RII Track-4: NSF: Developing 3D Models of Live-Endothelial Cell Dynamics with Application Appropriate Validation

RII Track-4：NSF：开发活内皮细胞动力学的 3D 模型并进行适当的应用验证

• 批准号: 2327466
• 负责人: David Long
• 金额: $ 23.1万
• 依托单位: Wichita State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327466&HistoricalAwards=false
• 关键词: RII; Track; NSF; Developing; 3D

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows project will provide a fellowship to an Assistant Professor and training for a graduate student at Wichita State University. Understanding how human life functions, by identifying and predicting observable characteristics of a cell or group of cells, is a grand challenge. A group of cells integral to how life functions are endothelial cells (ECs) lining blood vessels. They form an extensive cellular network that senses and changes based on stimuli. The PI will conduct research at the Allen Institute for Cell Science (AICS) in Seattle, WA. The PI and AICS collaborators will develop 3D models of live-EC dynamics from limited information, with application appropriate validation. This project’s value lies in its ability to connect live-cell behavior and subcellular shape, which would reveal insights into human health and disease. This project will help train a PhD student in a multidisciplinary environment to solve a complex STEM problem, and the research will be used to develop new course material for two courses the PI teaches at WSU. In addition, the PI will present to the Wichita Public Schools (WPS). The WPS represents a rich opportunity to encourage underrepresented groups to remain in STEM and make a positive impact on the local Wichita community. Finally, the work, training, and the research infrastructure enhancement, coupled with the cross-fertilization of ideas will empower the PI to sustain this project past the funding period. Computational models are impactful when they are coupled to experiments. However, there is often a disconnect. In particular, the cell morphology does not match the conditions of the experiments or is not measured. Morphology and subcellular organization are important determinants of endothelial function. The fellowship will support the PI’s activities at the Allen Institute for Cell Science (AICS) and the home institution to developed and validate machine learning algorithms to predict subcellular morphology of live cells from only one label that is easily imaged on live cells and develop spatial statistical models to validate morphology predictions with application appropriate validation. Primary human dermal microvascular endothelial cells will be exposed to steady unidirectional fluid shear stress. While exposed to fluid shear stress live-cell morphology will be determined by confocal microscopy. These images will serve as a reference data set for machine learning. The second objective is to use machine learning to predict 3D subcellular morphology from only images of fluorescently labeled cell membranes. An additional objective is to develop spatial statistical models to validate the 3D subcellular morphology predicted from machine learning. This approach will allow us to explore the connection between organization, cell shape, function, and mechanics for sheared endothelial cells.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.

该研究基础设施改进 Track-4 EPSCoR 研究员项目将为威奇托州立大学的助理教授提供奖学金，并为研究生提供培训，通过识别和预测细胞或细胞群的可观察特征来了解人类生命的运作方式，血管内皮细胞 (EC) 是生命功能不可或缺的一部分，它们形成了一个广泛的细胞网络，可以根据刺激进行感知和变化。 （AICS）位于华盛顿州西雅图。PI 和 AICS 合作者将利用有限的信息开发活 EC 动力学的 3D 模型，并进行适当的应用验证。该项目的价值在于其将活细胞行为和亚细胞形状联系起来的能力。该项目将帮助培养一名博士生在多学科环境中解决复杂的 STEM 问题，该研究将用于为 PI 在 WSU 教授的两门课程开发新的课程材料。 PI 将介绍威奇托公立学校 (WPS) 提供了一个丰富的机会，可以鼓励代表性不足的群体留在 STEM 领域，并对当地威奇托社区产生积极影响。想法的交叉融合将使 PI 能够在资助期结束后维持该项目。当计算模型与实验相结合时，它们会产生影响，尤其是细胞形态与实验条件不匹配。实验或形态和亚细胞组织是内皮功能的重要决定因素。该奖学金将支持 PI 在艾伦细胞科学研究所 (AICS) 及其所在机构开发和验证机器学习算法以预测活细胞的亚细胞形态。仅使用一种易于在活细胞上成像的标签，并开发空间统计模型，通过应用适当的验证来验证形态预测。原代人真皮微血管内皮细胞在暴露于流体时将受到稳定的单向流体剪切应力。剪切应力活细胞形态将通过共聚焦显微镜确定，这些图像将作为机器学习的参考数据集，第二个目标是利用机器学习仅根据荧光标记的细胞膜图像来预测 3D 亚细胞形态。目标是开发空间统计模型来验证机器学习预测的 3D 亚细胞形态，这种方法将使我们能够探索剪切内皮细胞的组织、细胞形状、功能和力学之间的联系。该奖项反映了 NSF 的奖项。法定使命，并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，被认为值得支持。