Nagasaka Risako, Gotou Yuto, Yoshida Kei, Kanie Kei, Shimizu Kazunori, Honda Hiroyuki, Kato Ryuji
Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8601, Japan.
Department of Biotechnology, Graduate School of Engineering, Nagoya University, Furocho, Chikusa-ku, Nagoya 464-8602, Japan.
J Biosci Bioeng. 2017 May;123(5):642-650. doi: 10.1016/j.jbiosc.2016.12.015. Epub 2017 Feb 8.
To meet the growing demand for human induced pluripotent stem cells (iPSCs) for various applications, technologies that enable the manufacturing of iPSCs on a large scale should be developed. There are several technological challenges in iPSC manufacturing technology. Image-based cell quality evaluation technology for monitoring iPSC quality in culture enables the manufacture of intact cells for further applications. Although several studies have reported the effectiveness of image-based evaluation of iPSCs, it remains challenging to detect irregularities that may arise using the same processing operations during quality evaluation of automated processing. In this study, we investigated the evaluation performance of image-based cell quality analysis in detecting small differences that can result from human measurement, even when the same protocol is followed. To imitate such culture conditions, by image-analysis guided colony pickup, we changed the proportions of morphologically different subpopulations: "good morphology, regular morphology correlated with undifferentiation marker expression" and "bad morphology, irregular morphology correlated with loss of undifferentiation marker expression". In addition, comprehensive gene-expression and metabolomics analyses were carried out for the same samples to investigate performance differences. Our data shows an example of investigating the usefulness and sensitivity of quality evaluation methods for iPSC quality monitoring.
为满足各种应用对人诱导多能干细胞（iPSC）不断增长的需求，应开发能够大规模制造iPSC的技术。iPSC制造技术存在若干技术挑战。用于监测培养过程中iPSC质量的基于图像的细胞质量评估技术能够制造完整的细胞以供进一步应用。尽管有几项研究报告了基于图像评估iPSC的有效性，但在自动处理的质量评估过程中，使用相同的处理操作检测可能出现的不规则情况仍然具有挑战性。在本研究中，我们调查了基于图像的细胞质量分析在检测即使遵循相同方案时人类测量可能产生的微小差异方面的评估性能。为模拟此类培养条件，通过图像分析引导的集落挑选，我们改变了形态不同的亚群的比例：“形态良好、与未分化标记表达相关的规则形态”和“形态不良、与未分化标记表达丧失相关的不规则形态”。此外，对相同样本进行了全面的基因表达和代谢组学分析，以研究性能差异。我们的数据展示了一个调查iPSC质量监测质量评估方法有用性和敏感性的实例。
