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

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 ^TM 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 ^TM 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.
Competing Interests: Declaration of Competing Interest KAP is a paid consultant and shareholder in Cell X Technologies Inc. GM is the Chief Technology Officer and shareholder in Cell X Technologies Inc. VL is an employee and shareholder in Cell X Technologies Inc.
(Copyright © 2023. Published by Elsevier Inc.)