IIBR Instrumentation: Integrated optical imaging and machine-learning platforms for mapping 3D topology of nanoscale cellular structures

IIBR Instrumentation：集成光学成像和机器学习平台，用于绘制纳米级细胞结构的 3D 拓扑

• 批准号: 1945373
• 负责人: Inhee Chung
• 金额: $ 79.27万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945373&HistoricalAwards=false
• 关键词: IIBR; Instrumentation; Integrated; optical; imaging

An award is made to the George Washington University to create a novel light microscopy platform to visualize and detect features of cell membranes in three dimensions (3D) at ultra-fine (nanometer-scale) localization accuracy and on living cells. The project also includes the development of a machine-learning algorithm as a low-cost alternative to predict real-time 3D changes from conventional 2D light microscopy data. This innovative research will enable cell biologists and biochemists to gain new training in disciplines including optics, lipid biophysics, and biomechanics. The research program will also educate students ranging from high school to graduate school with diverse backgrounds, including women and underrepresented minorities. Research outcomes will be disseminated via creation of a public database, peer-reviewed publications, and conference presentations. Outreach activities will include hosting high school summer interns and underrepresented minority students through the national SPARC program. Other societal benefits of this project are the potential extension of the use of the proposed instruments to additional biological research areas. This project will develop new tools to view and assess changes occurring in cell membranes, which may play key roles in important cellular processes. Current light microscopic technology cannot detect changes at a nanometer scale in 3D in living cells. However, the 3D nanometer-scale membrane morphologies and their changes are important features that control cell biology. To resolve these issues, this project will build an innovative light microscopy system that combines two existing technologies to enable ultra-fine, 3D detection of cell membrane morphology on living cells. The platform will be tested and validated on various cell types to ensure reliability and wide applicability. The new instrument will be complemented by a machine-learning based software tool that predicts 3D cellular features based on 2D data obtained by using conventional light microscopes. The software will both allow for broader use in research with conventional light microscopy, making it affordable for many settings, as well as further extend the capabilities of the newly developed hardware. These tools together have broad implications for advancing basic biology research, by facilitating a wide variety of studies addressing the effects of cell surface morphology on cell biological functions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

授予乔治华盛顿大学创建一个新的光学显微镜平台，以超细（纳米级）定位精度和活细胞在三维（3D）中可视化和检测细胞膜的特征。该项目还包括开发一种机器学习算法，作为一种低成本的替代方案，用于从传统的2D光学显微镜数据中预测实时3D变化。这项创新的研究将使细胞生物学家和生物化学家获得新的学科培训，包括光学，脂质生物物理学和生物力学。该研究计划还将教育从高中到研究生院的学生，他们具有不同的背景，包括妇女和代表性不足的少数民族。研究成果将通过建立公共数据库、同行评审出版物和会议演讲等方式传播。外联活动将包括通过国家奖学金计划接待高中暑期实习生和代表性不足的少数民族学生。该项目的其他社会效益是拟议仪器的使用可能扩展到其他生物研究领域。该项目将开发新的工具来观察和评估细胞膜发生的变化，这些变化可能在重要的细胞过程中发挥关键作用。目前的光学显微镜技术无法检测活细胞中纳米级的3D变化。然而，3D纳米尺度的膜形态及其变化是控制细胞生物学的重要特征。为了解决这些问题，该项目将建立一个创新的光学显微镜系统，结合两种现有技术，实现活细胞上细胞膜形态的超细3D检测。该平台将在各种细胞类型上进行测试和验证，以确保可靠性和广泛的适用性。新仪器将由基于机器学习的软件工具补充，该软件工具基于使用传统光学显微镜获得的2D数据预测3D细胞特征。该软件将允许在传统光学显微镜的研究中更广泛地使用，使其适用于许多设置，并进一步扩展新开发的硬件的功能。这些工具共同促进了各种各样的研究，解决细胞表面形态对细胞生物学功能的影响，对推进基础生物学研究具有广泛的意义。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。