Suppr超能文献

诱导多能干细胞定向分化为确定内胚层的计算机模拟建模

In silico modeling of directed differentiation of induced pluripotent stem cells to definitive endoderm.

作者信息

Mostofinejad Amirmahdi, Romero David A, Brinson Dana, Waddell Thomas K, Karoubi Golnaz, Amon Cristina H

机构信息

Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.

Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.

出版信息

PLoS Comput Biol. 2025 Aug 21;21(8):e1013407. doi: 10.1371/journal.pcbi.1013407. eCollection 2025 Aug.

Abstract

Differentiation of embryonic stem cells and induced pluripotent stem cells (iPSCs) into endoderm derivatives, including thyroid, thymus, lungs, liver, and pancreas, has broad implications for disease modeling and therapy. We utilize and expand a model development approach previously outlined by the authors to construct a model for the directed differentiation of iPSCs into definitive endoderm (DE). Assuming discrete intermediate stages in the differentiation process with a homogeneous population in each stage, three lineage models with two, three, and four populations and three growth models are constructed. Additionally, three models for error distribution are defined, resulting in a total of 27 models. Experimental data obtained in vitro are used for model calibration, model selection, and final validation. Model selection suggests that no transitory state during differentiation expresses the DE biomarkers CD117 and CD184, a finding corroborated by existing literature. Additionally, space-limited growth models, such as logistic and Gompertz growth, outperform exponential growth. Validation of the inferred model with leave-out data results in prediction errors of 26.4%. Using the inferred model, it is predicted that the optimal differentiation period is between 1.9 and 2.4 days, plating populations closer to 300 000 cells per well result in the highest yield efficiency, and that iPSC differentiation outpaces the DE proliferation as the main driver of the population dynamics. We also demonstrate that the model can predict the effect of growth modulators on cell population dynamics. Our model serves as a valuable tool for optimizing differentiation protocols, providing insights into developmental biology.

摘要

将胚胎干细胞和诱导多能干细胞(iPSC)分化为内胚层衍生物,包括甲状腺、胸腺、肺、肝和胰腺,对疾病建模和治疗具有广泛的意义。我们利用并扩展了作者之前概述的模型开发方法,构建了一个将iPSC定向分化为定形内胚层(DE)的模型。假设分化过程中有离散的中间阶段,且每个阶段的细胞群体是均匀的,构建了具有两个、三个和四个群体的三种谱系模型以及三种生长模型。此外,定义了三种误差分布模型,总共得到27种模型。体外获得的实验数据用于模型校准、模型选择和最终验证。模型选择表明,分化过程中没有过渡状态表达DE生物标志物CD117和CD184,这一发现得到了现有文献的证实。此外,空间受限的生长模型,如逻辑斯蒂增长模型和冈珀茨增长模型,优于指数增长模型。使用留一法数据对推断模型进行验证,预测误差为26.4%。利用推断模型预测,最佳分化期在1.9至2.4天之间,每孔接种接近300000个细胞的群体产生的产量效率最高,并且iPSC分化超过DE增殖,成为群体动态的主要驱动因素。我们还证明该模型可以预测生长调节剂对细胞群体动态的影响。我们的模型是优化分化方案的宝贵工具,为发育生物学提供了见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d19/12404646/34fde5725dbd/pcbi.1013407.g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验