Steve Gullans | Search PubMed

PubMed2006

A single-molecule method for the quantitation of microRNA gene expression.

MicroRNAs (miRNA) are short endogenous noncoding RNA molecules that regulate fundamental cellular processes such as cell differentiation, cell proliferation and apoptosis through modulation of gene expression. Critical to understanding the role of miRNAs in this regulation is a method to rapidly and accurately quantitate miRNA gene expression. Existing methods lack sensitivity, specificity and typically require upfront enrichment, ligation and/or amplification steps. The Direct miRNA assay hybridizes two spectrally distinguishable fluorescent locked nucleic acid (LNA)-DNA oligonucleotide probes to the miRNA of interest, and then tagged molecules are directly counted on a single-molecule detection instrument. In this study, we show the assay is sensitive to femtomolar concentrations of miRNA (500 fM), has a three-log linear dynamic range and is capable of distinguishing among miRNA family members. Using this technology, we quantified expression of 45 human miRNAs within 16 different tissues, yielding a quantitative differential expression profile that correlates and expands upon published results.

DNA Probes↗

PubMed2002

Interactive exploration of microarray gene expression patterns in a reduced dimensional space.

The very high dimensional space of gene expression measurements obtained by DNA microarrays impedes the detection of underlying patterns in gene expression data and the identification of discriminatory genes. In this paper we show the use of projection methods such as principal components analysis (PCA) to obtain a direct link between patterns in the genes and patterns in samples. This feature is useful in the initial interactive pattern exploration of gene expression data and data-driven learning of the nature and types of samples. Using oligonucleotide microarray measurements of 40 samples from different normal human tissues, we show that distinct patterns are obtained when the genes are projected on a two-dimensional plane spanned by the loadings of the two major principal components. These patterns define the particular genes associated with a sample class (i.e., tissue). When used separately from the other genes, these class-specific (i.e., tissue-specific) genes in turn define distinct tissue patterns in the projection space spanned by the scores of the two major principal components. In this study, PCA projection facilitated discriminatory gene selection for different tissues and identified tissue-specific gene expression signatures for liver, skeletal muscle, and brain samples. Furthermore, it allowed the classification of nine new samples belonging to these three types using the linear combination of the expression levels of the tissue-specific genes determined from the first set of samples. The application of the technique to other published data sets is also discussed.

Gene Expression Profiling↗