Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel‐encapsulated hiPSCs
Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel‐encapsulated hiPSCs
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水凝胶封装的 hiPSC 的心脏分化结果分类、分化第 10 天的心肌细胞百分比
DOI：
10.1002/amp2.10148
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Cremaschi, Selen
中科院分区：
文献类型：
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作者：
Mohammadi, Samira;Hashemi, Mohammadjafar;Finklea, Ferdous;Williams, Bianca;Lipke, Elizabeth;Cremaschi, Selen
本研究采用机器学习（ML）模型来预测包封在水凝胶微球体中的人诱导多能干细胞（hiPSC）分化后的心肌细胞（CM）含量，并确定影响CM产量的主要实验变量。了解如何使用hiPSC增强CM生成对于走向大规模生产并将其用于开发治疗药物和再生治疗至关重要。随着分化过程的改善，心肌细胞的产生已经进入了一个新的时代。然而，现有的工艺对于可靠的CM制造来说不够稳健。使用ML技术来关联初始的、实验指定的干细胞微环境对心脏分化的影响，可以识别重要的过程特征。用于训练ML模型的初始可调（受控）输入特征是从85个单独的实验中提取的。使用特征选择来选择受控输入特征的子集，并用于模型构建。采用随机森林、高斯过程和支持向量机作为ML模型。建立模型来预测分化第10天CM含量充足和不足两类。最好的模型预测足够的类的准确度为75%，精度为71%。确定的关键特征包括冷冻后传代次数、培养基类型、PF纤维蛋白原浓度、CHIR/S/V、轴比和细胞浓度，这些特征有助于深入了解重要的实验条件。这项研究表明，我们可以从实验中提取信息，并建立预测模型，通过使用ML技术来增强细胞生产过程。
This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.