Maturation of Nephrons by Implanting hPSC-Derived Kidney Progenitors Under Kidney Capsules of Unilaterally Nephrectomized Mice

Abstract Background Human pluripotent stem cell (hPSCs)-derived kidney organoids may contribute to disease modeling and generation of kidney replacement tissues. However, realization of such applications requires the induction of hPSCs into functional mature organoids. One of the key questions for this process is whether a specific vascular system exists for nephrogenesis. Our previous study showed that implantation of hPSC-derived organoids below the kidney capsules of unilaterally nephrectomized immunodeficient mice for a short-term (2 weeks) resulted in the enlargement of organoids and production of vascular cells, although signs of maturation were lacking. Methods In this study, organoids are induced in vitro during 15 days and then sub-capsularly grafted into kidneys, we used the same unilaterally nephrectomized immunodeficient mice model to examine whether a medium -term (4 weeks) implantation could improve organoid maturation and vascularization, as evaluated by immunofluorescence and transmission electron microscopy(TEM). Results We demonstrate that after 2–4 weeks implantation, implanted renal organoids can form host-derived vascularization and mature in the absence of any exogenous vascular endothelial growth factor. Glomerular filtration barrier maturation was evidenced by glomerular basement membrane deposition, perforated glomerular endothelial cell development, as well as apical to basal podocyte polarization. A polarized monolayer epithelium and extensive brush border were also observed for tubular epithelial cells. Conclusions Our results indicate that the in vivo microenvironment is important for the maturation of human kidney organoids. Stromal expansion and a reduction of nephron structures were observed following longer-term (12 weeks) implantation,suggesting effects on off-target cells during the induction process. Accordingly, induction efficiency and transplantation models should be improved in the future.