Shibata Mayu, Okamura Kohji, Yura Kei, Umezawa Akihiro
Department of Reproductive Biology, National Center for Child Health and Development, Tokyo, 157-8535, Japan.
Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, 112-8610, Japan.
Regen Ther. 2020 Oct 16;15:195-201. doi: 10.1016/j.reth.2020.09.005. eCollection 2020 Dec.
Establishment of a cell classification platform for evaluation and selection of human pluripotent stem cells (hPSCs) is of great importance to assure the efficacy and safety of cell-based therapy. In our previous work, we introduced a discriminant function that evaluates pluripotency from the cells' glycome. However, it is not yet suitable for general use.
The current study aims to establish a high-precision cell classification platform introducing supervised machine learning and test the platform on glycome analysis as a proof-of-concept study. We employed linear classification and neural network to the lectin microarray data from 1577 human cells and categorized them into five classes including hPSCs.
The linear-classification-based model and the neural-network-based model successfully predicted the sample type with accuracies of 89% and 97%, respectively.
Because of the high recognition accuracies and the small amount of computing resources required for these analyses, our platform can be a high precision conventional cell classification system for hPSCs.
建立一个用于评估和筛选人多能干细胞（hPSC）的细胞分类平台对于确保基于细胞的治疗的有效性和安全性至关重要。在我们之前的工作中，我们引入了一种从细胞糖组评估多能性的判别函数。然而，它尚未适用于一般用途。
当前研究旨在建立一个引入监督机器学习的高精度细胞分类平台，并在糖组分析上测试该平台作为概念验证研究。我们将线性分类和神经网络应用于来自1577个人类细胞的凝集素微阵列数据，并将它们分为包括hPSC在内的五个类别。
基于线性分类的模型和基于神经网络的模型分别以89%和97%的准确率成功预测了样本类型。
由于这些分析所需的识别准确率高且计算资源量少，我们的平台可以成为用于hPSC的高精度传统细胞分类系统。
