High-throughput optimization of genetically-encoded fluorescent biosensors

基因编码荧光生物传感器的高通量优化

• 批准号: 9362342
• 负责人: GARY I YELLEN
• 金额: $ 29.42万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9362342
• 关键词: Behavior; Binding; Binding Proteins; Biochemical; Biochemical Process; Biological Assay; Biology; Biosensor; Brain; Calcium; Caspase; Cells; Characteristics; Chemicals; Code; Cytolysis; Development; Disease; Environmental Risk Factor; Event; Family; Fluorescence; Gel; Glucose; Green Fluorescent Proteins; Imaging technology; Individual; Laboratories; Learning; Libraries; Ligand Binding; Ligands; Magnetic Resonance Spectroscopy; Measurement; Measures; Metabolism; Methods; Microfluidics; Microscopy; Mitochondria; Mutagenesis; NADH; Neurons; Outcome; Permeability; Plasmids; Preparation; Process; Property; Protein Engineering; Protein Family; Protein Kinase; Proteins; Publications; Publishing; Randomized; Reactive Oxygen Species; Reporter; Reporting; Resistance; Role; Sepharose; Series; Signal Transduction; Specificity; Spheroplasts; Stimulus; Superoxides; Temperature; Testing; Time; Tissues; Transgenes; Variant; Viral Vector; Work; Yeasts; base; brief screening; cell type; chemical reaction; design; imaging modality; improved; movie; novel; prototype; public health relevance; response; screening; sensor; sequence learning; tool; usability; virtual

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.

项目概要/摘要 基因编码荧光生物传感器使我们能够捕捉生化行为的实时“电影” 它们已经被证明是学习新生物学的宝贵工具。 突出的例子是细胞内钙传感器，它揭示了完整神经元的实时活动。 大脑，但还有许多其他新工具可以测量葡萄糖浓度、蛋白激酶活性、半胱天冬酶 作用、活性氧和核心代谢物，例如 ATP 和 NADH 通过病毒载体或表达。 转基因，它们可以靶向单个细胞或细胞类型，因此它们可以揭示时间依赖性 在活的、混合细胞型组织的背景下，这些细胞的信号传导或代谢发生变化——而且实际上 所有哺乳动物组织均由多种细胞类型组成，在信号传导和代谢中具有不同的作用。 相比之下，生化和质谱测量具有精湛的化学灵敏度，但它们通常 涉及牺牲准备（使时间课程难以学习），并且就像同样强大的磁性一样 共振光谱/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，生物传感器必须针对每个目标进行专门定制。 通常涉及半理性蛋白质工程的组合——其中荧光蛋白和 配体结合蛋白以特定方式融合在一起，然后筛选随机或靶向的 传感器变体的诱变文库。 这种筛选过程是传感器开发的主要限制，也是许多人发表论文的原因 生物传感器没有充分优化——这意味着许多已发布的传感器只是“原理证明” 不能轻易使用，或者更糟糕的是，存在干扰，使它们成为不可靠的名义生产者 优化具有挑战性的原因之一是传感器的许多特性必须得到满足。 同时优化：荧光响应的大小、目标的灵敏度范围、 传感器的特异性（包括其他配体的干扰）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、高内涵的遗传筛选方法。 编码荧光生物传感器，特别适用于那些响应配体结合或其他化学物质的生物传感器 这种筛选方法使用了一系列成熟的微流体和成像方法，我们 已经对其中的大部分进行了试点。完成后，这种筛选方法应该可以部署在其他地方。 它将能够在不到一天的时间内筛选出 104-105 个传感器变体， 有关每种传感器变体在十几种或更多不同条件下的信息，我们还将应用此筛选。 该项目需要特定优化的一系列已发表和未发表的生物传感器的方法。 将使研究新生物学的高质量生物传感器的可用性得到显着提高。