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

乔治华盛顿大学被授予创造一种新的光学显微镜平台，以超精细(纳米级)的定位精度在三维(3D)上可视化和检测活细胞上的细胞膜特征。该项目还包括开发一种机器学习算法，作为从传统的2D光学显微镜数据预测实时3D变化的低成本替代方案。这项创新研究将使细胞生物学家和生物化学家能够在光学、脂质生物物理学和生物力学等学科中获得新的培训。该研究项目还将教育从高中到研究生的不同背景的学生，包括女性和代表性不足的少数族裔。研究成果将通过创建公共数据库、同行评议出版物和会议报告来传播。外展活动将包括通过全国SPARC计划接待高中暑期实习生和代表不足的少数族裔学生。该项目的其他社会效益是可能将拟议仪器的使用扩展到更多的生物研究领域。该项目将开发新的工具来观察和评估细胞膜中发生的变化，这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在活细胞中检测到纳米级的3D变化。然而，三维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，该系统结合了两项现有技术，能够对活细胞的细胞膜形态进行超精细的3D检测。该平台将在各种类型的电池上进行测试和验证，以确保可靠性和广泛的适用性。新仪器将得到一个基于机器学习的软件工具的补充，该工具根据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，使其在许多情况下都是负担得起的，以及进一步扩展新开发的硬件的能力。这些工具一起对推进基础生物学研究具有广泛的影响，通过促进解决细胞表面形态对细胞生物功能的影响的广泛研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.