High-throughput optimization of genetically-encoded fluorescent biosensors

项目摘要/摘要 基因编码的荧光生物传感器使我们能够实时捕捉生化行为的“电影” 在单个细胞内，它们已经被证明是学习新生物学的宝贵工具。最多的 突出的例子是细胞内钙传感器，它显示了完整的神经元的实时活动 但还有许多其他新的工具来测量葡萄糖浓度、蛋白激酶活性、半胱氨酸天冬氨酸氨基转移酶 作用、活性氧物种和核心代谢物，如三磷酸腺苷和NADH。通过病毒载体表达或 转基因，它们可以针对单个细胞或细胞类型，因此它们可以揭示时间依赖关系 在活的混合细胞型组织的背景下，这些细胞中的信号或新陈代谢的变化 所有的哺乳动物组织都由多种类型的细胞组成，它们在信号和新陈代谢中扮演着不同的角色。在……里面 相比之下，生化和质谱学测量具有极高的化学敏感性，但它们通常 包括牺牲准备(使计时器难以学习)，并像同样强大的磁铁一样 共振光谱学/成像技术，它们很少具有单细胞特异性。 但与光谱方法不同的是，生物传感器必须为每个单独的目标量身定做。这 通常涉及半理性蛋白质工程的组合-其中荧光蛋白和 配基结合蛋白以一种特定的方式融合在一起-随后随机或靶向筛选 感应器变异体的突变文库。 这种筛选过程是传感器开发的一个主要限制，也是许多发表的原因 生物传感器没有得到充分的优化--这意味着许多已发表的传感器是“原则上的证据” 不容易使用，或者更糟糕的是，有干扰使他们的名义记者不可靠 目标。优化具有挑战性的一个原因是，传感器的许多特性必须 同时优化：荧光响应的大小，靶标的灵敏度范围， 传感器的专一性(包括来自其他配体的干扰)，以及对环境因素的抵抗力 例如pH值和温度。 因此，我们的目标是开发一种高通量和高含量的基因筛选方法。 编码的荧光生物传感器，特别是那些对配体结合或其他化学物质有反应的生物传感器 刺激物。这种筛查方法使用了一系列成熟的微流控和成像方法，我们 已经试行了其中的大部分。完成后，此筛选方法应可部署在其他 广泛使用的实验室。它将在不到一天的时间内筛选104-105个传感器变种， 有关十几个或更多不同条件下每个传感器变体的信息。我们还将应用此筛查 一系列需要具体优化的已发表和未发表的生物传感器的方法。这个项目 将使研究新生物学的高质量生物传感器的可用性得到显著改善。

PROJECT SUMMARY/ABSTRACT Genetically-encoded fluorescent biosensors allow us to capture real-time “movies” of biochemical behavior inside individual cells, and they have already proven to be valuable tools for learning new biology. The most prominent examples are the intracellular calcium sensors, which reveal real-time neuronal activity in the intact brain, but there are many other new tools for measuring glucose concentration, protein kinase activity, caspase action, reactive oxygen species, and core metabolites such as ATP and NADH. Expressed via viral vectors or transgenes, they can be targeted to individual cells or cell types, and thus they can reveal time-dependent changes in signaling or metabolism in these cells in the context of a living, mixed-cell-type tissue – and virtually all mammalian tissues are composed of multiple cell types with distinct roles in signaling and metabolism. In comparison, biochemical and mass-spec measurements have exquisite chemical sensitivity, but they usually involve sacrificing the preparation (making timecourses hard to learn), and like the also-powerful magnetic resonance spectroscopy/imaging technologies, they rarely have single-cell specificity. But unlike spectroscopic methods, the biosensors must be tailored specifically for each individual target. This generally involves a combination of semi-rational protein engineering – in which a fluorescent protein and a ligand-binding protein are fused together in a specific way – followed by screening of random or targeted mutagenic libraries of sensor variants. This screening process is a major limitation for sensor development, and a reason that many published biosensors are not adequately optimized – meaning that many published sensors are a “proof of principle” that cannot easily be used, or worse yet, have interferences that make them unreliable reporters of their nominal targets. One reason that optimization is challenging is that many characteristics of a sensor must be simultaneously optimized: the size of the fluorescence response, the sensitivity range for the target, the specificity of the sensor (including interference from other ligands), and resistance to environmental factors such as pH and temperature. We therefore aim to develop a high-throughput and high-content screening approach for genetically- encoded fluorescent biosensors, specifically for those that respond to ligand binding or other chemical stimuli. This screening method uses a series of well-established microfluidic and imaging methods, and we have piloted most of these already. When complete, this screening method should be deployable in other laboratories for widespread use. It will enable the screening of 104-105 sensor variants in less than a day, with information about each sensor variant in a dozen or more different conditions. We will also apply this screening approach to a series of published and unpublished biosensors in need of specific optimizations. This project will enable a dramatic improvement in the availability of high quality biosensors to study new biology.