A robust method for the amplification of RNA in the sense orientation

BACKGROUND: Small quantities of RNA (1–4 μg total RNA) available from biological samples frequently require a single round of amplification prior to analysis, but current amplification strategies have limitations that may restrict their usefulness in downstream genomic applications. The Eberwine amplification method has been extensively validated but is limited by its ability to produce only antisense RNA. Alternatives lack extensive validation and are often confounded by problems with bias or yield attributable to their greater biological and technical complexity. RESULTS: To overcome these limitations, we have developed a straightforward and robust protocol for amplification of RNA in the sense orientation. This protocol is based upon Eberwine's method but incorporates elements of more recent amplification techniques while avoiding their complexities. Our technique yields greater than 100-fold amplification, generates long transcript, and produces mRNA that is well suited for use with microarray applications. Microarrays performed with RNA amplified using this protocol demonstrate minimal amplification bias and high reproducibility. CONCLUSION: The protocol we describe here is readily adaptable for the production of sense or antisense, labeled or unlabeled RNA from intact or partially-degraded prokaryotic or eukaryotic total RNA. The method outperforms several commercial RNA amplification kits and can be used in conjunction with a variety of microarray platforms, such as cDNA arrays, oligonucleotide arrays, and Affymetrix GeneChip™ arrays

X-Ray Spectroscopy of Ultra-Thin Oxide/Oxide Heteroepitaxial Films: A Case Study of Single-Nanometer VO2/TiO2

Epitaxial ultra-thin oxide films can support large percent level strains well beyond their bulk counterparts, thereby enabling strain-engineering in oxides that can tailor various phenomena. At these reduced dimensions (typically \u3c 10 nm), contributions from the substrate can dwarf the signal from the epilayer, making it difficult to distinguish the properties of the epilayer from the bulk. This is especially true for oxide on oxide systems. Here, we have employed a combination of hard X-ray photoelectron spectroscopy (HAXPES) and angular soft X-ray absorption spectroscopy (XAS) to study epitaxial VO2/TiO2 (100) films ranging from 7.5 to 1 nm. We observe a low-temperature (300 K) insulating phase with evidence of vanadium-vanadium (V-V) dimers and a high-temperature (400 K) metallic phase absent of V-V dimers irrespective of film thickness. Our results confirm that the metal insulator transition can exist at atomic dimensions and that biaxial strain can still be used to control the temperature of its transition when the interfaces are atomically sharp. More generally, our case study highlights the benefits of using non-destructive XAS and HAXPES to extract out information regarding the interfacial quality of the epilayers and spectroscopic signatures associated with exotic phenomena at these dimensions

attract: A Method for Identifying Core Pathways That Define Cellular Phenotypes

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system

Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay

INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive carcinomas, one fibroadenoma and five normal tissues) and three breast cancer cell lines were prospectively analyzed using a microarray (Agilent) and a qRT-PCR assay comprised of 53 genes. Biological subtypes were assigned from the microarray and qRT-PCR data by hierarchical clustering. A proliferation signature was used as a single meta-gene (log(2 )average of 14 genes) to predict outcome within the context of estrogen receptor status and biological 'intrinsic' subtype. RESULTS: We found that the qRT-PCR assay could determine the intrinsic subtype (93% concordance with microarray-based assignments) and that the intrinsic subtypes were predictive of outcome. The proliferation meta-gene provided additional prognostic information for patients with the Luminal subtype (P = 0.0012), and for patients with estrogen receptor-positive tumors (P = 3.4 × 10(-6)). High proliferation in the Luminal subtype conferred a 19-fold relative risk of relapse (confidence interval = 95%) compared with Luminal tumors with low proliferation. CONCLUSION: A real-time qRT-PCR assay can recapitulate microarray classifications of breast cancer and can risk-stratify patients using the intrinsic subtype and proliferation. The proliferation meta-gene offers an objective and quantitative measurement for grade and adds significant prognostic information to the biological subtypes

Lone-pair stabilization in transparent amorphous tin oxides:a potential route to p-type conduction pathways

The electronic and atomic structures of amorphous transparent tin oxides have been investigated by a combination of X-ray spectroscopy and atomistic calculations. Crystalline SnO is a promising p-type transparent oxide semiconductor due to a complex lone-pair hybridization that affords both optical transparency despite a small electronic band gap and spherical s-orbital character at the valence band edge. We find that both of these desirable properties (transparency and s-orbital valence band character) are retained upon amorphization despite the disruption of the layered lone-pair states by structural disorder. We explain the anomalously large band gap widening necessary to maintain transparency in terms of lone-pair stabilization via atomic clustering. Our understanding of this mechanism suggests that continuous hole conduction pathways along extended lone pair clusters should be possible under certain stoichiometries. Moreover, these findings should be applicable to other lone-pair active semiconductors

Why Is There a Lack of Consensus on Molecular Subgroups of Glioblastoma? Understanding the Nature of Biological and Statistical Variability in Glioblastoma Expression Data

Gene expression patterns characterizing clinically-relevant molecular subgroups of glioblastoma are difficult to reproduce. We suspect a combination of biological and analytic factors confounds interpretation of glioblastoma expression data. We seek to clarify the nature and relative contributions of these factors, to focus additional investigations, and to improve the accuracy and consistency of translational glioblastoma analyses.We analyzed gene expression and clinical data for 340 glioblastomas in The Cancer Genome Atlas (TCGA). We developed a logic model to analyze potential sources of biological, technical, and analytic variability and used standard linear classifiers and linear dimensional reduction algorithms to investigate the nature and relative contributions of each factor.Commonly-described sources of classification error, including individual sample characteristics, batch effects, and analytic and technical noise make measurable but proportionally minor contributions to inconsistent molecular classification. Our analysis suggests that three, previously underappreciated factors may account for a larger fraction of classification errors: inherent non-linear/non-orthogonal relationships among the genes used in conjunction with classification algorithms that assume linearity; skewed data distributions assumed to be Gaussian; and biologic variability (noise) among tumors, of which we propose three types.Our analysis of the TCGA data demonstrates a contributory role for technical factors in molecular classification inconsistencies in glioblastoma but also suggests that biological variability, abnormal data distribution, and non-linear relationships among genes may be responsible for a proportionally larger component of classification error. These findings may have important implications for both glioblastoma research and for translational application of other large-volume biological databases