Chromosomal microarray analysis (CMA) is performed either by array comparative genomic hybridization or by using a single nucleotide polymorphism array. In the prenatal setting, CMA is on par with traditional karyotyping for detection of major chromosomal imbalances such as aneuploidy and unbalanced rearrangements. CMA offers additional diagnostic benefits by revealing sub-microscopic imbalances or copy number variations that are too small to be seen on a standard G-banded chromosome preparation. These submicroscopic imbalances are also referred to as microdeletions and microduplications, particularly when they include specific genomic regions that are associated with clinical sequelae. Not all microdeletions/duplications are associated with adverse clinical phenotypes and in many cases, their presence is benign. In other cases, they are associated with a spectrum of clinical phenotypes that may range from benign to severe, while in some situations, the clinical significance may simply be unknown. These scenarios present a challenge for prenatal diagnosis, and genetic counseling prior to prenatal CMA greatly facilitates delivery of complex results. In prenatal diagnostic samples with a normal karyotype, chromosomal microarray will diagnose a clinically significant subchromosomal deletion or duplication in approximately 1% of structurally normal pregnancies and 6% with a structural anomaly. Pre-test counseling is also necessary to distinguish the primary differences between the benefits, limitations and diagnostic scope of CMA versus the powerful but limited screening nature of non-invasive prenatal diagnosis using cell-free fetal DNA.
Background: High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for low-cost high-throughput methylation profiling. Both Beta-value and M-value statistics have been used as metrics to measure methylation levels. However, there are no detailed studies of their relations and their strengths and limitations. Results: We demonstrate that the relationship between the Beta-value and M-value methods is a Logit transformation, and show that the Beta-value method has severe heteroscedasticity for highly methylated or unmethylated CpG sites. In order to evaluate the performance of the Beta-value and M-value methods for identifying differentially methylated CpG sites, we designed a methylation titration experiment. The evaluation results show that the M-value method provides much better performance in terms of Detection Rate (DR) and True Positive Rate (TPR) for both highly methylated and unmethylated CpG sites. Imposing a minimum threshold of difference can improve the performance of the M-value method but not the Beta-value method. We also provide guidance for how to select the threshold of methylation differences. Conclusions: The Beta-value has a more intuitive biological interpretation, but the M-value is more statistically valid for the differential analysis of methylation levels. Therefore, we recommend using the M-value method for conducting differential methylation analysis and including the Beta-value statistics when reporting the results to investigators.
Microarray studies have contributed significantly to the current understanding of Borrelia burgdorferi genome content and transcriptional regulation. Here, we describe the use of microarray technology for several aspects of B. burgdorferi genomic analysis.
Microarrays permit the analysis of gene expression, DNA sequence variation, protein levels, tissues, cells and other biological and chemical molecules in a massively parallel format. Robust microarray manufacture, hybridization, detection and data analysis technologies permit novice users to adapt this exciting technology readily, and more experienced users to push the boundaries of discovery.
Previous microarray studies on breast cancer identified multiple tumour classes, of which the most prominent, named luminal and basal, differ in expression of the oestrogen receptor a gene ( ER). We report here the identification of a group of breast tumours with increased androgen signalling and a 'molecular apocrine' gene expression pro. le. Tumour samples from 49 patients with large operable or locally advanced breast cancers were tested on Affymetrix U133A gene expression microarrays. Principal components analysis and hierarchical clustering split the tumours into three groups: basal, luminal and a group we call molecular apocrine. All of the molecular apocrine tumours have strong apocrine features on histological examination (P = 0.0002). The molecular apocrine group is androgen receptor (AR) positive and contains all of the ER-negative tumours outside the basal group. Kolmogorov-Smirnov testing indicates that oestrogen signalling is most active in the luminal group, and androgen signalling is most active in the molecular apocrine group. ERBB2 amplification is commoner in the molecular apocrine than the other groups. Genes that best split the three groups were identified by Wilcoxon test. Correlation of the average expression pro. le of these genes in our data with the expression pro. le of individual tumours in four published breast cancer studies suggest that molecular apocrine tumours represent 8-14% of tumours in these studies. Our data show that it is possible with microarray data to divide mammary tumour cells into three groups based on steroid receptor activity: luminal (ER+AR+), basal (ER+AR+) and molecular apocrine (ER+AR+).
Hypertension is a complex disorder in which multiple genes, pathways, and organ systems simultaneously interact to contribute to the final level of blood pressure. Fully elucidating these interactions is an important area of hypertension research and one in which high-throughput methods such as microarrays can play a key role. With recent advances in microarray technology, reliable and accurate quantification of all known mRNA transcripts in a sample is now routinely performed. In addition, with improved statistical methods and publicly available tools and resources, robust analysis of the large amount of data generated from microarray experiments is now achievable for all research laboratories as will be outlined in this review.
Microarray analysis in glioblastomas is done using either cell lines or patient samples as starting material. A survey of the current literature points to transcript-based microarrays and immunohistochemistry (IHC)-based tissue microarrays as being the preferred methods of choice in cancers of neurological origin. Microarray analysis may be carried out for various purposes including the following: i. To correlate gene expression signatures of glioblastoma cell lines or tumors with response to chemotherapy (DeLay et al., Clin Cancer Res 18(10):2930-2942, 2012). ii. To correlate gene expression patterns with biological features like proliferation or invasiveness of the glioblastoma cells (Jiang et al., PLoS One 8(6):e66008, 2013). iii. To discover new tumor classificatory systems based on gene expression signature, and to correlate therapeutic response and prognosis with these signatures (Huse et al., Annu Rev Med 64(1):59-70, 2013; Verhaak et al., Cancer Cell 17(1):98-110, 2010). While investigators can sometimes use archived tumor gene expression data available from repositories such as the NCBI Gene Expression Omnibus to answer their questions, new arrays must often be run to adequately answer specific questions. Here, we provide a detailed description of microarray methodologies, how to select the appropriate methodology for a given question, and analytical strategies that can be used. Experimental methodology for protein microarrays is outside the scope of this chapter, but basic sample preparation techniques for transcript-based microarrays are included here.
Microarray DNA hybridization techniques have been used widely from basic to applied molecular biology research. Generally, in a DNA microarray, different probe DNA molecules are immobilized on a solid support in groups and form an array of microspots. Then, hybridization to the microarray can be performed by applying sample DNA solutions in either the bulk or the microfluidic manner. Because the immobilized probe DNA binds and retains its complementary target DNA, detection is achieved through the read-out of the tagged markers on the sample target molecules. The recent microfluidic hybridization method shows the advantages of less sample usage and reduced incubation time. Here, sample solutions are confined in microfabricated channels and flow through the probe microarray area. The high surface-to-volume ratio in microchannels of nanolitre volume greatly enhanced the sensitivity as obtained with the bulk solution method. To generate nanolitre flows, different techniques have been developed, and this including electrokinetic control, vacuum suction and syringe pumping. The latter two are pressure-driven methods which are more flexible without the need of considering the physicochemical properties of solutions. Recently, centrifugal force is employed to drive liquid movement in microchannels. This method utilizes the body force from the liquid itself and there are no additional solution interface contacts such as from electrodes or syringes and tubing. Centrifugal force driven flow also features the ease of parallel hybridizations. In this review, we will summarize the recent advances in microfluidic microarray hybridization and compare the applications of various flow methods.