Data quality in genomics and microarrays

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

Integrating Next-Generation Sequencing in the Clinical Pharmacogenomics Workflow

Pharmacogenomics has been recognized as a fundamental tool in the era of personalized medicine with up to 266 drug labels, approved by major regulatory bodies, currently containing pharmacogenomics information. Next-generation sequencing analysis assumes a critical role in personalized medicine, providing a comprehensive profile of an individual's variome, particularly that of clinical relevance, comprising of pathogenic variants and pharmacogenomic biomarkers. Here, we propose a strategy to integrate next-generation sequencing into the current clinical pharmacogenomics workflow from deep resequencing to pharmacogenomics consultation, according to the existing guidelines and recommendations.

Improved Performance of DNA Microarray Multiplex Hybridization Using Probes Anchored at Several Points by Thiol-Ene or Thiol-Yne Coupling Chemistry

Nucleic acid microarray-based assay technology has shown lacks in reproducibility, reliability, and analytical sensitivity. Here, a new strategy of probe attachment modes for silicon-based materials is built up. Thus, hybridization ability is enhanced by combining thiol-ene or thiol-yne click chemistry reactions with a multipoint attachment of polythiolated probes. The viability and performance of this approach was demonstrated by specifically determining Salmonella PCR products up to a 20 pM sensitivity level.

Detecting and correcting systematic variation in large-scale RNA sequencing data

High-throughput RNA sequencing (RNA-seq) enables comprehensive scans of entire transcriptomes, but best practices for analyzing RNA-seq data have not been fully defined, particularly for data collected with multiple sequencing platforms or at multiple sites. Here we used standardized RNA samples with built-in controls to examine sources of error in large-scale RNA-seq studies and their impact on the detection of differentially expressed genes (DEGs). Analysis of variations in guanine-cytosine content, gene coverage, sequencing error rate and insert size allowed identification of decreased reproducibility across sites. Moreover, commonly used methods for normalization (cqn, EDASeq, RUV2, sva, PEER) varied in their ability to remove these systematic biases, depending on sample complexity and initial data quality. Normalization methods that combine data from genes across sites are strongly recommended to identify and remove site-specific effects and can substantially improve RNA-seq studies.

BIOPHYSICAL PROPERTIES OF NUCLEIC ACIDS AT SURFACES RELEVANT TO MICROARRAY PERFORMANCE

Both clinical and analytical metrics produced by microarray-based assay technology have recognized problems in reproducibility, reliability and analytical sensitivity. These issues are often attributed to poor understanding and control of nucleic acid behaviors and properties at solid-liquid interfaces. Nucleic acid hybridization, central to DNA and RNA microarray formats, depends on the properties and behaviors of single strand (ss) nucleic acids (e.g., probe oligomeric DNA) bound to surfaces. ssDNA's persistence length, radius of gyration, electrostatics, conformations on different surfaces and under various assay conditions, its chain flexibility and curvature, charging effects in ionic solutions, and fluorescent labeling all influence its physical chemistry and hybridization under assay conditions. Nucleic acid (e.g., both RNA and DNA) target interactions with immobilized ssDNA strands are highly impacted by these biophysical states. Furthermore, the kinetics, thermodynamics, and enthalpic and entropic contributions to DNA hybridization reflect global probe/target structures and interaction dynamics. Here we review several biophysical issues relevant to oligomeric nucleic acid molecular behaviors at surfaces and their influences on duplex formation that influence microarray assay performance. Correlation of biophysical aspects of single and double-stranded nucleic acids with their complexes in bulk solution is common. Such analysis at surfaces is not commonly reported, despite its importance to microarray assays. We seek to provide further insight into nucleic acid-surface challenges facing microarray diagnostic formats that have hindered their clinical adoption and compromise their research quality and value as genomics tools.

Utilizing toxicogenomic data to understand chemical mechanism of action in risk assessment

The predominant role of toxicogenomic data in risk assessment, thus far, has been one of augmentation of more traditional in vitro and in vivo toxicology data. This article focuses on the current available examples of instances where toxicogenomic data has been evaluated in human health risk assessment (e.g., acetochlor and arsenicals) which have been limited to the application of toxicogenomic data to inform mechanism of action. This article reviews the regulatory policy backdrop and highlights important efforts to ultimately achieve regulatory acceptance. A number of research efforts on specific chemicals that were designed for risk assessment purposes have employed mechanism or mode of action hypothesis testing and generating strategies. The strides made by large scale efforts to utilize toxicogenomic data in screening, testing, and risk assessment are also discussed. These efforts include both the refinement of methodologies for performing toxicogenomics studies and analysis of the resultant data sets. The current issues limiting the application of toxicogenomics to define mode or mechanism of action in risk assessment are discussed together with interrelated research needs. In summary, as chemical risk assessment moves away from a single mechanism of action approach toward a toxicity pathway-based paradigm, we envision that toxicogenomic data from multiple technologies (e.g., proteomics, metabolomics, transcriptomics, supportive RT-PCR studies) can be used in conjunction with one another to understand the complexities of multiple, and possibly interacting, pathways affected by chemicals which will impact human health risk assessment.

Observing photophysical properties of quantum dots in air at the single molecule level: advantages in microarray applications

Quantum dots (QDs) are promising fluorescent tags for microarrays. Because most microarrays are analyzed under dry conditions, it is necessary to examine the photo properties of QDs in air. We demonstrate that the photophysical characteristics of individual quantum dots are different at the liquid/solid interface compared with QDs at the air/solid interface by observing them through a wide-field fluorescence microscope. QDs in air show higher photo-stability, higher fluorescence signal, slower spectral blue shift rate, less blinking and shorter bulk fluorescence lifetime than those in solution. These beneficial properties indicate QDs are good alternative fluorescent probes for microarrays.

PCR-free quantification of multiple splice variants in a cancer gene by surface-enhanced Raman spectroscopy

We demonstrate a surface-enhanced Raman spectroscopy (SERS) based array platform to monitor gene expression in cancer cells in a multiplex and quantitative format without amplification steps. A strategy comprising DNA/RNA hybridization, S1 nuclease digestion, and alkaline hydrolysis was adopted to obtain DNA targets specific to two splice junction variants, Delta(9,10) and Delta(5), of the breast cancer susceptibility gene 1 from MCF-7 and MDA-MB-231 breast cancer cell lines. These two targets were identified simultaneously, and their absolute quantities were estimated by a SERS strategy utilizing the inherent plasmon-phonon Raman mode of gold nanoparticle probes as a self-referencing standard to correct for the variability in surface enhancement. The results were then validated by reverse-transcription polymerase chain reaction. Our proposed methodology could be expanded to a higher level of multiplexing for quantitative gene expression analysis of any gene without any amplification steps.

Development of Potential Pharmacodynamic and Diagnostic Markers for Anti-IFN-α Monoclonal Antibody Trials in Systemic Lupus Erythematosus

To identify potential pharmacodynamic biomarkers to guide dose selection in clinical trials using anti-interferon-alpha (IFN-α) monoclonal antibody (mAb) therapy for systemic lupus erythematosus (SLE), we used an Affymetrix human genome array platform and identified 110 IFN-α/β-inducible transcripts significantly upregulated in whole blood (WB) of 41 SLE patients. The overexpression of these genes was confirmed prospectively in 54 additional SLE patients and allowed for the categorization of the SLE patients into groups of high, moderate, and weak overexpressers of IFN-α/β-inducible genes. This approach could potentially allow for an accurate assessment of drug target neutralization in early trials of anti-IFN-α mAb therapy for SLE. Furthermore, ex vivo stimulation of healthy donor peripheral blood mononuclear cells with SLE patient serum and subsequent neutralization with anti-IFN-α mAb or anti-IFN-α receptor mAb showed that anti-IFN-α mAb has comparable effects of neutralizing the overexpression of type I IFN-inducible genes as that of anti-IFNAR mAb. These results suggest that IFN-α, and not other members of type I IFN family in SLE patients, is mainly responsible for the induction of type I IFN-inducible genes in WB of SLE patients. Taken together, these data strengthen the view of IFN-α as a therapeutic target for SLE.

Quality control in microarray assessment of gene expression in human airway epithelium

Background

Microarray technology provides a powerful tool for defining gene expression profiles of airway epithelium that lend insight into the pathogenesis of human airway disorders. The focus of this study was to establish rigorous quality control parameters to ensure that microarray assessment of the airway epithelium is not confounded by experimental artifact. Samples (total n = 223) of trachea, large and small airway epithelium were collected by fiberoptic bronchoscopy of 144 individuals and hybridized to Affymetrix microarrays. The pre- and post-chip quality control (QC) criteria established, included: (1) RNA quality, assessed by RNA Integrity Number (RIN) ≥ 7.0; (2) cRNA transcript integrity, assessed by signal intensity ratio of GAPDH 3' to 5' probe sets ≤ 3.0; and (3) the multi-chip normalization scaling factor ≤ 10.0.

Results

Of the 223 samples, all three criteria were assessed in 191; of these 184 (96.3%) passed all three criteria. For the remaining 32 samples, the RIN was not available, and only the other two criteria were used; of these 29 (90.6%) passed these two criteria. Correlation coefficients for pairwise comparisons of expression levels for 100 maintenance genes in which at least one array failed the QC criteria (average Pearson r = 0.90 ± 0.04) were significantly lower (p < 0.0001) than correlation coefficients for pairwise comparisons between arrays that passed the QC criteria (average Pearson r = 0.97 ± 0.01). Inter-array variability was significantly decreased (p < 0.0001) among samples passing the QC criteria compared with samples failing the QC criteria.

Conclusion

Based on the aberrant maintenance gene data generated from samples failing the established QC criteria, we propose that the QC criteria outlined in this study can accurately distinguish high quality from low quality data, and can be used to delete poor quality microarray samples before proceeding to higher-order biological analyses and interpretation.

Nanoliter high-throughput RT-qPCR: a statistical analysis and assessment

Biomarkers discovered from gene expression profiles using hybridization microarrays have made great inroads in the diagnosis and development of safer and efficacious drugs. The candidate gene set is biologically validated by quantitative measurement with reverse transcriptase quantitative PCR (RT-qPCR) and is an effective strategy when implemented with microplates if the number of candidate genes and samples is small. With the trend toward informative candidate gene panels increasing from tens to hundreds of genes and sample cohorts exceeding several hundred, an alternative fluidic approach is needed that preserves the intrinsic analytical precision, large dynamic range, and high sensitivity of RT-qPCR, yet is scalable to high throughputs. We have evaluated the performance of a nanoliter fluidic system that enables up to 3072 nanoliter RT-qPCR assays simultaneously in a high-density array format. We measured the transcription from two different adult human tissues to assess measurement reproducibility across replicates, measurement accuracy, precision, specificity, and sensitivity; determined the false positive rate (FPR) and false negative rate (FNR) of the expressed transcript copies; and determined differences in kinase gene expression reflecting tissue and dosage differences. Using our methodology, we confirm the potential of this technology in advancing pharmaceutical research and development.

Perspectives of DNA microarray and next-generation DNA sequencing technologies

DNA microarray and next-generation DNA sequencing technologies are important tools for high-throughput genome research, in revealing both the structural and functional characteristics of genomes. In the past decade the DNA microarray technologies have been widely applied in the studies of functional genomics, systems biology and pharmacogenomics. The next-generation DNA sequencing method was first introduced by the 454 Company in 2003, immediately followed by the establishment of the Solexa and Solid techniques by other biotech companies. Though it has not been long since the first emergence of this technology, with the fast and impressive improvement, the application of this technology has extended to almost all fields of genomics research, as a rival challenging the existing DNA microarray technology. This paper briefly reviews the working principles of these two technologies as well as their application and perspectives in genome research.

Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

Methods

We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue.

Results

Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR.

Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified.

Conclusion

The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.

Diagnostic devices as biomaterials: a review of nucleic acid and protein microarray surface performance issues

This review of current DNA and protein microarray diagnostic and bio-analytical technologies focuses on the different surface chemistries used in these miniaturized surface-capture formats. Description of current strategies in bio-immobilization and coupling to create multiplexed affinity bioassays in micrometer-sized printed spots, problems with current formats and review of some detection methods are included. Recommendations for improving long-standing challenges in DNA- and protein-based arrays are forwarded. The biomaterials community can contribute relevant expertise to these formidable bio-interfacial problems that represent significant barriers to clinical implementation of microarray assays.

Statistical issues on the diagnostic multivariate index assay for targeted clinical trials

In the past decade, pharmacogenomics and microarrays are considered two of the most important scientific breakthroughs for detection and treatment of diseases with many other applications. After completion of the Human Genome Project (HGP), the importance of diagnostic tests for identification of molecular targets increases as more targeted clinical trials are conducted for the individualized treatment of patients in the post-genomic era. As a result, the co-development of drug-device has become the foundation for achieving the ultimate goal of personalized medicine. One of the diagnostic devices for detection of molecular targets is the in vitro diagnostic multivariate index assays (IVDMIA) based on the genomic composite biomarker (GCB) classifiers. Thus, the quality of the IVDMIA, innovative designs, and the evaluation of efficacy for targeted clinical trials are vital for achieving this goal. However, before personalized medicine becomes a reality, many challenges for assurance of the accuracy and precision of the IVDMIA and the estimates of the treatment effect in the population with the molecular targets are to be resolved. In this paper, we identify the following issues on the IVDMIA and targeted clinical trials: (i) the selection of the differentially expressed genes, (ii) the optimal representation and algorithm for the genomic composite biomarker (GCB) classifier for the best diagnostic accuracy of the molecular target, (iii) the validation of the IVDMIA, and (iv) the evaluation of effectiveness and sample size estimation for targeted clinical trials. For each issue, the problem and possible resolutions are discussed. An overall assessment and some concluding remarks are also provided.

Label-less fluorescence-based method to detect hybridization with applications to DNA micro-array

By coupling scattered light from DNA to excite fluorescence in a polymer, we describe a quantitative, label-free assay for DNA hybridization detection. Since light scattering is intrinsically proportional to number of molecules, the change in (scattering coupled) fluorescence is highly linear with respect to percent binding of single stranded DNA (ssDNA) target with the immobilized ssDNA probes. The coupling is achieved by immobilizing ssDNA on a fluorescent polymer film at optimum thickness in nanoscale. The fluorescence from the underlining polymer increases due to proportionate increase in scattering from double stranded DNA (dsDNA) (i.e., probe-target binding) compared to ssDNA (i.e., probe). Because the scattering is proportional to fourth power of refractive index, the detection of binding is an order of magnitude more sensitive compared to other label-free optical methods, such as, reflectivity, interference, ellipsometry and surface-plasmon resonance. Remarkably, polystyrene film of optimum thickness 30 nm is the best fluorescent agent since its excitation wavelength matches (within 5 nm) with wavelength for the maximum refractive index difference between ssDNA and dsDNA. A quantitative model (with no fitting parameters) explains the observations. Potential dynamic range is 1 in 10(4) at signal-to-noise ratio of 3:1.

A pathologist's perspective on emerging genomic tests for breast cancer

Emerging molecular tests for breast cancer indicate how future progress in diagnostic pathology might guide patient care. However, the challenge will be to bring the best results from this exciting research into clinical use as accurate and reliable standardized tests that integrate into the diagnostic work-up.

A ratiometric lectin microarray approach to analysis of the dynamic mammalian glycome

Glycosylation creates an intricate and complex code for biological information that plays a role in cell-cell communication, infection, and immunity among many biological events. Dynamic changes in the glycosylation status of cells have been observed in tumor cell metastasis and cell differentiation but have been difficult to analyze because of a lack of high-throughput and facile technologies. Here, we present a method for the rapid evaluation of differences in the glycosylation of heterogeneous mammalian samples using a ratiometric two-color lectin microarray approach. This work represents a significant improvement in glycomics technology and sets the stage for the systematic evaluation of how glycans encode biological information in complex systems.

Application of toxicogenomics to genetic toxicology risk assessment

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.

Plastic and adaptive gene expression patterns associated with temperature stress in Arabidopsis thaliana

Transcriptional profiling using DNA microarrays has become a widely used approach for identifying genes with important roles in stress-regulatory networks. In previous studies, genes exhibiting a plastic expression pattern with respect to stress and control treatments have been identified as candidates with putative roles in stress-response pathways. This approach, however, often identifies numerous genes, and it is difficult to discern which genes have major effects that impact the fitness of individuals under stress. In this study, we investigated the impacts of temperature stress (cold and heat) on gene expression in the Arabidopsis thaliana model system. We identified genes exhibiting plastic patterns of gene expression with respect to temperature stress, but in contrast to previous studies, we also considered the adaptive significance of genes by examining their expression patterns among 10 Arabidopsis ecotypes indigenous to a range of latitudes. Our findings support a general association between plasticity of gene expression and adaptive value. In comparison to non-plastic genes, genes exhibiting plastic expression patterns were associated with greater among-ecotype variation in expression levels, and such variation was more strongly correlated with geographical temperature gradients. Surprisingly, while more than 16,000 genes were associated with plastic expression patterns, significant evidence of both expression plasticity and adaptive value was obtained for only 43 genes. These selected genes represent strong candidates for future experimental investigations into the molecular basis of temperature acclimation in the A. thaliana model system.

dbZach toxicogenomic information management system

Quantitative risk assessment and the elucidation of mechanisms of toxicity require innovative computational analysis and infrastructure capable of integrating all levels of biological data organization. Enterprise data management solutions provide the capability to manage and integrate disparate data throughout the source-to-outcome continuum. These capabilities enable software engineers and developers to create and validate software solutions to facilitate the identification of more predictive biomarkers for exposures, molecular responses and toxicity, allowing evaluation of historical assessments and the comparison of data across technologies, species and chemical classes. The toxicogenomic information management system dbZach is a modular relational database with associated data insertion, retrieval and mining tools that manages traditional toxicology and complementary toxicogenomic data to facilitate comprehensive data integration, analysis and sharing. To achieve widescale toxicogenomic data reporting and sharing, a dialog with other toxicogenomic database groups and the toxicogenomics community has emerged to identify needs and requirements, as well as establish the structure of data reporting and exchange standards.

[Advances in "omics" technologies for toxicological research]

Toxicology research can be applied to evaluate potential human health risks resulting from exposure to chemicals and other factors in the environment. The tremendous advances that have been made in high-throughput "omics" technologies (e.g., genomics, transcriptomics, proteomics and metabolomics) are providing good tools for toxicological research. Toxicogenomics is the study of changes in gene expression, protein and metabolite profiles, and combines the tools of traditional toxicology with those of genomics and bioinformatics. In particular, identification of changes in gene expression using DNA microarrays is an important method for understanding toxicological processes and obtaining an informative biomarker. Although these technologies have emerged as a powerful tool for clarifying hazard mechanisms, there are some concerns for the application of these technologies to toxicological research. This review summarizes the impact of "omics" technologies in toxicological study, followed by a brief discussion of future research.

Cross-species comparative toxicogenomics as an aid to safety assessment

Cross-species comparative toxicogenomics has the potential for improving the understanding of the different responses of animal models to toxicants at a molecular level. This understanding could then lead to a more accurate extrapolation of the risk posed by these toxicants to humans. Cross-species comparative studies have been carried out at the genomic sequence level and using microarrays to examine changes in global mRNA profiles. However, these studies face considerable bioinformatic challenges in terms of identifying which genes are truly orthologous across species. The resources to analyse such studies, in the context of such orthologues, beg improvement. Finally, the experimental design of such studies needs to be carefully considered to make their results fully interpretable. These issues are discussed, along with the current state-of-the-art cross-species comparative toxicogenomics in this review.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.