Department of Chemical and Biological Engineering, University of Wisconsin, 3637 Engineering Hall, 1415 Engineering Drive, Madison, Wisconsin, 53706.
Biotechnol Bioeng. 2013 Nov;110(11):3024-37. doi: 10.1002/bit.24968. Epub 2013 Jul 9.
Human pluripotent stem cells (hPSCs) have an unparalleled potential for tissue engineering applications including regenerative therapies and in vitro cell-based models for studying normal and diseased tissue morphogenesis, or drug and toxicological screens. While numerous hPSC differentiation methods have been developed to generate various somatic cell types, the potential of hPSC-based technologies is hinged on the ability to translate these established lab-scale differentiation systems to large-scale processes to meet the industrial and clinical demands for these somatic cell types. Here, we demonstrate a strategy for investigating the efficiency and scalability of hPSC differentiation platforms. Using two previously reported epithelial differentiation systems as models, we fit an ODE-based kinetic model to data representing dynamics of various cell subpopulations present in our culture. This fit was performed by estimating rate constants of each cell subpopulation's cell fate decisions (self-renewal, differentiation, death). Sensitivity analyses on predicted rate constants indicated which cell fate decisions had the greatest impact on overall epithelial cell yield in each differentiation process. In addition, we found that the final cell yield was limited by the self-renewal rate of either the progenitor state or the final differentiated state, depending on the differentiation protocol. Also, the relative impact of these cell fate decision rates was highly dependent on the maximum capacity of the cell culture system. Overall, we outline a novel approach for quantitative analysis of established laboratory-scale hPSC differentiation systems and this approach may ease development to produce large quantities of cells for tissue engineering applications.
人类多能干细胞（hPSCs）在组织工程应用方面具有无与伦比的潜力，包括再生疗法和体外基于细胞的模型，用于研究正常和患病组织形态发生，或药物和毒理学筛选。虽然已经开发了许多 hPSC 分化方法来产生各种体细胞类型，但 hPSC 基技术的潜力取决于将这些已建立的实验室规模分化系统转化为大规模过程的能力，以满足对这些体细胞类型的工业和临床需求。在这里，我们展示了一种研究 hPSC 分化平台效率和可扩展性的策略。使用之前报道的两个上皮分化系统作为模型，我们根据代表我们培养物中存在的各种细胞亚群动态的 ODE 基动力学模型拟合数据。通过估计每个细胞亚群的细胞命运决定（自我更新、分化、死亡）的速率常数来进行这种拟合。对预测的速率常数进行敏感性分析表明，在每个分化过程中，哪些细胞命运决定对总体上皮细胞产量的影响最大。此外，我们发现最终细胞产量受到祖细胞状态或最终分化状态的自我更新率的限制，这取决于分化方案。此外，这些细胞命运决策率的相对影响高度依赖于细胞培养系统的最大容量。总的来说，我们概述了一种用于定量分析已建立的实验室规模 hPSC 分化系统的新方法，该方法可能有助于开发大量用于组织工程应用的细胞。
