GCR: Collaborative Research: Fine-grain generation of multiscale patterns in programmable organoids using microrobots

GCR：协作研究：使用微型机器人在可编程类器官中细粒度生成多尺度模式

• 批准号: 2020973
• 负责人: Sambeeta Das
• 金额: $ 27.38万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020973&HistoricalAwards=false
• 关键词: GCR; Collaborative; Research; Fine; grain

People with diseased or defective vital organs often need organ replacement to survive, but the availability of replacement organs is severely restricted by shortages of suitable tissue-matched donors and complexities such as postmortem organ deterioration and immunological rejection. These problems could be overcome by using high fidelity artificially-grown organs, but achieving that goal faces daunting and long-standing scientific and engineering challenges that this project aims to begin to meet. The project will focus on proof-of-concept generation of microscale patterns in a liver organoid to mimic the anatomical structure of lobules arranged in hexagonal patterns. The researchers will use microrobots to dynamically regulate gene expression in 3D vascularized liver organoids to generate the lobule like patterns. The results of this project will define a new area of robot-assisted biological design. This research will result in new biological rules, synthetic biology tools, and microrobotics that can be applied in numerous disciplines. If successful, another broader impact will be the demonstration of a method that could be used to create a new, in vitro, native-like organoid for biological and medical research, opening the door for research into the creation and repair of synthetic human organs. The project includes research training for graduate students and postdoctoral researchers.Conventional methods of reproducing biological patterns in vitro suffer from multiple limitations. Previous research on pattern formation has largely relied on delivering global stimuli and studying reaction-diffusion mediated patterning of cell fates in the cell culture. Such methods yield only static patterns and give neither precise spatial nor temporal control over gene expression and resulting biological tissue formation. Current tissue engineering capabilities such as 3D printing and optogenetics are also unable to recapitulate the multiscale self-assembled patterns evident in native-like organs. The proposed approach will enable precise control of microrobots to achieve dynamic control over patterning in 3D biological systems, creating a paradigm shift in the field. The proof-of-concept goal is to modulate localized gene expression in engineered 3D tissue constructs to control the emergence of multiscale patterns. Machine learning will be used to derive and characterize desired multiscale patterns, synthetic biology to endow the stem cells with genetic circuits that can differentiate the cells to form desired tissue constructs, and microrobots to alter localized gene expression to form multiscale patterns in tissue constructs. In particular, the researchers will develop and control microrobots capable of sustaining and carrying engineered sender cells, drive the microrobots and associated sender cells within a vascularized 3D liver organoid to specific locations, and use the microrobot controlled sender cells to communicate with endothelial cells, inducing these endothelial cells to secrete Wnt and generate gradients controlling liver lobule zonation. This patterned lobule zonation will regulate the metabolic activity of the liver organoids.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.

重要器官患病或有缺陷的人通常需要器官置换才能生存，但由于缺乏合适的组织匹配供体以及死后器官退化和免疫排斥等复杂性，替代器官的可用性受到严重限制。这些问题可以通过使用高保真人工生长的器官来克服，但实现这一目标面临着艰巨的和长期存在的科学和工程挑战，该项目旨在开始满足。该项目将重点关注肝脏类器官中微观模式的概念验证生成，以模拟以六边形模式排列的小叶的解剖结构。研究人员将使用微型机器人动态调节3D血管化肝脏类器官中的基因表达，以生成小叶样模式。该项目的结果将定义机器人辅助生物设计的新领域。这项研究将产生新的生物学规则，合成生物学工具和可应用于许多学科的微型机器人。如果成功，另一个更广泛的影响将是展示一种可用于生物和医学研究的新的体外天然类器官，为人造人体器官的创造和修复研究打开大门。 该项目包括对研究生和博士后研究人员的研究培训。传统的体外复制生物模式的方法受到多种限制。以前的研究模式的形成在很大程度上依赖于提供全球刺激和研究反应扩散介导的细胞培养中的细胞命运的图案。这些方法只产生静态模式，对基因表达和生物组织形成既没有精确的空间控制，也没有精确的时间控制。目前的组织工程能力，如3D打印和光遗传学，也无法概括天然器官中明显的多尺度自组装模式。所提出的方法将能够精确控制微型机器人，以实现对3D生物系统中图案的动态控制，从而在该领域实现范式转变。概念验证的目标是调节工程化3D组织构建体中的局部基因表达，以控制多尺度模式的出现。机器学习将被用于获得和表征所需的多尺度模式，合成生物学将赋予干细胞遗传电路，这些遗传电路可以使细胞分化以形成所需的组织结构，而微型机器人将改变局部基因表达以形成组织结构中的多尺度模式。特别是，研究人员将开发和控制能够维持和携带工程化发送细胞的微型机器人，将微型机器人和血管化3D肝脏类器官内的相关发送细胞驱动到特定位置，并使用微型机器人控制的发送细胞与内皮细胞通信，诱导这些内皮细胞分泌Wnt并产生控制肝小叶分区的梯度。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。