Towards Modelling Genetic Kidney Diseases with Human Pluripotent Stem Cells

Nephron. 2021;145(3):285-296. doi: 10.1159/000514018. Epub 2021 Mar 26.

Abstract

Background: Kidney disease causes major suffering and premature mortality worldwide. With no cure for kidney failure currently available, and with limited options for treatment, there is an urgent need to develop effective pharmaceutical interventions to slow or prevent kidney disease progression.

Summary: In this review, we consider the feasibility of using human pluripotent stem cell-derived kidney tissues, or organoids, to model genetic kidney disease. Notable successes have been made in modelling genetic tubular diseases (e.g., cystinosis), polycystic kidney disease, and medullary cystic kidney disease. Organoid models have also been used to test novel therapies that ameliorate aberrant cell biology. Some progress has been made in modelling congenital glomerular disease, even though glomeruli within organoids are developmentally immature. Less progress has been made in modelling structural kidney malformations, perhaps because sufficiently mature metanephric mesenchyme-derived nephrons, ureteric bud-derived branching collecting ducts, and a prominent stromal cell population are not generated together within a single protocol. Key Messages: We predict that the field will advance significantly if organoids can be generated with a full complement of cell lineages and with kidney components displaying key physiological functions, such as glomerular filtration. The future economic upscaling of reproducible organoid generation will facilitate more widespread research applications, including the potential therapeutic application of these stem cell-based technologies.

Keywords: Embryo; Gene; Glomerulus; Kidney disease; Organoid; Tubule.

Publication types

  • Review

MeSH terms

  • Genetic Predisposition to Disease
  • Humans
  • Kidney Diseases / congenital
  • Kidney Diseases / genetics*
  • Kidney Diseases / pathology
  • Pluripotent Stem Cells / metabolism*