Controlling Stem Cell Fate through Computational Modeling

通过计算模型控制干细胞的命运

• 批准号: 9166324
• 负责人: Jeremy Purvis
• 金额: $ 228万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9166324
• 关键词: Bacteria; Cell Therapy; Cells; Clinical; Complex; Computer Simulation; Data; Development; Dose; Drug Delivery Systems; Event; Genetic Transcription; Goals; Health; Human; Interstitial Lung Diseases; Laboratories; Lead; Lung; Lung diseases; Measurement; Microscopy; Modeling; Molecular; Monitor; Patients; Price; Procedures; Protocols documentation; Pulmonary Emphysema; Pulmonary Fibrosis; Regenerative Medicine; Regulatory Pathway; Resolution; Series; Signal Transduction; Sports; Stem cells; Time; Tissues; Training; Work; cancer cell; cell type; design; essays; human stem cells; improved; induced pluripotent stem cell; live cell microscopy; model building; novel; predictive modeling; predictive tools; research study; stem cell fate; stem cell therapy; time use; virtual

ABSTRACT Since the discovery of induced pluripotent stem cells, a major goal for regenerative medicine has been to replace damaged or diseased tissues with patient-derived cells. In principle, it should be possible to generate functional tissues for clinical use through directed differentiation protocols that specifically alter the fate of pluripotent cells. Unfortunately, there are many challenges associated with directed differentiation. First, we do not fully understand all of the signaling mechanisms that participate in developmental regulatory pathways. We therefore cannot always identify the correct combination of molecular factors that will recapitulate these signals in a laboratory setting. Even if the required factors are known, it is unclear what doses, timing, or combinations of factors will produce an efficient differentiation. Most protocols are developed by trial-and-error; take years to optimize; and lead to incomplete, inefficient, or heterogeneous mixtures of the desired cell types. Clearly, there is a critical need for improving differentiation procedures if we expect to produce realistic cell-based therapies. In this essay, I propose a novel solution for controlling the fate of human stem cells. The approach, which we have pioneered in our lab, works by allowing a computational model to design the differentiation protocol. To build the model, we use time-lapse microscopy to monitor the expression of key developmental markers over the time course of differentiation. These data provide a mechanistic description of cellular fate decisions at single-cell resolution. To train the model, we then apply a series of systematic perturbations to differentiating cells, monitor the cells by live-cell microscopy, and integrate these new measurements into the working model. Finally, we use the model as a predictive tool by performing thousands of virtual experiments to identify a set of perturbations that are predicted to alter stem cell fate in a prescribed way. Model predictions are validated through single-cell transcription profiling, and these data are used to iteratively refine the model’s predictive power. As proof of principle, we will use this approach to produce precise combinations of human lung progenitor cells that could be used to treat pulmonary diseases such as pulmonary fibrosis, emphysema, or interstitial lung disease. In the near term, our approach will enable us to manipulate pluripotent cells to acquire a precise combination of differentiated cell fates—overcoming a major hurdle in stem cell therapy. In the long term, this approach could be used more generally to control the fate of other cell types including bacteria or cancer cells, and may help to design improved drug delivery protocols.

摘要 自从发现诱导多能干细胞以来，再生医学的一个主要目标是 用患者来源的细胞替换受损或患病的组织。原则上，应该可以生成 通过定向分化方案用于临床用途的功能组织， 多能细胞不幸的是，有许多与定向分化相关的挑战。首先，我们 没有完全理解参与发育调节途径的所有信号传导机制。我们 因此，并不能总是识别出能够概括这些信号的分子因素的正确组合 in a laboratory实验setting设置.即使所需的因素是已知的，也不清楚什么剂量、时间或组合 将产生一个有效的差异化。大多数协议都是通过试错法开发的;需要数年时间才能实现 优化;并导致所需细胞类型的不完全、低效或异质混合物。显然 如果我们期望产生现实的基于细胞的疗法，那么改进分化程序是至关重要的。 在这篇文章中，我提出了一个新的解决方案来控制人类干细胞的命运。方法，我们 在我们的实验室里开创性的，通过允许一个计算模型来设计分化协议。到 建立模型，我们使用延时显微镜来监测关键发育标记物的表达， 分化的时间进程。这些数据提供了细胞命运决定的机制描述， 单细胞分辨率。为了训练模型，我们应用一系列系统扰动来区分 细胞，通过活细胞显微镜监测细胞，并将这些新的测量结果整合到工作模型中。 最后，我们使用该模型作为预测工具，通过执行数千个虚拟实验来识别一组 预测以规定方式改变干细胞命运的扰动。模型预测得到验证 通过单细胞转录谱分析，这些数据被用来迭代地完善模型的预测 动力.作为原理的证明，我们将使用这种方法来生产人类肺的精确组合， 可以用于治疗肺部疾病如肺纤维化、肺气肿或 间质性肺病。在短期内，我们的方法将使我们能够操纵多能细胞来获得 分化细胞命运的精确组合-克服干细胞治疗中的主要障碍。从长远 长期而言，这种方法可以更普遍地用于控制其他细胞类型的命运，包括细菌或 癌细胞，并可能有助于设计改进的药物输送方案。