NIRT: Nanofluidic Networks for Single-Molecule Protein Analysis

NIRT：用于单分子蛋白质分析的纳流体网络

• 批准号: 0304318
• 负责人: Don DeVoe
• 金额: $ 75万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0304318&HistoricalAwards=false
• 关键词: NIRT; Nanofluidic; Networks; Single; Molecule

Project Abstract The proposed research aims to develop novel approaches for realizing polymer-based nanofluidic networks, and apply these nanofabrication techniques to ultra-sensitive bioanalytical instrumentation for single-molecule protein separations. Complex biological samples such as tissues, tumors, serum, or cells can contain thousands of proteins ranging from highly abundant proteins which perform common housekeeping tasks, to extremely low abundance proteins which may be present in quantities as small as a single molecule per cell. This NIRT project addresses the need for high-resolution, high-sensitivity separation techniques which can sort ultra-low molarities, down to the individual protein or peptide level, for identification. successful completion of the project will result in new ultra-high-sensitivity screening strategies for applications ranging from fundamental biological studies to disease research and drug discovery. The proposed technology platform, coupling nanochannel-based single molecule detection with integrated sample manipulation, opens the potential for extraordinary reductions in sample consumption for broad-scale protein analysis where the quantity of protein is extremely limited, impacting industry-critical areas such as genomics, proteomics, and drug discovery.

项目摘要 本研究旨在开发实现基于聚合物的纳米流体网络的新方法，并将这些纳米制造技术应用于单分子蛋白质分离的超灵敏生物分析仪器。组织、肿瘤、血清或细胞等复杂的生物样品可能含有数千种蛋白质，从执行常见内务任务的高丰度蛋白质，到每个细胞中存在的量小至单个分子的极低丰度蛋白质。该 NIRT 项目满足了对高分辨率、高灵敏度分离技术的需求，该技术可以对超低摩尔浓度进行分类，直至单个蛋白质或肽水平，以进行识别。该项目的成功完成将带来新的超高灵敏度筛选策略，其应用范围从基础生物学研究到疾病研究和药物发现。所提出的技术平台将基于纳米通道的单分子检测与集成样品操作相结合，为大规模蛋白质分析（蛋白质数量极其有限）的样品消耗量大幅减少提供了可能，从而影响了基因组学、蛋白质组学和药物发现等行业关键领域。