Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Institute for Vision Research, Carver College of Medicine, University of Iowa, Iowa City, IA, USA; Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, USA.
SLAS Technol. 2023 Dec;28(6):416-422. doi: 10.1016/j.slast.2023.07.004. Epub 2023 Jul 15.
Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellX robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellX system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.
人诱导多能干细胞(hiPSCs)在细胞治疗和再生医学等多种应用中显示出巨大的应用前景。临床级 hiPSC 的生产需要可重复的制造方法和严格的质量控制,例如图像控制的机器人处理系统提供的质量控制。在本文中,我们提出了一种自动化图像分析方法,用于使用 CellX 机器人细胞处理系统识别和挑选 hiPSC 集落进行克隆扩增。该方法结合了一种基于 U-Net 架构的轻量级深度学习分割方法,用于自动分割全视场(FOV)高分辨率相差图像中的 hiPSC 集落,并采用标准化方法建议挑选位置。该方法使用从 CellX 系统获得的图像和数据进行了验证,该系统中对临床级 hiPSC 进行了重新编程、克隆扩增,并分化为视网膜类器官,用于治疗遗传性视网膜退行性失明患者。