Differentiating Engineered Tissue Images and Experimental Factors to Classify Cardiomyocyte Content
Differentiating Engineered Tissue Images and Experimental Factors to Classify Cardiomyocyte Content
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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
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发表时间：
2022
影响因子：
4.1
通讯作者：
Cremaschi, Selen
中科院分区：
文献类型：
--
作者：
Mohammadi, Samira;Hashemi, Mohammadjafar;Finklea, Ferdous;Lipke, Elizabeth Ann;Cremaschi, Selen
在这项研究中，我们使用机器学习 (ML) 对人诱导多能干细胞 (hiPSC) 负载的微球体分化第 10 天的心肌细胞 (CM) 含量进行分类，使用在分化过程中拍摄的易于获取的无损相差图像和可调节的实验参数。放大悬浮培养、使用工程组织支持干细胞分化以及用于改善悬浮系统中细胞微环境控制的 CM 生产需要非破坏性方法来跟踪工程组织发育。能够以公正的方式将基于早期分化时间点的可视化捕获实验者感知的“好”或“坏”批次的图像与实际实验结果结合起来，是朝着构建这些方法迈出的一步。近年来，机器学习技术已成功应用于识别关键过程参数，并使用此信息构建模型来描述细胞生产和 hiPSC 分化过程的结果。基于这些成功，我们在这里利用卷积神经网络 (CNN) 为 hiPSC-CM 分化第 10 天 (dd10) 的 CM 内容构建二元分类器模型。我们将两个独立的数据集视为分类模型的潜在输入特征。第一组包括在不同实验条件下分化批次的第 3 天和第 5 天拍摄的微球体组织的相差图像。第二组用可调节的实验分化参数补充图像，例如细胞浓度和微球体的大小。 CM 内容类别是充足的和不足的。仅使用图像的 CNN 分类器的准确率为 63%。实验特征的添加将准确性提高到 85%，表明可调参数在预测 CM 含量中的重要性。影响陈述使用机器学习方法来预测通过人类诱导的充满多能干细胞的工程组织微球体的悬浮心脏分化产生的工程心脏组织微球体的最终心肌细胞 (CM) 含量类别（充足与不足）。这些模型使用指定的实验特征和使用非破坏性廉价方法收集的数据，特别是在分化的最初几天拍摄的相差图像作为输入。最好的模型是使用实验特征和区分第 5 天图像训练的卷积神经网络。它以 85% 的准确度对 CM 内容进行分类，并通过合并早期时间点的图像来复制和形式化实验者关于分化结果的视觉直觉。
In this study, we used machine learning (ML) to classify the cardiomyocyte (CM) content on day 10 of the differentiation of human-induced pluripotent stem cell (hiPSC)-laden microspheroids using easily acquirable nondestructive phase-contrast images taken in the middle of differentiation and tunable experimental parameters. Scale-up suspension culture, use of engineered tissues to support stem cell differentiation, and CM production for improved control over cellular microenvironment in the suspension system need nondestructive methods to track engineered tissue development. The ability to couple images that capture experimenter perceived “good” or “bad” batches based on visualization at early differentiation time points with actual experimental outcomes in an unbiased way is a step toward building these methods. In recent years, ML techniques have been successfully applied to identify critical process parameters and use this information to build models that describe process outcomes in cell production and hiPSC differentiation. Building upon these successes, here, we utilize convolutional neural networks (CNNs) to build a binary classifier model for CM content on differentiation day 10 (dd10) for hiPSC-CMs. We consider two separate data sets as potential input features for the classification models. The first set includes phase-contrast images of microspheroid tissues taken on days 3 and 5 of the differentiation batches at different experimental conditions. The second set supplements the images with tunable experimental differentiation parameters, such as cell concentration and microspheroids' size. The CM content classes weresufficientandinsufficient. The accuracy of the CNN classifier using images only was 63%. The addition of experimental features increased the accuracy to 85%, indicating the importance of tunable parameters in predicting CM content.Impact statementMachine learning approaches were used to predict the final cardiomyocyte (CM) content class (sufficient vs. insufficient) of engineered cardiac tissue microspheroids produced through suspension-based cardiac differentiation of human-induced pluripotent stem cell-laden engineered tissue microspheroids. The models used specified experimental features and data collected using nondestructive inexpensive methods, specifically phase-contrast images taken during the initial days of differentiation as inputs. The best model was a convolutional neural network trained using experimental features and differentiation day 5 images. It classified the CM content with 85% accuracy and replicated and formalized experimenter's visual intuition about differentiation outcomes by incorporating images from early time points.