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

在活的动物和植物中，动植物的动态平衡是一个自我调节的过程，通过这个过程，有机体可以在适应不断变化的外部条件的同时保持内部稳定。这些调控过程涉及许多分子机制，包括细胞感知和通讯、细胞内和膜兴奋以及细胞外连接。机制生物学增强了控制细胞间通讯、细胞内和膜兴奋以及细胞外连接的细胞传感基因和通道。该项目将开发一个单细胞多路原位标记(ScMIST)系统，通过连续几轮的标记和成像，结合先进的机器学习算法，在典型的生物实验室环境中使用普通荧光显微镜和简单的程序有效地实现数千个数据点的多路传输，这有可能给机械生物学领域带来革命性的变化。该项目将致力于面向少数族裔学生和对STEM领域感兴趣的人进行外联和招募工作。增强动态物理刺激诱导的细胞活性和运动性是生物系统对机械转导和再生做出反应的重要组成部分和因素之一。该项目将侧重于1)通过Ca^(2+)释放、Wnt/β-连环蛋白信号和T细胞免疫途径以及Piezo1缓解和不缓解、细胞间和细胞内交流以及细胞分化来建立细胞分化和适应的综合数据库；2)通过量化来自不同细胞和蛋白质的信号蛋白质和表面标记来评估单细胞DNA编码序列上的动态负载；3)利用R-Studio和生物信息学平台开发机器学习和深度学习算法；4)开发了一个基于人工智能的生物信息学分析框架，以可视化高维数据，按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。这项研究将进一步加深我们对外部环境对生命系统的影响及其在细胞、分子和蛋白质水平上的适应的理解。该项目的结果将通过实验室网站向公众公布，https://you.stonybrook.edu/qinlab/home/.This奖反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.