Imai Yuta, Yoshida Kei, Matsumoto Megumi, Okada Mai, 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.
Regen Ther. 2018 Jul 9;9:15-23. doi: 10.1016/j.reth.2018.06.001. eCollection 2018 Dec.
Advancing industrial-scale manufacture of cells as therapeutic products is an example of the wide applications of regenerative medicine. However, one bottleneck in establishing stable and efficient cell manufacture is quality control. Owing to the lack of effective in-process measurement technology, analyzing the time-consuming and complex cell culture process that essentially determines cellular quality is difficult and only performed by manual microscopic observation. Our group has been applying advanced image-processing and machine-learning modeling techniques to construct prediction models that support quality evaluations during cell culture. In this study, as a model of errors during the cell culture process, intentional errors were compared to the standard culture and analyzed based only on the time-course morphological information of the cells.
Twenty-one lots of human mesenchymal stem cells (MSCs), including both bone-marrow-derived MSCs and adipose-derived MSCs, were cultured under 5 conditions (one standard and 4 types of intentional errors, such as clear failure of handlings and machinery malfunctions). Using time-course microscopic images, cell morphological profiles were quantitatively measured and utilized for visualization and prediction modeling. For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.
By modified PCA visualization, the differences in cellular lots and culture conditions were illustrated as traits on a morphological transition line plot and found to be effective descriptors for discriminating the deviated samples in a real-time manner. In prediction modeling, both the cell growth rate and error condition discrimination showed high accuracy (>80%), which required only 2 days of culture. Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large amount of failure data.
Morphological information that can be quantitatively acquired during cell culture has great potential as an in-process measurement tool for quality control in cell manufacturing processes.
推动细胞作为治疗产品的工业化规模制造是再生医学广泛应用的一个例子。然而，建立稳定高效的细胞制造的一个瓶颈是质量控制。由于缺乏有效的过程中测量技术，分析本质上决定细胞质量的耗时且复杂的细胞培养过程很困难，且仅通过手动显微镜观察来进行。我们的团队一直在应用先进的图像处理和机器学习建模技术来构建支持细胞培养过程中质量评估的预测模型。在本研究中，作为细胞培养过程中错误的模型，将故意错误与标准培养进行比较，并仅基于细胞的时间进程形态信息进行分析。
21批人间充质干细胞（MSCs），包括骨髓来源的MSCs和脂肪来源的MSCs，在5种条件下培养（一种标准条件和4种故意错误类型，如操作明显失误和机械故障）。使用时间进程显微镜图像，对细胞形态特征进行定量测量，并用于可视化和预测建模。对于可视化，使用了改进的主成分分析（PCA）。对于预测建模，应用了线性回归分析和MT方法。
通过改进的PCA可视化，细胞批次和培养条件的差异在形态转变线图上被描绘为特征，并被发现是实时区分偏差样本的有效描述符。在预测建模中，细胞生长速率和错误条件判别均显示出高精度（>80%），这仅需要2天的培养时间。此外，我们使用MT方法证明了不同机器学习概念的适用性，该方法对于大多数收集标准数据但没有大量故障数据的制造过程有效。
在细胞培养过程中可以定量获取的形态信息作为细胞制造过程中质量控制的过程中测量工具具有巨大潜力。
