Publications by authors named "Yvonne Hey"

Article Synopsis
  • The EpiStem RNA-Amp™ method allows for reliable amplification of cDNA from single cells, enabling detailed RNA profiling in various applications like RNA-Seq and qPCR.
  • Initial tests showed that EpiStem RNA-Amp™ outperformed other commercial alternatives in sensitivity when analyzing cDNA from single cells.
  • The method successfully identified important transcriptional differences in rare cancer initiating cells, demonstrating its potential for uncovering clinically relevant biological signatures.
View Article and Find Full Text PDF
Article Synopsis
  • Strand-specific RNA sequencing in S. pombe revealed a complex program of non-coding RNA (ncRNA) expression at over 600 locations, showing a role in sexual differentiation.
  • Antisense transcripts influenced gene function, disrupting meiotic processes when artificially induced and suggesting that natural antisense production regulates function during differentiation.
  • The study finds that this extensive ncRNA landscape is crucial for controlling sexual differentiation, challenging the notion that such RNAs are mere byproducts of transcription.
View Article and Find Full Text PDF

Background: RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.

View Article and Find Full Text PDF

Microarray gene expression profiling of formalin-fixed paraffin-embedded (FFPE) tissues is a new and evolving technique. This report compares transcript detection rates on Affymetrix U133 Plus 2.0 and Human Exon 1.

View Article and Find Full Text PDF
Article Synopsis
  • Microarrays have evolved over the past decade from a limited technology used in a few research labs to a common method for expression profiling, now capable of assaying complete transcriptomes and single cells.
  • Advances in RNA labeling have made it possible to analyze samples even from formalin-fixed paraffin-embedded archival samples, making it a prime time for microarray core facilities.
  • However, the rise of Next Generation Sequencers, which can offer a more comprehensive view of cellular expression, poses a challenge to microarray dominance, though they are still not widely available enough to meet current demand.
View Article and Find Full Text PDF

Background: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

View Article and Find Full Text PDF

Exon arrays aim to provide comprehensive gene expression data at the level of individual exons, similar to that provided on a per-gene basis by existing expression arrays. This report describes the performance of Affymetrix GeneChip Human Exon 1.0 ST array by using replicated RNA samples from two human cell lines, MCF7 and MCF10A, hybridized both to Exon 1.

View Article and Find Full Text PDF

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways.

View Article and Find Full Text PDF