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: 10631997
• 负责人: GARY I YELLEN
• 金额: $ 33.9万
• 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/10631997
• 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; Collection; Color; Computing Methodologies; DNA; DNA Sequence; Data; Development; Disease; Dose; Encapsulated; 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; Permeability; Phenotype; Process; Production; Proteins; Publications; Publishing; Reporter; Resistance; Resolution; Second Messenger Systems; Sensitivity and Specificity; Series; Signal Transduction; Site-Directed Mutagenesis; Sorting; Specificity; Technology; Testing; Time; Validation; Variant; acetoacetyl CoA; 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 基因编码的荧光生物传感器是强大的工具，可以跟踪内部的化学事件 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，关于化学过程动力学的直接经验信息是无可替代的 与大多数生化测量不同，生物传感器可以提供空间分辨率 单个细胞或部分细胞的水平以及秒级（或更好）的时间分辨率仍然存在。 对于许多人来说，我们在使用生物传感器跟踪细胞信号或代谢细节的能力方面存在重大差距。 有趣的生化过程，我们没有针对关键代谢物的生物传感器，即使有生物传感器。 存在，它可能不具有观察所需过程所需的正确灵敏度和特异性，或者它可能 对 pH 值或其他可能误导实验者的环境参数敏感。 生物传感器是通过结合荧光蛋白（如水母绿色荧光蛋白，GFP）构建的 但找到结合蛋白质的正确方法具有挑战性， 即使设计合理，获得具有强、特异和鲁棒信号的生物传感器也需要 大量的优化是通过筛选目标随机的传感器变体库来完成的。 当前的方法通常仅限于每天处理数百个变体，通常只处理一对 测量值以指导选择变体以进行进一步验证。 在上一个资助期内，我们开发了一种高通量、高内涵的筛选管道，可以 一天内筛选数千到数万个变体，根据详细的剂量反应选择“获胜者” 我们的方法使用微流体封装每个变体的DNA和蛋白质。 一个小的半透性珠子，然后对一系列下的数千个珠子进行自动显微镜成像 条件（不同的[分析物]、其他测试化合物、pH 等）。该屏幕将允许彻底优化。 传感器，并将使原本失败的传感器项目取得成功。 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 我们还将优化新的总体策略。 用二聚转录因子（用于传感的微生物蛋白大家族）构建传感器， 我们将利用屏幕的高吞吐量与计算方法来改变绑定 现有传感器的位点特异性，以生产重要代谢目标分子的传感器。 与此同时，我们将改进筛选流程以扩大其范围，目标是实现实质性目标： 显着提高效率和吞吐量，并恢复大量表型的基因型信息 这些进步可以极大地促进新型和改进型传感器的开发。 生物传感器以及用于研究和操纵活细胞化学过程的其他工具。