Advancing transcriptome platforms

During the last decade of years, remarkable technological innovations have emerged that allow the direct or indirect determination of the transcriptome at unprecedented scale and speed. Studies using these methods have already altered our view of the extent and complexity of transcript profiling, which has advanced from one-gene-at-a-time to a holistic view of the genome. Here, we outline the major technical advances in transcriptome characterization, including the most popular used hybridization-based platform, the well accepted tag-based sequencing platform, and the recently developed RNA-Seq (RNA sequencing) based platform. Importantly, these next-generation technologies revolutionize assessing the entire transcriptome via the recent RNA-Seq technology

Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer.

Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor

Clinical application of high throughput molecular screening techniques for pharmacogenomics.

Genetic analysis is one of the fastest-growing areas of clinical diagnostics. Fortunately, as our knowledge of clinically relevant genetic variants rapidly expands, so does our ability to detect these variants in patient samples. Increasing demand for genetic information may necessitate the use of high throughput diagnostic methods as part of clinically validated testing. Here we provide a general overview of our current and near-future abilities to perform large-scale genetic testing in the clinical laboratory. First we review in detail molecular methods used for high throughput mutation detection, including techniques able to monitor thousands of genetic variants for a single patient or to genotype a single genetic variant for thousands of patients simultaneously. These methods are analyzed in the context of pharmacogenomic testing in the clinical laboratories, with a focus on tests that are currently validated as well as those that hold strong promise for widespread clinical application in the near future. We further discuss the unique economic and clinical challenges posed by pharmacogenomic markers. Our ability to detect genetic variants frequently outstrips our ability to accurately interpret them in a clinical context, carrying implications both for test development and introduction into patient management algorithms. These complexities must be taken into account prior to the introduction of any pharmacogenomic biomarker into routine clinical testing

Microarray-based ultra-high resolution discovery of genomic deletion mutations

BACKGROUND: Oligonucleotide microarray-based comparative genomic hybridization (CGH) offers an attractive possible route for the rapid and cost-effective genome-wide discovery of deletion mutations. CGH typically involves comparison of the hybridization intensities of genomic DNA samples with microarray chip representations of entire genomes, and has widespread potential application in experimental research and medical diagnostics. However, the power to detect small deletions is low. RESULTS: Here we use a graduated series of Arabidopsis thaliana genomic deletion mutations (of sizes ranging from 4 bp to ~5 kb) to optimize CGH-based genomic deletion detection. We show that the power to detect smaller deletions (4, 28 and 104 bp) depends upon oligonucleotide density (essentially the number of genome-representative oligonucleotides on the microarray chip), and determine the oligonucleotide spacings necessary to guarantee detection of deletions of specified size. CONCLUSIONS: Our findings will enhance a wide range of research and clinical applications, and in particular will aid in the discovery of genomic deletions in the absence of a priori knowledge of their existence

Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects.

Organogenesis involves integration of diverse cell types; dysregulation of cell-type-specific gene networks results in birth defects, which affect 5% of live births. Congenital heart defects are the most common malformations, and result from disruption of discrete subsets of cardiac progenitor cells1, but the transcriptional changes in individual progenitors that lead to organ-level defects remain unknown. Here we used single-cell RNA sequencing to interrogate early cardiac progenitor cells as they become specified during normal and abnormal cardiogenesis, revealing how dysregulation of specific cellular subpopulations has catastrophic consequences. A network-based computational method for single-cell RNA-sequencing analysis that predicts lineage-specifying transcription factors2,3 identified Hand2 as a specifier of outflow tract cells but not right ventricular cells, despite the failure of right ventricular formation in Hand2-null mice4. Temporal single-cell-transcriptome analysis of Hand2-null embryos revealed failure of outflow tract myocardium specification, whereas right ventricular myocardium was specified but failed to properly differentiate and migrate. Loss of Hand2 also led to dysregulation of retinoic acid signalling and disruption of anterior-posterior patterning of cardiac progenitors. This work reveals transcriptional determinants that specify fate and differentiation in individual cardiac progenitor cells, and exposes mechanisms of disrupted cardiac development at single-cell resolution, providing a framework for investigating congenital heart defects

Next-generation sequencing: applications beyond genomes.

The development of DNA sequencing more than 30 years ago has profoundly impacted biological research. In the last couple of years, remarkable technological innovations have emerged that allow the direct and cost-effective sequencing of complex samples at unprecedented scale and speed. These next-generation technologies make it feasible to sequence not only static genomes, but also entire transcriptomes expressed under different conditions. These and other powerful applications of next-generation sequencing are rapidly revolutionizing the way genomic studies are carried out. Below, we provide a snapshot of these exciting new approaches to understanding the properties and functions of genomes. Given that sequencing-based assays may increasingly supersede microarray-based assays, we also compare and contrast data obtained from these distinct approaches

Capturing the ‘ome’ : the expanding molecular toolbox for RNA and DNA library construction

All sequencing experiments and most functional genomics screens rely on the generation of libraries to comprehensively capture pools of targeted sequences. In the past decade especially, driven by the progress in the field of massively parallel sequencing, numerous studies have comprehensively assessed the impact of particular manipulations on library complexity and quality, and characterized the activities and specificities of several key enzymes used in library construction. Fortunately, careful protocol design and reagent choice can substantially mitigate many of these biases, and enable reliable representation of sequences in libraries. This review aims to guide the reader through the vast expanse of literature on the subject to promote informed library generation, independent of the application

Isolation of Unknown Genes from Human Bone Marrow by Differental Screening and Single-Pass cDNA Sequences Determination

A cDNA sequencing project was initiated to characterize gene expression in human bone marrow and develop strategies to isolate novel genes. Forty-eight random cDNAs from total human bone marrow were subjected to single-pass DNA sequence analysis to determine a limited complexity of mRNAs expressed in the bone marrow. Overall, 8 cDNAs (17%) showed no similarity to known sequences. Information from DNA sequence analysis was used to develop a differential prescreen to subtract unwanted cDNAs and to enrich for unknown cDNAs. Forty-eight cDNAs that were negative with a complex probe were subject to single-pass DNA sequence determination. Of these prescreened cDNAs, the number of unknown sequences increased to 23 (48%). Unknown cDNAs were also characterized by RNA expression analysis using 25 different human leukemic cell lines. Of 13 unknown cDNAs tested, 10 were expressed in all cell types tested and 3 revealed a hematopoietic lineage-restricted expression pattern. Interestingly, while a total of only 96 bone marrow cDNAs were sequenced, 31 of these cDNAs represent sequences from unknown genes and 12 showed significant similarities to sequences in the data bases. One cDNA revealed a significant similarity to a serine/threonine-protein kinase at the amino acid level (56% identity for 123 amino acids) and may represent a previously unknown kinase. Differential screening techniques coupled with single-pass cDNA sequence analysis may prove to be a powerful and simple technique to examine developmental gene expression