Detection of allelic variations of human gene expression by polymerase colonies

Abstract Background Quantification of variations of human gene expression is complicated by the small differences between different alleles. Recent work has shown that variations do exist in the relative allelic expression levels in certain genes of heterozygous individuals. Herein, we describe the application of an immobilized polymerase chain reaction technique as an alternative approach to measure relative allelic differential expression. Results Herein, we report a novel assay, based on immobilized polymerase colonies, that accurately quantifies the relative expression levels of two alleles in a given sample. Mechanistically, this was accomplished by PCR amplifying a gene in a cDNA library in a thin polyacrylamide gel. By immobilizing the PCR, it is ensured that each transcript gives rise to only a single immobilized PCR colony, or "polony". Once polony amplified, the two alleles of the gene were differentially labeled by performing in situ sequencing with fluorescently labeled nucleotides. For these sets of experiments, silent single nucleotide polymorphisms (SNPs) were used to discriminate the two alleles. Finally, a simple count was then performed on the differentially labeled polonies in order to determine the relative expression levels of the two alleles. To validate this technique, the relative expression levels of PKD2 in a family of heterozygous patients bearing the 4208G/A SNP were examined and compared to the literature. Conclusions We were able to reproduce the results of allelic variation in gene expression using an accurate technology known as polymerase colonies. Therefore, we have demonstrated the utility of this method in human gene expression analysis.</p

Digital Quantitative Measurements of Gene Expression

Abstract: One of the primary goals of functional genomics is to provide a quantitative understanding of gene function. However, the success of this enterprise is dependent on the accuracy and precision of the functional genomic data. A novel approach, digital analysis of gene expression (DAGE) described herein, is an accurate and precise technology for measuring digital gene expression on a relative or absolute scale by simply counting the number of transcripts of a gene being expressed at a given time. The result is a greatly improved technology sensitive enough for identifying and quantifying small (but biologically important and statistically relevant) changes in gene expression. Fourteen genes involved in galactose metabolism in Saccharomyces cerevisiae were analyzed for their expression levels in glucose and galactose minimal media. The quantitative expression results were characterized in terms of distributional and accuracy attributes; they were also in general agreement (in terms of direction of change) with corresponding results obtained using microarray technology. DAGE is likely to have profound implications in the field of functional genomics because the gene expression measurements are digital in nature and therefore more accurate than any othe