High-throughput synthesis and selection of DNA G-quadruplex optical probes

特定的富含鸟嘌呤的DNA序列可以折叠成被称为G-四链体(G4S)的四链结构，被认为参与了健康细胞的一系列基本生物学功能，并与疾病有关。然而，在活的生物样本中检测它们仍然非常具有挑战性。最近，我们提出了一种新的方法来监测活细胞中G4的形成，基于对小分子光学探针荧光寿命的检测[自然通讯2015，6,8178]。尽管这项概念研究取得了成功，但我们的探针需要进一步修改和优化，以便在与G4 DNA结合时获得高对比度信号。探针结构的多种可能的变化，以及DNA拓扑和浓度的变化，意味着如果使用标准的合成程序和荧光寿命分析来进行新的探针测试，将是既耗时又费力的。在这里，我们提出了一种高通量合成和分析大量噻唑橙(TO)衍生物的策略，TO分子在我们的概念验证研究中显示出作为G4 DNA传感器的重要前景。这个项目将开发一种新的方法，以全自动的方式进行高通量合成和结果统计测试，使我们能够为G4选择理想的光学探针，这些探针在博士的时间框架内无法通过标准路线访问。关键词：DNA光学探针，高通量合成和分析，荧光寿命，分子转子，自动化，数据驱动的优化

Specific guanine-rich sequences of DNA can fold into four-stranded structures known as G-quadruplexes (G4s), which are thought to be involved in a range of essential biological functions in healthy cells and are implicated in diseases. However, their detection in live biological samples remains very challenging. Recently, we have suggested a novel way of monitoring G4 formation in live cells, based on detection of the fluorescence lifetime of small optical probes [Nature Commun 2015, 6, 8178]. Despite this successful proof of concept study, our probes require further modification and optimization, in order to achieve high contrast signal upon binding to G4 DNA. Multiple possible variations in the probe structure, as well as the variations in DNA topology and concentration, mean that testing of new probes will be time consuming and laborious if performed using standard synthetic procedures and fluorescence lifetime analysis. Here we propose a strategy for high throughput synthesis and analysis of a large number of derivatives of thiazole orange (TO), a molecule which showed significant promise as a G4 DNA sensor in our proof of concept studies. This project will develop a novel methodology for high throughput synthesis and statistical testing of results in a fully automated way, allowing us to select ideal optical probes for G4 that are not accessible through standard routes within the timeframe of a PhD.Keywords: DNA optical probes, high throughput synthesis and analysis, fluorescence lifetime, molecular rotors, automation, data-driven optimisation