FMSG:BIO: Integrating Artificial Intelligence with Bioprinting for Future Manufacturing of Organoids

FMSG：BIO：将人工智能与生物打印相结合，用于未来类器官的制造

• 批准号: 2229156
• 负责人: Ipsita Banerjee
• 金额: $ 50万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229156&HistoricalAwards=false
• 关键词: FMSG; BIO; Integrating; Artificial; Intelligence

Human pluripotent stem cell (hPSC) derived organoids are highly valuable for disease treatment and therapeutic development. Translating organoids to practical use will require scalable and reproducible methods to produce high-quality organoids. This project will devise strategies exploiting three-dimensional (3D) bioprinting for scalable organoid manufacturing. Machine Learning (ML)/Artificial Intelligence (AI) techniques will be developed to rapidly assess organoid quality from microscopy images. The goal is to offer a pathway for large-scale manufacturing of tailored and patient-specific organoids by augmenting ML/AI techniques for 3D bioprinting. The research will provide trainees with multidisciplinary skill sets, encompassing bioprinting, organoid engineering, imaging, and AI methods. In addition, the project will integrate global and social outreach aspects into engineering curriculum, offering a holistic educational pathway to train the future, globally aware U.S. biomanufacturing workforce to lead the world in revolutionary manufacturing concepts.This proof-of-concept Future Manufacturing Seed Grant (FMSG) project aims to integrate beneficial mechano-signaling pathways for organoid manufacturing via ML/AI aided 3D bioprinting. In executing this approach, the goal is to develop non-invasive assays to measure organoid phenotype to meet the critical quality attributes of the manufacturing pipeline. The central hypothesis is that imposing cell confinement via encapsulation will induce favorable mechano-transduction pathways, which will enhance organoid differentiation by deactivating actin filaments. The resulting cytoskeletal modification from actin remodeling can be exploited to extract information about cell characteristics to predict phenotype. This hypothesis will be tested by developing techniques for (i) cell-confinement driven organoid derivation using 3D bioprinting; and (ii) predicting cell fate from cytoskeletal modifications using Artificial Intelligence (AI) models to identify features of organoid development in bright-field images. This FMSG project demonstrate the feasibility of integrating AI with 3D bioprinting in the organoid generation pipeline, with the long-term goal of enabling scalable manufacturing of organoids with stringent in-line quality control.This project is jointly funded by the Division of Chemical, Bioengineering, Environmental, and Transport Systems and the Division of Engineering Education and Centers in the Directorate for Engineering and the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences.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.

人类多能干细胞 (hPSC) 衍生的类器官对于疾病治疗和治疗开发非常有价值。将类器官转化为实际应用需要可扩展且可重复的方法来生产高质量的类器官。该项目将制定利用三维（3D）生物打印进行可扩展类器官制造的策略。将开发机器学习（ML）/人工智能（AI）技术来快速评估显微镜图像中的类器官质量。目标是通过增强 3D 生物打印的 ML/AI 技术，为大规模生产定制的、针对患者的类器官提供一条途径。该研究将为学员提供多学科技能，包括生物打印、类器官工程、成像和人工智能方法。此外，该项目还将把全球和社会外展方面纳入工程课程，提供全面的教育途径来培训未来、具有全球意识的美国生物制造劳动力，以革命性的制造概念引领世界。这项概念验证的未来制造种子补助金(FMSG) 项目旨在通过 ML/AI 辅助 3D 生物打印整合有益的机械信号通路，用于类器官制造。在执行这种方法时，目标是开发非侵入性测定法来测量类器官表型，以满足生产流程的关键质量属性。中心假设是，通过封装施加细胞限制将诱导有利的机械传导途径，这将通过使肌动蛋白丝失活来增强类器官的分化。肌动蛋白重塑产生的细胞骨架修饰可用于提取有关细胞特征的信息以预测表型。这一假设将通过开发以下技术来检验：(i) 使用 3D 生物打印进行细胞限制驱动的类器官衍生； (ii) 使用人工智能 (AI) 模型通过细胞骨架修饰预测细胞命运，以识别明场图像中类器官发育的特征。该 FMSG 项目展示了在类器官生成管道中将人工智能与 3D 生物打印相结合的可行性，其长期目标是通过严格的在线质量控制实现类器官的可扩展制造。该项目由化学与生物工程部门共同资助、环境和运输系统以及工程教育部和工程理事会中心以及生物科学理事会分子和细胞生物科学部。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。