Year: 2026
Type: preprint
Abstract: Autosomal dominant polycystic kidney disease (ADPKD) exhibits substantial interpatient variability in disease course and therapeutic response, but the cellular basis for this variability remains poorly understood. Here, we combine single-nucleus RNA sequencing of human cyst epithelia with machine learning–based histological analysis of >1,800 cysts to resolve three epithelial cyst types—proximal tubule–like, collecting duct–like, and mixed. These cyst types display distinct injury states, metabolic programs, and stromal microenvironments, including a mixed-cyst niche enriched for CCL2-associated inflammatory signaling. Expression of key therapeutic targets was highly cell-type specific with CFTR enriched in proximal-like epithelia, whereas AVPR2 expression was confined to AQP2-positive collecting duct–like cells. Cyst-type composition varied widely across patients and in an orthologous mouse model ( Pkd1 RC/RC ) in which the burden of AQP2-positive cysts correlated with responsiveness to tolvaptan. These findings identify cyst-type heterogeneity as a major determinant of molecular pathway activation and predictability of therapeutic response in ADPKD.
Authors: Johannes Roeles, Axel Giesler, Jessica Schmitz, Johannes König, Frank Friedersdorff, Jonas Busch, Robert Greite, Matteus Krappitz, Isabel A. Layne, Michael Köttgen, E. Wolfgang Kuehn, Jessica Schmid, Katharina Schmidt, Konrad Hoeft, Rafael Kramann, Kai-Uwe Eckardt, Christine Kocks, Jan H. Bräsen, Jan Halbritter, Roland Schmitt, Silke Härteis, Katharina Hopp, Kai M. Schmidt-Ott, Christian Hinze
Institutions: Medizinische Hochschule Hannover, Evangelisches Krankenhaus Königin Elisabeth Herzberge, Charité – Universitätsmedizin Berlin, Vivantes Klinikum, University of Colorado Anschutz, University of Freiburg, University of Regensburg, University Hospital Regensburg, RWTH Aachen University, Max Delbrück Center, University Hospital Schleswig-Holstein
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: G1062240321, G2509783464, G2920955566
OpenAlex ID: W7161068188