Automating Human Induced Pluripotent Stem Cell Culture and Differentiation of iPSC-Derived Retinal Pigment Epithelium for Personalized Drug Testing

自动化人类诱导性多能干细胞培养和 iPSC 衍生的视网膜色素上皮分化,用于个性化药物测试

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作者:Vincent Truong, Kevin Viken, Zhaohui Geng, Samantha Barkan, Blake Johnson, Mara C Ebeling, Sandra R Montezuma, Deborah A Ferrington, James R Dutton

Abstract

Derivation and differentiation of human induced pluripotent stem cells (hiPSCs) provide the opportunity to generate medically important cell types from individual patients and patient populations for research and the development of potential cell therapies. This technology allows disease modeling and drug screening to be carried out using diverse population cohorts and with more relevant cell phenotypes than can be accommodated using traditional immortalized cell lines. However, technical complexities in the culture and differentiation of hiPSCs, including lack of scale and standardization and prolonged experimental timelines, limit the adoption of this technology for many large-scale studies, including personalized drug screening. The entry of reproducible end-to-end automated workflows for hiPSC culture and differentiation, demonstrated on commercially available platforms, provides enhanced accessibility of this technology for both research laboratories and commercial pharmaceutical testing. Here we have utilized TECAN Fluent automated cell culture workstations to perform hiPSC culture and differentiation in a reproducible and scalable process to generate patient-derived retinal pigment epithelial cells for downstream use, including drug testing. hiPSCs derived from multiple donors with age-related macular degeneration (AMD) were introduced into our automated workflow, and cell lines were cultured and differentiated into retinal pigment epithelium (RPE). Donor hiPSC-RPE lines were subsequently entered in an automated drug testing workflow to measure mitochondrial function after exposure to "mitoactive" compounds. This work demonstrates scalable, reproducible culture and differentiation of hiPSC lines from individuals on the TECAN Fluent platform and illustrates the potential for end-to-end automation of hiPSC-based personalized drug testing.

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