使用氧化石墨烯進行單核苷酸多型性檢測之自動化微流道 DNA 微陣列晶片系統

Abstract

在結束全人類基因體定序後，科學家發現人與人之間僅有百分之一的序列是不同的，這意味著每個人之間比我們想像的還要相似。然而這百分之一的不同就是每個人在七十多億人口中仍然是獨特的主因，其中在這百分之一的基因變異中，有高達百分之九十是由單核苷酸多型性 (single nucleotide polymorphism, SNP) 所造成，在經過科學化的分析與統計，已經能瞭解大多數單核苷酸多型性變異點對於基因的影響，因此藉由對單核苷酸多型性的檢測我們可以判斷重大疾病的罹病風險 (如：癌症、心腦血管疾病) 或是臨床用藥指標等資訊。但現今多使用大型自動化DNA陣列檢測平臺，其機台價格高昂且需要一天以上的檢測時間，並不利於基因檢測普及化。 因此本論文想開發一自動化檢測平台並結合DNA微陣列以及氧化石墨烯 (graphene oxide, GO) 以檢測單核苷酸多型性，此平台結合了自動化微流體控制以及往復流動以加快DNA微陣列的反應速率；且為了提升DNA微陣列在單核苷酸多型性的辨別率，我們在DNA微陣列雜交反應後加入了氧化石墨烯。在本論文中我們藉由模擬以及使用合成序列做為檢測樣本證實我們的平台可在短時間(約三小時)完成檢測並成功分辨單核苷酸多型性。接著使用臨床樣本證實此平台與檢測方式可用於臨床分析。我們相信此自動化微流道檢測平台有潛力達成各式基因變異的檢測，以及使基因檢測更加普及化。

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.