Collaborative Research: RECODE: Directing and Controlling Cardiac Differentiation Through Cellular and Microenvironmental Manipulation and Application of Machine-Learning

合作研究：RECODE：通过细胞和微环境操纵以及机器学习的应用来指导和控制心脏分化

• 批准号: 2135059
• 负责人: Elizabeth Lipke
• 金额: $ 92.47万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135059&HistoricalAwards=false
• 关键词: Collaborative; Research; RECODE; Directing; Controlling

US mortality rates from heart disease are increasing, driven particularly by the increasing prevalence of patients with heart failure. Limited availability of native human cardiac tissue impedes research, drug discovery, and clinical cardiac regeneration efforts. Treatment with stem cell-derived cardiac tissues has exceptionally high potential to achieve clinically meaningful outcomes. However, the generation of a heterogeneous mixture of cell types is a critical barrier to cell-based cardiac therapy. By employing developmental biology, tissue engineering, and machine learning, this Reproducible Cells and Organoids via Directed-Differentiation Encoding (RECODE)research builds the foundation for overcoming this obstacle and develops methodologies and design approaches to produce functional cell types needed in understanding and treating heart disease. This project will support undergraduate students from Alabama State University – a historically black university – to participate in summer research experiences at Auburn University.Despite significant advances in our understanding of human induced pluripotent stem cell and cardiac development biology, our ability to generate specific cardiac cell subtypes from pluripotent stem cells in sufficient quantities remains limited. Cardiac differentiation of human induced pluripotent stem cells has been broken down into a stepwise process from pluripotency to mesoderm to cardiac progenitors to first and second heart fields. However, this progression occurs at differing rates, require differing concentrations, durations, and timing of exposure to key cell signaling molecules, and yield varying concentrations of cardiomyocytes. Understanding the population dynamics and probabilities that a given cell will move towards becoming one cell type versus another is necessary for making predictions and directing decisions to achieve a desired final cell type or a mixture of cell types. The goal of this RECODE project is to establish a paired experimental process and guiding hybrid model utilizing real-time measurements from differentiating cardiomyocytes to predict both the outcome of ongoing cardiac differentiation and the process parameters that should be adjusted to achieve the desired result. The project work will (1) marry innovative machine learning tools and cardiac developmental stages to mine single cell RNA sequencing data to identify key developmental decisions and levers that control cell fate at these instances, (2) perform directed cardiac differentiation in 3D to address complex autocrine, paracrine, cell-cell and cell-matrix interactions that are absent in conventional 2D assays, (3) employ cardiac cell subtype-specific fluorescent reporter to quantify differentiation outcome in real-time, and (4) develop a process control analytical platform that integrates differentiation outcome data with experimentally-defined input parameters that can enable generation of specific composition of cardiac cell subtypes on-demand using robustly validated and reproducible differentiation design rules.This RECODE award is co-funded by the Mechanics and Engineering Materials Cluster in the Division of Civil, Mechanical, and Manufacturing Innovation and the Engineering Biology and Health Cluster in the Division of Chemical, Bioengineering, Environmental, and Transport Systems.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.

美国心脏病的死亡率正在上升，尤其是由于心力衰竭患者的患病率不断上升。天然人类心脏组织的有限可用性阻碍了研究、药物发现和临床心脏再生的努力。干细胞来源的心脏组织治疗具有非常高的潜力，以实现临床有意义的结果。然而，细胞类型的异质混合物的产生是基于细胞的心脏治疗的关键障碍。通过利用发育生物学、组织工程学和机器学习，这项通过定向分化编码（RECODE）的可再生细胞和类器官研究为克服这一障碍奠定了基础，并开发了方法和设计方法，以产生理解和治疗心脏病所需的功能细胞类型。该项目将支持阿拉巴马州立大学（一所历史悠久的黑人大学）的本科生参加奥本大学的夏季研究经历。尽管我们对人类诱导多能干细胞和心脏发育生物学的理解取得了重大进展，但我们从多能干细胞中产生足够数量的特定心脏细胞亚型的能力仍然有限。人类诱导多能干细胞的心脏分化分为从多能性到中胚层到心脏祖细胞到第一和第二心脏野的逐步过程。然而，这种进展以不同的速率发生，需要不同的浓度、持续时间和暴露于关键细胞信号分子的时间，并产生不同浓度的心肌细胞。了解种群动态和特定细胞向一种细胞类型转变的概率，对于做出预测和指导决策以实现期望的最终细胞类型或细胞类型的混合是必要的。RECODE项目的目标是建立一个配对实验过程和指导混合模型，利用分化心肌细胞的实时测量来预测正在进行的心脏分化的结果和应该调整的过程参数以达到预期的结果。该项目工作将(1)结合创新的机器学习工具和心脏发育阶段，挖掘单细胞RNA测序数据，以确定在这些情况下控制细胞命运的关键发育决策和杠杆；(2)在3D中进行定向心脏分化，以解决传统2D分析中缺乏的复杂的自分泌、旁分泌、细胞-细胞和细胞-基质相互作用。(3)利用心肌细胞亚型特异性荧光报告因子实时量化分化结果；(4)开发一个过程控制分析平台，将分化结果数据与实验定义的输入参数相结合，使用经过验证且可重复的分化设计规则，可以按需生成心肌细胞亚型的特定组成。该RECODE奖项由土木、机械和制造创新部门的力学和工程材料部门以及化学、生物工程、环境和运输系统部门的工程生物学和健康部门共同资助。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Investigating tunable experiment variable effects on hiPSC-CMs maturation via unsupervised learning

通过无监督学习研究可调实验变量对 hiPSC-CM 成熟的影响

• 发表时间: 2023
• 期刊: Computer aided chemical engineering
• 作者: Rajendiran, Shenbageshwaran;Hashemi, Mohammadjafar;Finklea, Ferdous;Young, Nathan;Lipke, Elizabeth;Cremaschi, Selen
• 通讯作者: Cremaschi, Selen

Differentiating Engineered Tissue Images and Experimental Factors to Classify Cardiomyocyte Content

区分工程组织图像和实验因素以对心肌细胞含量进行分类

• DOI: 10.1089/ten.tea.2022.0122
• 发表时间: 2022
• 期刊: Tissue Engineering Part A
• 影响因子: 4.1
• 作者: Mohammadi, Samira;Hashemi, Mohammadjafar;Finklea, Ferdous;Lipke, Elizabeth Ann;Cremaschi, Selen
• 通讯作者: Cremaschi, Selen

Image classification of experimental yields for cardiomyocyte cells differentiated from human induced pluripotent stem cells

人诱导多能干细胞分化的心肌细胞实验产量的图像分类

• 发表时间: 2022
• 期刊: Computer aided chemical engineering
• 作者: Mohammadi, B.;Finklea, F.;Hashemi, M.;Lipke, E.;Cremaschi, S.
• 通讯作者: Cremaschi, S.