RECODE: Real-time Monitoring, Modeling and Manipulation of Progenitor Co-differentiation in Heterotypic Cell Systems

从干细胞开发组织移植物和模型是一个复杂的过程。模仿自然组织的结构和功能需要协调来自多种细胞类型的信号。这些信号如何相互作用以指导复杂组织的发育尚不清楚，也缺乏有效的预测工具。该项目的目标是监测、模拟和调节干细胞的分化以产生组织。该项目将提供一幅更清晰的动态组织发展图景，以及预测和操纵干细胞命运的新工具。这将导致一种精确和可重复的组织工程战略，导致改进的组织工程疗法和更好的药物开发模式。这个项目将包括本科生的研究经验，顶峰设计组件，学生暑期实习计划，以及研究生行业沉浸计划。这个Recode项目将应用随机系统逆向工程(SRES)方法来监测、建模和调节3D有机物中前体细胞的异型共分化。SRES将推进复杂组织中细胞共分化的科学基础。该项目的关键是开发纳米级生物传感器，在复杂的异型有机物中提供单细胞的动态多基因表达分析。将监测时空分化标记的表达，并结合分类种群中的增殖措施。这些数据将被用来开发间充质干细胞(MSC)功能和分化的随机代理模型。这些模型将根据多路刺激预测成骨、成内皮样、成软骨和成脂谱系的相对比例，并可使用实时生物传感器数据进行更新。预测模型将通过使用时间控制的siRNA分化因子调节异型分化轨迹来测试，以创建异型骨和软骨有机体。这一新知识将为实现复杂的骨骼结构所需的异型3D组织模型的工程设计提供系统的手段。该项目的技术目标是：(I)为分化标记开发新的多重实时细胞内报告程序，(Ii)基于动态分化标记表达开发祖细胞增殖和命运的随机模型，以及(Iii)使用已知的从头调控分化的siRNA靶向途径验证基于代理的有机体发育的随机模型。该项目由ENG/CBET中的工程生物学和健康集群和BIO/MCB中的细胞动力学和功能集群共同支持。该奖项反映了NSF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。

Developing tissue grafts and models from stem cells is a complex process. Mimicking the structure and function of natural tissue requires coordination of signals from many cell types. How these signals interact to direct development of complex tissues is not well understood, and effective predictive tools are lacking. The goal of this project is to monitor, model, and modulate the differentiation of stem cells to produce tissues. This project will provide a clearer picture of dynamic tissue development and new tools to predict and manipulate stem cell fate. This will lead to a strategy for engineering of accurate and reproducible tissues, leading to improved tissue engineered therapies and better models for drug development. This project will involve undergraduate research experiences, a capstone design component, a student summer internship program, and an industry immersion program for graduate students. This RECODE project will apply a systems reverse engineering of stochasticity (SRES) approach to monitor, model, and modulate heterotypic co-differentiation of progenitor cells in 3D organoids. SRES will advance the science which underlies the co-differentiation of cells in complex tissues. Crucial to this project is the development of nanoscale biosensors providing dynamic multigene expression analysis in single cells within complex heterotypic organoids. Spatiotemporal differentiation marker expression, combined with proliferation measures in sorted populations will be monitored. The data will be used to develop stochastic agent-based models of mesenchymal stem cell (MSC) function and differentiation. The models will predict the relative proportions of osteogenic, endotheliogenic, chondrogenic and adipogenic lineages based on multiplexed stimuli, and be updatable using real-time biosensor data. The predictive models will be tested by modulating heterotypic differentiation trajectories with temporally controlled siRNA differentiation factors to create heterotypic bone and cartilage organoids. This new knowledge will provide a systematic means for engineering heterotypic 3D tissue models necessary for realizing intricate skeletal architectures. The technical goals of the project are to: (i) develop new multiplex real-time intracellular reporters for differentiation markers, (ii) develop stochastic models of progenitor cell proliferation and fate based on dynamic differentiation marker expression and (iii) validate the agent-based stochastic models of organoid development using siRNA targeting pathways known to de novo regulate differentiation.This project is being jointly supported by the Engineering Biology and Health Cluster in ENG/CBET and the Cellular Dynamics and Function Cluster in BIO/MCB.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.