High-throughput optimization of genetically-encoded fluorescent biosensors

摘要 遗传编码荧光生物传感器是一种强大的工具，可以跟踪内部的化学事件 真实的活细胞。即使对生物化学，酶学，调节信号有了详细的了解， 和遗传学，没有任何东西可以替代关于化学过程动力学的直接经验信息 和细胞中的信号。与大多数生化测量不同，生物传感器可以提供空间分辨率， 单个细胞或部分细胞的水平，以及秒（或更好）的时间分辨率。然而， 我们使用生物传感器跟踪细胞信号或代谢细节的能力存在重大差距。对于许多 有趣的生化过程，我们没有关键代谢物的生物传感器。即使生物传感器 存在，它可能不具有观察所需过程所需的正确灵敏度和特异性，或者它可能 对pH值或其他可能误导实验者的环境参数敏感。 生物传感器是通过结合荧光蛋白（如水母绿色荧光蛋白，GFP） 与目标化学物质的结合蛋白结合但是找到正确的方法来联合收割机的蛋白质是具有挑战性的， 即使有合理的设计，获得具有强，特异性和鲁棒信号的生物传感器也需要 大量的优化。这种优化是通过筛选传感器变体的靶向随机文库来完成的。 目前的方法通常仅限于每天处理数百个变体，通常只有一对 以指导选择变体进行进一步验证。 在上一个资助期，我们开发了一个高通量、高内容的筛选管道， 在一天内筛选数千到数万种变异，根据详细的剂量反应选择“优胜者” 和选择性数据。我们的方法使用微流体封装的DNA和蛋白质的每一个变种， 一个小的，半渗透性的珠子，然后在一系列的自动显微镜下对数千个珠子进行成像。 条件（变化[分析物]、其他供试化合物、pH值等）。该屏幕将允许彻底优化 传感器，并将允许成功，否则失败的传感器项目。 我们建议使用新的筛选方法来优化一些现有的传感器（例如，葡萄糖和ATP：ADP 比率）和传感器原型（例如，乳酸盐和丙二酰辅酶A）。我们还将优化新的总体战略， 从二聚体转录因子（用于传感的微生物蛋白质的大家族）构建传感器， 我们将利用筛选的高通量与计算方法来改变结合 现有传感器的位点特异性以产生用于重要代谢靶分子的传感器。 与此同时，我们会改善筛选渠道，以扩大其覆盖范围，目标是实现以下目标： 从而提高效率和通量，并恢复大量表型的基因型信息， 类型的传感器变体。这些进步可以大大提高新的和改进的发展， 生物传感器，以及用于研究和操纵活细胞中的化学过程的其他工具。

ABSTRACT Genetically encoded fluorescent biosensors are powerful tools that allow the tracking of chemical events inside living cells, in real time. Even with a detailed understanding of biochemistry, enzymology, regulatory signaling, and genetics, there is no substitute for direct empirical information about the dynamics of chemical processes and signaling in cells. Unlike most biochemical measurements, the biosensors can provide spatial resolution at the level of single cells or parts of cells, and temporal resolution of seconds (or better). Nevertheless, there are major gaps in our ability to follow the details of cell signaling or metabolism using biosensors. For many interesting biochemical processes, we have no biosensors for the key metabolites. And even when a biosensor exists, it may not have the right sensitivity and specificity required for observing the desired process, or it may have sensitivity to pH or other environmental parameters that can mislead the experimenters. Biosensors are constructed by combining a fluorescent protein (like the jellyfish green fluorescent protein, GFP) with a binding protein for the chemical of interest. But finding the right way to combine the proteins is challenging, and even with a well-reasoned design, getting a biosensor with a strong, specific, and robust signal requires a large amount of optimization. This optimization is done by screening targeted random libraries of sensor variants. Current methods are typically limited to processing hundreds of variants per day, usually with just a single pair of measurements to guide selection of a variant for further validation. In the previous grant period, we developed a high-throughput, high-content screening pipeline that can screen thousands to tens of thousands of variants in a day, selecting “winners” based on detailed dose-response and selectivity data. Our approach uses microfluidic encapsulation of both DNA and protein for each variant in a small, semipermeable bead, followed by automated microscope imaging of thousands of beads under a series of conditions (varying [analyte], other test compounds, pH, etc.). This screen will permit thorough optimization of sensors and will allow success in otherwise failed sensor projects. We propose to use the new screening method to optimize some existing sensors (e.g., glucose and ATP:ADP ratio) and sensor prototypes (e.g., lactate and malonyl-CoA). We will also optimize a new general strategy for constructing sensors from dimeric transcription factors (a large family of microbial proteins useful for sensing), and we will exploit the high throughput of the screen in concert with computational methods to change the binding site specificity of existing sensors to produce sensors for important metabolic target molecules. In parallel, we will make improvements in the screening pipeline to expand its reach, with the goals of substan- tially increasing efficiency and throughput, and of recovering genotype information on a large number of pheno- typed sensor variants. These advances can dramatically improve the development of novel and improved biosensors, as well as other tools for the study and manipulation of chemical processes in living cells.