Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
PLoS One. 2011;6(11):e27672. doi: 10.1371/journal.pone.0027672. Epub 2011 Nov 16.
Current cell culture practices are dependent upon human operators and remain laborious and highly subjective, resulting in large variations and inconsistent outcomes, especially when using visual assessments of cell confluency to determine the appropriate time to subculture cells. Although efforts to automate cell culture with robotic systems are underway, the majority of such systems still require human intervention to determine when to subculture. Thus, it is necessary to accurately and objectively determine the appropriate time for cell passaging. Optimal stem cell culturing that maintains cell pluripotency while maximizing cell yields will be especially important for efficient, cost-effective stem cell-based therapies. Toward this goal we developed a real-time computer vision-based system that monitors the degree of cell confluency with a precision of 0.791±0.031 and recall of 0.559±0.043. The system consists of an automated phase-contrast time-lapse microscope and a server. Multiple dishes are sequentially imaged and the data is uploaded to the server that performs computer vision processing, predicts when cells will exceed a pre-defined threshold for optimal cell confluency, and provides a Web-based interface for remote cell culture monitoring. Human operators are also notified via text messaging and e-mail 4 hours prior to reaching this threshold and immediately upon reaching this threshold. This system was successfully used to direct the expansion of a paradigm stem cell population, C2C12 cells. Computer-directed and human-directed control subcultures required 3 serial cultures to achieve the theoretical target cell yield of 50 million C2C12 cells and showed no difference for myogenic and osteogenic differentiation. This automated vision-based system has potential as a tool toward adaptive real-time control of subculturing, cell culture optimization and quality assurance/quality control, and it could be integrated with current and developing robotic cell cultures systems to achieve complete automation.
目前的细胞培养实践依赖于人工操作，仍然繁琐且高度主观，导致结果差异较大且不一致，尤其是在使用细胞融合度的视觉评估来确定合适的细胞传代时间时。尽管正在努力通过机器人系统实现细胞培养自动化，但大多数此类系统仍需要人工干预来确定何时进行细胞传代。因此，有必要准确、客观地确定细胞传代的合适时间。优化干细胞培养，在最大限度提高细胞产量的同时保持细胞多能性，对于高效、具有成本效益的基于干细胞的治疗方法将尤为重要。为此，我们开发了一种基于实时计算机视觉的系统，该系统可以以 0.791±0.031 的精度和 0.559±0.043 的召回率监测细胞融合度。该系统由自动相差时相差显微镜和服务器组成。多个培养皿依次成像，并将数据上传到服务器，服务器执行计算机视觉处理，预测细胞何时会超过最佳细胞融合度的预定义阈值，并提供基于 Web 的远程细胞培养监测界面。在达到此阈值之前 4 小时和达到此阈值时，系统还会通过短信和电子邮件通知人工操作员。该系统成功用于指导范式干细胞群 C2C12 细胞的扩增。计算机指导和人工指导的对照传代需要 3 个连续培养才能达到 5000 万 C2C12 细胞的理论目标细胞产量，并且在成肌和成骨分化方面没有差异。这种基于自动化视觉的系统具有作为自适应实时传代控制、细胞培养优化和质量保证/质量控制工具的潜力，并且可以与当前和正在开发的机器人细胞培养系统集成，以实现完全自动化。
