Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications.

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications.
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DOI:
10.1016/j.slast.2023.07.004
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发表时间:
2023-12
期刊:
影响因子:
2.7
通讯作者:
Tucker, Budd A.
Tucker, Budd A.
中科院分区:
医学4区
文献类型:
--
作者:
Powell, Kimerly A.;Bohrer, Laura R.;Stone, Nicholas E.;Hittle, Bradley;Anfinson, Kristin R.;Luangphakdy, Viviane;Muschler, George;Mullins, Robert F.;Stone, Edwin M.;Tucker, Budd A.

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人类诱导多能干细胞(hiPSC)已被证明在包括细胞治疗和再生医学在内的各种应用中具有巨大的前景。临床级hiPSC的生产需要具有严格质量控制的可再现制造方法,例如由图像控制的机器人处理系统提供的那些。在本文中,我们提出了一种自动图像分析方法,用于使用CellX™机器人细胞处理系统识别和挑选用于克隆扩增的hiPSC集落。该方法结合了基于U-Net架构的轻量级深度学习分割方法,以自动分割全视场(FOV)高分辨率相衬图像中的hiPSC菌落,并采用标准化方法建议拾取位置。使用从CellX™系统获得的图像和数据证明了该方法的实用性,其中临床级hiPSC被重编程、克隆扩增并分化成视网膜类器官,用于治疗患有遗传性视网膜变性失明的患者。
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.
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