Data_Sheet_1_Application and Comparison of Supervised Learning Strategies to Classify Polarity of Epithelial Cell Spheroids in 3D Culture.PDF

Three-dimensional culture systems that allow generation of monolayered epithelial cell spheroids are widely used to study epithelial function in vitro. Epithelial spheroid formation is applied to address cellular consequences of (mono)-genetic disorders, that is, ciliopathies, in toxicity testing, or to develop treatment options aimed to restore proper epithelial cell characteristics and function. With the potential of a high-throughput method, the main obstacle to efficient application of the spheroid formation assay so far is the laborious, time-consuming, and bias-prone analysis of spheroid images by individuals. Hundredths of multidimensional fluorescence images are blinded, rated by three persons, and subsequently, differences in ratings are compared and discussed. Here, we apply supervised learning and compare strategies based on machine learning versus deep learning. While deep learning approaches can directly process raw image data, machine learning requires transformed data of features extracted from fluorescence images. We verify the accuracy of both strategies on a validation data set, analyse an experimental data set, and observe that different strategies can be very accurate. Deep learning, however, is less sensitive to overfitting and experimental batch-to-batch variations, thus providing a rather powerful and easily adjustable classification tool.

可生成单层上皮细胞球体的三维培养体系，已被广泛应用于体外上皮功能研究。上皮球体形成实验可用于探究（单）遗传性疾病（即纤毛病（ciliopathies））的细胞层面效应，既可应用于毒性测试，也可用于开发旨在恢复上皮细胞正常特征与功能的治疗方案。尽管该方法具备高通量应用潜力，但目前限制球体形成检测实验高效落地的核心障碍，是人工分析球体图像时存在的工作量繁重、耗时冗长且易引入偏差的问题。本研究将数百幅多维荧光图像进行盲法处理后，由三名研究者独立评分，随后对评分差异展开比较与讨论。在此基础上，我们采用监督学习方法，对比了基于机器学习与深度学习的两类分析策略。深度学习方法可直接处理原始图像数据，而机器学习则需依托从荧光图像中提取的特征经转换后得到的数据方可开展分析。我们在验证数据集上验证了两种策略的准确性，并对实验数据集进行了分析，结果显示不同策略均可实现较高的分类准确率。不过，深度学习对过拟合以及实验批次间差异的敏感性更低，因此可作为一款性能强劲且易于调整的分类工具。