High-throughput optimization of genetically-encoded fluorescent biosensors

摘要 基因编码的荧光生物传感器是一种强大的工具，可以跟踪体内的化学事件 活细胞，实时的。即使对生物化学、酶学、调控信号有详细的了解， 和遗传学，关于化学过程动力学的直接经验信息是不可替代的 以及细胞内的信号。与大多数生化测量不同的是，生物传感器可以在 单个细胞或部分细胞的水平，以及秒(或更好)的时间分辨率。尽管如此，还有 我们使用生物传感器跟踪细胞信号或新陈代谢细节的能力存在重大差距。对许多人来说 有趣的生化过程，我们没有关键代谢物的生物传感器。即使当一个生物传感器 存在，它可能不具有观察所需过程所需的正确的敏感性和特异性，或者它可能 对可能误导实验者的pH值或其他环境参数敏感。 生物传感器是通过结合荧光蛋白(如水母绿色荧光蛋白，GFP)而构建的。 与感兴趣的化学物质的结合蛋白。但找到正确的结合蛋白质的方法是具有挑战性的， 即使有一个合理的设计，要想得到一个具有强烈、特定和强大信号的生物传感器，也需要 大量的优化。这种优化是通过筛选传感器变体的目标随机文库来完成的。 目前的方法通常局限于每天处理数百种变体，通常只使用一对 用于指导选择变种进行进一步验证的测量。 在之前的资助期间，我们开发了一种高通量、高内容的筛选管道，可以 在一天内筛选数千到数万种变种，根据详细的剂量反应来选择“赢家” 和选择性数据。我们的方法使用微流控技术对DNA和蛋白质进行微流控封装 一种小的、半渗透的珠子，然后是自动显微镜成像，在一系列下面的数千个珠子 条件(变化的[分析物]、其他测试化合物、pH等)。此屏幕将允许进行彻底的优化 并将在其他失败的传感器项目中取得成功。 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 比例)和传感器原型(例如，乳酸和丙二酰辅酶A)。我们还将优化新的总体战略，以 用二聚体转录因子(一大类可用于传感的微生物蛋白质)构建传感器， 我们将利用屏幕的高吞吐量与计算方法相结合来更改绑定 现有传感器的位置特异性，为重要的代谢靶分子生产传感器。 同时，我们将改进筛查渠道，以扩大其覆盖范围，目标是-- 提高了效率和吞吐量，并恢复了大量表型的基因信息。 类型的传感器变种。这些进步可以极大地促进小说的发展和改进 生物传感器，以及用于研究和操纵活细胞中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.