples of bioassays are presented along with their advantages and disadvantages. The final selection of a potency assay for use in lot release of a commercializable therapeutic whole cell vaccine ultimately will depend on the totality of the data available at the time of approval by regulatory agencies. Based on information currently available, it is likely that quantitative antigen expression or a bioassay could be used to measure potency. If both are determined to be acceptable, the use of quantitative antigen expression could be considered for routine lot release, while the bioassay could be reserved for use as one of the elements in establishing comparability when manufacturing changes are being considered after approval.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

Comparison of Affymetrix GeneChip expression measures

MOTIVATION: In the Affymetrix GeneChip system, preprocessing occurs before one obtains expression level measurements. Because the number of competing preprocessing methods was large and growing we developed a benchmark to help users identify the best method for their application. A webtool was made available for developers to benchmark their procedures. At the time of writing over 50 methods had been submitted. RESULTS: We benchmarked 31 probe set algorithms using a U95A dataset of spike in controls. Using this dataset, we found that background correction, one of the main steps in preprocessing, has the largest effect on performance. In particular, background correction appears to improve accuracy but, in general, worsen precision. The benchmark results put this balance in perspective. Furthermore, we have improved some of the original benchmark metrics to provide more detailed information regarding precision and accuracy. A handful of methods stand out as providing the best balance using spike-in data with the older U95A array, although different experiments on more current arrays may benchmark differently. AVAILABILITY: The affycomp package, now version 1.5.2, continues to be available as part of the Bioconductor project (http://www.bioconductor.org). The webtool continues to be available at http://affycomp.biostat.jhsph.edu CONTACT: rafa@jhu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Exploring the functional landscape of gene expression: directed search of large microarray compendia

MOTIVATION: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. RESULTS: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. AVAILABILITY: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL. SUPPLEMENTARY INFORMATION: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement.     

Fundamentals of DNA hybridization arrays for gene expression analysis

DNA hybridization arrays [also known as macroarrays, microarrays and/or high-density oligonucleotide arrays (Gene Chips)] bring gene expression analysis to a genomic scale by permitting investigators to simultaneously examine changes in the expression of literally thousands of genes. For hybridization arrays, the general approach is to immobilize gene-specific sequences (probes) on a solid state matrix (nylon membranes, glass microscope slides, silicon/ceramic chips). These sequences are then queried with labeled copies of nucleic acids from biological samples (targets). The underlying theory is that the greater the expression of a gene, the greater the amount of labeled target, and hence, the greater output signal. In spite of the simplicity of the experimental design, there are at least four different platforms and several different approaches to processing and labeling the biological samples. Moreover, investigators must also determine whether they will utilize commercially available arrays or generate their own. This review will cover the status of the hybridization array field with an eye toward underlying principles and available technologies. Future developments and technological trends will also be evaluated.     

Accurate detection of aneuploidies in array CGH and gene expression microarray data

MOTIVATION: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. RESULTS: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation-maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. AVAILABILITY: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/     

Integrating probe-level expression changes across generations of Affymetrix arrays

There is an urgent need for bioinformatic methods that allow integrative analysis of multiple microarray data sets. While previous studies have mainly concentrated on reproducibility of gene expression levels within or between different platforms, we propose a novel meta-analytic method that takes into account the vast amount of available probe-level information to combine the expression changes across different studies. We first show that the comparability of relative expression changes and the consistency of differentially expressed genes between different Affymetrix array generations can be considerably improved by determining the expression changes at the probe-level and by considering the latest information on probe-level sequence matching instead of the probe annotations provided by the manufacturer. With the improved probe-level expression change estimates, data from different generations of Affymetrix arrays can be combined more effectively. This will allow for the full exploitation of existing results when designing and analyzing new experiments.     

Quality control parameters on a large dataset of regionally dissected human control brains for whole genome expression studies

We are building an open-access database of regional human brain expression designed to allow the genome-wide assessment of genetic variability on expression. Array and RNA sequencing technologies make assessment of genome-wide expression possible. Human brain tissue is a challenging source for this work because it can only be obtained several and variable hours post-mortem and after varying agonal states. These variables alter RNA integrity in a complex manner. In this report, we assess the effect of post-mortem delay, agonal state and age on gene expression, and the utility of pH and RNA integrity number as predictors of gene expression as measured on 1266 Affymetrix Exon Arrays. We assessed the accuracy of the array data using QuantiGene, as an independent non-PCR-based method. These quality control parameters will allow database users to assess data accuracy. We report that within the parameters of this study post-mortem delay, agonal state and age have little impact on array quality, array data are robust to variable RNA integrity, and brain pH has only a small effect on array performance. QuantiGene gave very similar expression profiles as array data. This study is the first step in our initiative to make human, regional brain expression freely available.