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: 9751930
• 负责人: GARY I YELLEN
• 金额: $ 29.41万
• 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/9751930
• 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; Exposure to; 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.

项目摘要/摘要 遗传编码的荧光生物源使我们能够捕获生化行为的实时“电影” 在单个细胞内部，它们已经被证明是学习新生物学的宝贵工具。最多 突出的例子是细胞内钙传感器，该传感器揭示了完整的实时神经元活性 大脑，但是还有许多其他用于测量葡萄糖浓度，蛋白激酶活性的新工具 作用，活性氧和核心代谢物，例如ATP和NADH。通过病毒向量或 转基因，它们可以针对单个细胞或细胞类型，因此可以揭示时间依赖性 在这些细胞中的信号传导或代谢的变化，在生存的混合细胞类型组织的背景下 - 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作用。在 比较，生化和质谱测量值具有独特的化学敏感性，但通常 涉及牺牲准备工作（使时间很难学习），并且也像功能强大的磁性一样 共振光谱/成像技术很少具有单细胞特异性。 但是与光谱法不同，必须专门针对每个目标定制生物传感器。这 通常涉及半理性蛋白工程的组合 - 其中荧光蛋白和A 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 传感器变体的诱变库。 这种筛选过程是传感器开发的主要限制，也是许多人发表的原因 生物传感器没有充分优化 - 这意味着许多已发表的传感器是一种“原则证明” 无法轻易使用，或更糟糕的是，进行访谈，使他们的名义记者不可靠 目标。优化具有挑战性的原因之一是传感器的许多特征必须是 类似地优化：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）和对环境因素的抗性 例如pH和温度。 因此，我们旨在为遗传学开发高通量和高含量筛选方法 编码的荧光生物传感器，特别是针对响应配体结合或其他化学物质的荧光生物传感器 刺激。这种筛选方法使用一系列建立的微流体和成像方法，我们 已经驾驶了大多数。完成后，该筛选方法应在其他 用于宽度的实验室。它将在不到一天的时间内筛选104-105传感器变体 在十几个或更多不同条件下的每个传感器变体的信息。我们还将应用此筛选 采用一系列已发表和未发表的生物传感器，需要特定的优化。这个项目 将在研究新生物学的高质量生物传感器的可用性方面做出巨大改进。