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; 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.