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: 2020983
• 负责人: Calin Belta
• 金额: $ 17.5万
• 依托单位: Trustees of Boston University
• 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=2020983&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.

患病或有缺陷的重要器官通常需要替换器官才能生存，但是替代器官的可用性严重限制了合适的组织匹配捐赠者的短缺以及复杂性，例如验尸，后者器官降低和免疫抑制。可以通过使用高忠诚度人工增长的器官来克服这些问题，但是实现该目标面临着艰巨的科学和工程挑战，该项目旨在开始满足。该项目将重点介绍肝脏器官中微观模式的概念证明，以模仿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血管化肝脏器官中的基因表达，以产生小叶类似模式。该项目的结果将定义机器人辅助生物设计的新领域。这项研究将导致新的生物学规则，合成生物学工具和可用于许多学科的微生物学。如果成功的话，另一个更广泛的影响将是一种可以用来创建一种新的，体外的，类似天然的器官进行生物学和医学研究的方法，为研究和修复合成人体器官的创造和修复打开了大门。 该项目包括针对研究生的研究培训和博士后研究人员。经过多种限制的生物学模式的规定方法。对模式形成的先前研究很大程度上取决于提供全球刺激和研究细胞培养中细胞命运的反应扩散介导的模式。这种方法仅产生静态模式，既没有对基因表达和产生的生物组织形成的精确空间或时间控制。当前的组织工程能力（例如3D打印和光遗传学）也无法概括在本机样器官中明显的多尺度自组装模式。所提出的方法将使微型机器人的精确控制能够在3D生物系统中实现动态控制对图案的动态控制，从而在现场产生范式转移。概念验证的目标是调节工程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。机器学习将用于得出和表征所需的多尺度模式，合成生物学，以赋予干细胞具有遗传回路，可以区分细胞以形成所需的组织构建体，并以微生物形成局部基因表达以形成组织构建体中的多阶段模式。特别是，研究人员将开发和控制能够维持和携带工程发件人细胞的微型机器人，驱动微型机器人和相关的发件人细胞在血管化的3D肝脏器官中到特定位置，并使用微机器人控制的发件人细胞与内皮细胞进行通信，从而诱导这些内皮细胞，从而使这些内皮细胞分泌Wnt wnt wnt wnt wnt和生成Zon Zon。该图案化的小叶分区将调节肝脏类器官的代谢活动。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。