The application of iPSC-derived kidney organoids and genome editing in kidney disease modeling

Abstract

Introduction: Conventionally, cell cultures and animal models are employed to study disease mechanisms and treatments. However, a translational gap between preclinical research and clinical trials in patients is present. The induced pluripotent stem cell (iPSC) is a promising approach to fill this gap. Kidney organoids derived from iPSCs maintain structural and functional features of native kidneys, providing a unique experimental material to study kidney disease mechanisms. Precise genome editing in iPSCs enables advanced disease modeling, drug screens, tissue regeneration, and personalized medicine. This chapter reviews recent progress in kidney organoids, genome editing technologies, and kidney disease modeling and discusses current challenges and future perspectives. Methods: The databases Pubmed, Web of Science, and Scopus were searched for all possible combinations of selected key words, including but not limited to iPSCs, directed differentiation, direct reprogramming, kidney organoids, diseases modeling, genome editing, and CRISPR/Cas9. Results: iPSCs and iPSC-derived kidney organoids serve as a unique platform to model human renal diseases "in a dish," as exemplified by genetic conditions, infectious diseases, and tumors. Additionally, applying genome editing enables to characterize gene functions and to reveal pathogenesis and underlying disease mechanisms of prevalent as well as rare renal diseases from early onset on. Hereby, important human-typical disease features have already been successfully recapped. Furthermore, employing patient-specific iPSCs generates patient-specific disease models and therefore enables to identify individual optimal treatment strategies, which makes this technology highly relevant for personalized medicine. Conclusion: Many important findings have already been revealed by employing human iPSCs for kidney disease modeling in a human-specific manner. The wide range of possibilities offered by this approach can be even more broadened by using genome editing techniques. However, both applications still face challenges and offer a promising potential for optimization.