Abstract
Transplantation of retinal pigment epithelial (RPE) sheets derived from human induced pluripotent cells (hiPSC) is a promising cell therapy for RPE degeneration, such as in age-related macular degeneration. Current RPE replacement therapies, however, face major challenges. They require a tedious manual process of selecting differentiated RPE from hiPSC-derived cells, and despite wide variation in quality of RPE sheets, there exists no efficient process for distinguishing functional RPE sheets from those unsuitable for transplantation. To overcome these issues, we developed methods for the generation of RPE sheets from hiPSC, and image-based evaluation. We found that stepwise treatment with six signaling pathway inhibitors along with nicotinamide increased RPE differentiation efficiency (RPE6iN), enabling the RPE sheet generation at high purity without manual selection. Machine learning models were developed based on cellular morphological features of F-actin-labeled RPE images for predicting transepithelial electrical resistance values, an indicator of RPE sheet function. Our model was effective at identifying low-quality RPE sheets for elimination, even when using label-free images. The RPE6iN-based RPE sheet generation combined with the non-destructive image-based prediction offers a comprehensive new solution for the large-scale production of pure RPE sheets with lot-to-lot variations and should facilitate the further development of RPE replacement therapies.
Highlights
Transplantation of retinal pigment epithelial (RPE) sheets derived from human induced pluripotent cells is a promising cell therapy for RPE degeneration, such as in age-related macular degeneration
The lectin rBC2LCN binds to unique glycans on the cell surface of undifferentiated human embryonic stem cells (hESC) and hiPSC25, and staining with rBC2LCN-rhodamine revealed that all colonies were positive for rBC2LCN (Fig. S1D: 1383D6)
Two lines 1383D2 and A18945 expressed OCT4 (Fig. S1E,F). These results indicate that the human induced pluripotent stem cells (hiPSC) used in this research maintained a pluripotent state under our feeder-free condition
Summary
Transplantation of retinal pigment epithelial (RPE) sheets derived from human induced pluripotent cells (hiPSC) is a promising cell therapy for RPE degeneration, such as in age-related macular degeneration. Current RPE replacement therapies, face major challenges They require a tedious manual process of selecting differentiated RPE from hiPSC-derived cells, and despite wide variation in quality of RPE sheets, there exists no efficient process for distinguishing functional RPE sheets from those unsuitable for transplantation. To overcome these issues, we developed methods for the generation of RPE sheets from hiPSC, and image-based evaluation. Transplantation of RPE suspension or RPE sheets derived from hPSC for age-related macular degeneration (AMD) and Stargardt disease patients is considered safe and potentially effective[2,3,4,5] This treatment faces challenges regarding the purity and quality control of RPE products for transplantation therapy. While we have previously established machine learning models to estimate the quality of other cell p roducts[21,22], a non-destructive and quantitative prediction method based on morphological features of RPE cells has been lacking
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
