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.

细胞在许多重要的生理过程中使用拉力或收缩力。这些收缩力推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，使用这些力挤压组织，甚至变形了自己的核，以滑过微小的空间。已经开发了直接测量这些力量的方法，但是它们是昂贵，具有挑战性且有局限性的方法。这一促进了工程（BRITE）重新推出项目的变革性和公平进步的研究思想旨在开发基于成像的方法，以比目前更容易估计细胞力。这些方法有望使这些机械力通过要使用的理论和实验技术更容易地塑造健康或患病的组织。研究生和本科生将接受这项研究的培训。此外，将开发新的课程材料，以使传统的工程课程使学生能够了解现实世界中的问题和社会目标。还将开发模拟，以帮助为高中，中学和大学生学习科学。该项目将揭示蜂窝收缩力与丝状肌动蛋白结构的组织之间的关系，并将开发用于从肌动蛋白组织中预测力量绘制力量的工具。收缩力是由细胞细胞骨架施加的，以前的工作导致了以下假设：测量在荧光显微镜图像中可见的细胞骨架组织可能可以预测这些力。这个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仿真软件进行模拟。通过更改参数和初始条件，将产生许多类型的细胞骨架结构，并测量运动蛋白在焦点粘附上施加的力。细胞骨架组织与力分布之间的关系将使用机器学习方法研究。细胞将在玻璃表面进行培养和镀板，并使用活细胞肌动蛋白和DNA染色成像。将成像有和没有遗传扰动的细胞骨架结构，并与模拟预测识别并形成鲜明对比的结构差异。细胞对局灶性粘连的作用将测量，并同时成像这些细胞中的细胞骨架结构。机器学习和高维数据分析方法将应用于了解力与细胞骨架组织之间的关系。实验结果将提供反馈和改进的模拟。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。