Molecular signaling in mechanobiology regulation by single-cell analyses using bioinformatics approach

使用生物信息学方法通过单细胞分析进行机械生物学调节中的分子信号传导

• 批准号: 2327144
• 负责人: Yi-Xian Qin
• 金额: $ 89.82万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327144&HistoricalAwards=false
• 关键词: Molecular; signaling; mechanobiology; regulation; single

Homeostasis in living animals and in plants is a self-regulating process by which an organism can maintain internal stability while adjusting to changing external conditions. Numerous molecular mechanisms are involved in these regulatory processes, including cell sensing and communication, intracellular and membrane excitation, and extracellular connections. Mechanobiology enhances cellular sensing genes and channels controlling cell-cell communication, intracellular and membrane excitation, and extracellular connections. The project will develop a single-cell multiplex in situ tagging (scMIST) system combined with advanced machine leaning algorithms through successive rounds of labeling and imaging to effectively achieve a multiplexity of thousands of data points using a common fluorescence microscope and a simple procedure in a typical biological laboratory setting, which has the potential to revolutionize the field of mechanical biology. The project will be committed to outreach and recruitment efforts targeting minority students and those interested in STEM fields.Enhancing cellular viability and motility induced by dynamic physical stimulation is one of the vital components and factors in the biological system’s response to mechano-transduction and regeneration. The project will focus on 1) generating an integrated database of cellular differentiation and adaptation through Ca^(2+) release, Wnt/beta-Catenin signaling, and T-cell immuno-pathway with and without Piezo1 mitigation, and inter- and intra-cellular communication, and cell differentiation; 2) evaluating dynamic loading on single-cell DNA-encoded sequencing by quantifying signaling proteins and surface markers from various cell and proteins; 3) developing machine learning and deep learning algorithms, using R-Studio and bioinformatics platforms; and 4) developing an AI-based framework for bioinformatics analysis to visualize high-dimensional data, classify cell subtypes by both functions and phenotypes, and determine the signaling networks of each subtypes. This study will further enhance our understanding of the impact of the external environment on the living system and its adaptation at the cellular, molecular, and protein levels. The results of the project will be made to public through the lab website, https://you.stonybrook.edu/qinlab/home/.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.

活动物和植物中的体内平衡是一个自我调节的过程，组织可以在适应不断变化的外部条件的同时保持内部稳定性。这些调节过程中涉及许多分子机制，包括细胞感应和通信，细胞内和膜兴奋以及细胞外连接。机械生物学增强了控制细胞 - 细胞内和膜细胞外连接的细胞传感基因和通道。该项目将通过成功的标签和成像回合来开发一个单细胞多路复用原位标记（SCMIST）系统，并结合先进的机器倾斜算法，从而有效地实现了数千个数据点的多重性，并使用典型的生物实验室中的常见荧光显微镜和一个简单的过程实现了革命的机械生物学领域的潜力。该项目将致力于针对少数族裔学生和对STEM领域感兴趣的人进行宣传和招聘工作。增强动态物理刺激引起的细胞活力和运动性是生物系统对机械转导和再生的响应的重要组成部分和因素之一。该项目将集中于1）通过CA^（2+）释放，Wnt/beta-catenin信号传导和T-Cell免疫pathway生成细胞分化和适应的集成数据库，并没有压电1，以及细胞间和细胞内和细胞内通信以及细胞差异； 2）通过量化来自各种细胞和蛋白质的信号蛋白和表面标记来评估单细胞DNA编码测序的动态载荷； 3）使用R-Studio和BioInformitics平台开发机器学习和深度学习算法； 4）开发基于AI的生物信息学分析框架，以可视化高维数据，通过功能和表型对细胞亚型进行分类，并确定每个亚型的信号网络。这项研究将进一步增强我们对外部环境对生活系统的影响及其在细胞，分子和蛋白质水平的适应性的理解。该项目的结果将通过实验室网站https://you.stonybrook.edu/qinlab/home/.this Award公开公开，反映了NSF的法定任务，并且我们是否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我们的支持。