Novel multiplex and quantitative genetic analysis method by CE-based SSCP

Recent development of molecular biological tools has created several research fields such as systems biology, omics studies, and molecular diagnostics. Among them, molecular diagnostics is a new area of clinical diagnosis, and it includes all tests and methods to identify a disease or the predisposition for a disease analyzing DNA, RNA or proteins of an organism. After completion of the human genome project, enormous reports on genetic variations were reported which cause phenotypic variations and disease conditions. DNA microarray, which can detect thousands of DNA sequences simultaneously, was the major technology to discover a novel DNA sequence area which could differentiate between one condition and another; the DNA sequence is generally called genetic marker. After the genetic marker discovery, validation by quantitative method such as real-time qPCR is often a required step because of error-prone nature of DNA microarray. For molecular diagnosis using discovered genetic markers, however, both DNA microarray and real-time qPCR are not appropriate because DNA microarray is error-prone by nature and real-time qPCR cannot detect multiple targets by limitation on fluorescence dye. Capillary electrophoresis-based single-strand DNA conformation polymorphism analysis (CE-SSCP), which separates DNA molecules by conformational difference of ssDNA and quantifies them by fluorescence intensity, can detect multiple genetic markers quantitatively. We have demonstrated CE-SSCP could be used to detect multiple pathogen targets quantitatively by its genomic regions of 16S rRNA gene or 16S rRNA itself. Moreover, multiplex mRNA quantification was also demonstrated for seven mRNAs involved in the central carbon metabolic pathway, and this result showed that expression of marker genes can be monitored by this method. In addition, we also did some modification on conventional CE settings because it was not optimized for SSCP analysis. Conventional polymer matrix, which is the major component of CE separation, was replaced by the new polymer matrix that we developed, and it showed better resolution than the conventional one. Additionally, more examples will be shown for the potentials in the various applications such as copy number variations, single nucleotide polymorphism, and expression analysis.