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

活体动物和植物的内稳态是一个自我调节的过程，通过这个过程，生物体可以在保持内部稳定的同时适应不断变化的外部条件。许多分子机制参与了这些调节过程，包括细胞感知和通讯，细胞内和膜兴奋，以及细胞外连接。机械生物学增强细胞传感基因和通道控制细胞间通讯，细胞内和膜的兴奋，以及细胞外的连接。该项目将开发一个单细胞多重原位标记（scMIST）系统，结合先进的机器学习算法，通过连续的标记和成像，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重性，这有可能彻底改变机械生物学领域。该项目将致力于针对少数民族学生和对STEM领域感兴趣的学生的推广和招聘工作。通过动态物理刺激增强细胞活力和运动能力是生物系统响应机械转导和再生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 - catenin信号和t细胞免疫途径（有或没有Piezo1缓解）、细胞间和细胞内通信以及细胞分化，生成细胞分化和适应的综合数据库；2)通过定量各种细胞和蛋白质的信号蛋白和表面标记物来评估单细胞dna编码测序的动态负载；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)开发基于人工智能的生物信息学分析框架，以可视化高维数据，根据功能和表型对细胞亚型进行分类，并确定每种亚型的信号网络。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。该项目的结果将在实验室网站https://you.stonybrook.edu/qinlab/home/.This上公布，该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，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.