BRITE Relaunch: Using Cell Shape and Cytoskeletal Organization for Understanding and Predicting Cellular Force Generation

BRITE 重新推出：利用细胞形状和细胞骨架组织来理解和预测细胞力的产生

• 批准号: 2227605
• 负责人: Ashok Prasad
• 金额: $ 58万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227605&HistoricalAwards=false
• 关键词: BRITE; Relaunch; Using; Cell; Shape

Cells use pulling, or contractile, forces for many important physiological processes. These contractile forces drive cell division and cell migration. Cells of the immune system, as well as invaders like metastatic cancer cells, use these forces to squeeze through tissue and even deform their own nuclei to slip through tiny spaces. Methods to directly measure these forces have been developed, but they are expensive, challenging, and have limitations. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Relaunch project aims to develop imaging-based methods to estimate cellular forces easier and cheaper than currently possible. The methods promise to make these mechanical forces shaping healthy or diseased tissues more easily measurable through the theoretical and experimental techniques to be employed. Graduate students and undergraduate students will be trained in this research. Additionally, new course material will be developed for traditional engineering courses to enable students to develop an understanding of real-world problems and societal goals. Simulations will also be developed as an aid to learning science for high school, middle school, and college students.This project will uncover the relationship between cellular contractile forces and the organization of the filamentous actin structure and will develop tools for predictive mapping of forces from actin organization. Contractile forces are exerted by the cellular cytoskeleton, and previous work has led to the hypothesis that measuring cytoskeletal organization visible in fluorescence microscopy images may allow for the prediction of these forces. This overarching hypothesis will be tested using computer simulations, experiments, imaging and artificial intelligence methods. The actin cytoskeleton will be simulated using specialized cytoskeletal simulation software. By changing parameters and initial conditions, many types of cytoskeletal structures will be generated, and forces exerted by motor proteins on focal adhesions will be measured. The relation between cytoskeletal organization and force distribution will be investigated using machine learning methods. Cells will be cultured and plated on glass surfaces and imaged using live- or fixed- cell actin and DNA stains. Cytoskeletal structure with and without genetic perturbations will be imaged, and differences in architecture identified and contrasted with simulation predictions. Forces exerted by the cells on focal adhesions will be measured and the cytoskeletal structure in these cells will be simultaneously imaged. Machine learning and high-dimensional data analysis methods will be applied to understand the relation between forces and cytoskeletal organization. Experimental results will provide feedback and improved simulations.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.

细胞利用拉力或收缩力进行许多重要的生理过程。这些收缩力驱动细胞分裂和细胞迁移。免疫系统的细胞，以及像转移性癌细胞这样的入侵者，利用这些力量挤压组织，甚至使它们自己的细胞核变形，以穿过微小的空间。已经开发了直接测量这些力的方法，但是它们昂贵、具有挑战性并且具有局限性。这个促进工程变革和公平进步的研究思路（BRITE）重新启动项目旨在开发基于成像的方法，以比目前更容易和更便宜地估计细胞力。这些方法有望使这些塑造健康或患病组织的机械力更容易通过所采用的理论和实验技术进行测量。研究生和本科生将在这项研究中接受培训。此外，将为传统的工程课程开发新的课程材料，使学生能够了解现实世界的问题和社会目标。模拟也将被开发作为高中，初中和大学学生学习科学的辅助手段。该项目将揭示细胞收缩力和丝状肌动蛋白结构组织之间的关系，并将开发用于预测肌动蛋白组织力的工具。收缩力是由细胞的细胞骨架，和以前的工作已经导致的假设，测量可见的荧光显微镜图像中的细胞骨架组织可能允许预测这些力量。这一总体假设将使用计算机模拟，实验，成像和人工智能方法进行测试。肌动蛋白细胞骨架将使用专门的细胞骨架模拟软件进行模拟。通过改变参数和初始条件，将产生多种类型的细胞骨架结构，并测量马达蛋白对粘着斑施加的力。细胞骨架组织和力分布之间的关系将使用机器学习方法进行研究。将细胞培养并铺在玻璃表面上，并使用活细胞或固定细胞肌动蛋白和DNA染色剂进行成像。细胞骨架结构与不遗传扰动将被成像，并在架构的差异识别和对比模拟预测。将测量细胞对粘着斑施加的力，并同时对这些细胞中的细胞骨架结构进行成像。机器学习和高维数据分析方法将被应用于理解力和细胞骨架组织之间的关系。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。