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: 10364295
• 负责人: GARY I YELLEN
• 金额: $ 33.89万
• 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 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10364295
• 关键词: Acetyl Coenzyme A; Address; Adopted; Bathing; Binding Proteins; Binding Sites; Biochemical; Biochemical Process; Biochemistry; Biological Assay; Biosensor; Cells; Cellular Metabolic Process; Chemical Dynamics; Chemicals; Coenzyme A; Collection; Color; Computing Methodologies; DNA; DNA Sequence; Data; Development; Disease; Dose; Enzymatic Biochemistry; Event; Exposure to; Family; Fluorescence; Gel; Generations; Genetic; Genotype; Glucose; Goals; Grant; Green Fluorescent Proteins; Homodimerization; Image; Individual; Jellyfish; Libraries; Malonyl Coenzyme A; Mass Spectrum Analysis; Measurement; Measures; Metabolic; Methods; Microfluidics; Microscope; Phenotype; Process; Production; Proteins; Publications; Publishing; Reporter; Resistance; Resolution; Second Messenger Systems; Sensitivity and Specificity; Series; Signal Transduction; Site-Directed Mutagenesis; Sorting - Cell Movement; Specificity; Technology; Testing; Time; Validation; Variant; acetoacetyl CoA; base; cell behavior; chemical reaction; design; dimer; glucose sensor; high throughput screening; improved; insight; interest; machine learning method; microbial; microscopic imaging; movie; next generation sequencing; novel; prototype; public health relevance; response; scaffold; screening; sensor; success; temporal measurement; tool; transcription factor

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.

抽象的 遗传编码的荧光生物传感器是强大的工具，可以跟踪化学事件 活细胞，实时。即使对生物化学，酶学，调节信号传导有详细的了解， 和遗传学，无需替代有关化学过程动力学的直接经验信息 和细胞中的信号传导。与大多数生化测量不同，生物传感器可以提供空间分辨率 单细胞或细胞部分的水平，以及秒的暂时分辨率（或更好）。然而，有 我们使用生物传感器遵循细胞信号传导或代谢细节的能力的主要差距。对于许多人 有趣的生化过程，我们没有关键代谢物的生物传感器。即使是生物传感器 存在，它可能没有观察所需过程所需的正确敏感性和特异性，或者可以 对pH或其他可能会误导专家的环境参数具有敏感性。 生物传感器是通过结合荧光蛋白（例如水母绿色荧光蛋白，GFP）来构建的。 与感兴趣的化学物质的结合蛋白。但是找到合并蛋白质的正确方法是具有挑战性的， 即使采用良好的设计，获得具有强，特定和健壮信号的生物传感器也需要一个 大量优化。通过筛选有针对性的传感器变体的随机库来完成此优化。 当前方法通常仅限于每天处理数百种变体，通常只有一对 测量以指导选择变体以进行进一步验证。 在上一个赠款期间，我们开发了一条高通量，高核心筛选管道，可以 一天中屏幕成千上万到成千上万的变体，根据详细的剂量反应选择“获奖者” 和选择性数据。我们的方法使用每种变体的DNA和蛋白质的微流体封装 一个小的，可半透明的珠子，然后是成千上万个珠子的自动显微镜成像 条件（变化[分析]，其他测试化合物，pH等）。该屏幕将允许彻底优化 传感器，将允许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某些现有传感器（例如葡萄糖和ATP：ADP 比率）和传感器原型（例如，裂缝和丙二酰辅酶A）。我们还将优化一种新的一般策略 从二聚体转录因子（一个有用的微生物蛋白家族）中构造传感器， 我们将与计算方法一起探索屏幕的高吞吐量，以更改绑定 现有传感器的位点特异性以生成重要代谢靶标分子的传感器。 同时，我们将在筛选管道方面进行改进，以扩大其覆盖范围，其目标的目标 从而提高效率和吞吐量，并恢复有关大量现象的基因型信息 打字传感器变体。这些进步可以极大地改善新颖和改进的发展 生物传感器以及其他用于研究和操纵活细胞化学过程的工具。