An Automatic Microfluidic DNA Microarray Platform Utilizing Graphene Oxide for Single Nucleotide Polymorphism Detection

• Main Authors: Shu-Hong Huang, 黃舒鴻
• Other Authors: Nien-Tsu Huang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/65pw9d

碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 106 === Upon the completion of the Human Genome Project in 2003, scientists discovered that an astonishing 99% of the 3 billion base pairs in humans are the same in all people. This 1% difference between individuals is known as genetic variation, and can be used to explain the differences between each individual’s disease susceptibility and drug response. Remarkably, up to 90% of all genetic variations are caused by single nucleotide polymorphisms (SNPs), which are point mutations occurring in more than 1% of the population. With several individuals having the same SNP, researchers can specifically identify the relationships between the SNPs and the individual’s disease susceptibility and drug response. Since SNPs are the key enabler of personalized medicine, it is important that we have a quick and effective way to identify SNPs. In this thesis, a fully automatic microfluidic DNA microarray platform for detecting SNPs is developed. To minimize the experiment handling process and shorten the hybridization time, an automatic system applying reciprocating flow is designed. To enhance the signal difference between ssDNA and dsDNA, graphene oxide (GO) is integrated to quench the non-specific fluorescence signals. Our study first demonstrated uniform hybridization conditions with simulations and oligonucleotide sequences. Then, an automatic point-mutation detection of clinical sample is completed by our platform in under 3 hours. We believe this platform can potentially be used to detect all types of genetic variations.