In humans, mutations in the PKD1 or PKD2 gene account for about 93% of Autosomal Dominant Polycystic kidney disease (ADPKD) cases, although recent reports highlight that other genes, such as GANAB, DNAJB11, IFT140, and ALG9, can also cause an ADPKD phenotype. Further adding to the complexity, different types of genetic mutations are present within the PKD1 and PKD2 family and result in varying disease severity. For example, patients with truncating mutations in the PKD1 gene typically have more severe disease compared to patients with non-truncating PKD1 mutations. The combination of these factors has resulted in significant difficulties when attempting to develop therapeutic options, as evidenced by the fact that only one ADPKD drug has been discovered despite decades of research. As such, there is a significant and urgent need to develop new ADPKD-focused therapeutics. One of the major hinderances to the development of new therapeutics is the plethora of animal models that researchers use to study ADPKD. These mouse models include both orthologous (i.e. mutations in Pkd1 or Pkd2 gene) and non-orthologous (i.e. Cys1cpk, Ift88, etc) varieties as well as congenic and inducible models. The fact that these models have different genetic mutations and progress at highly variable rates causes variation in research results. For example, our data indicate that the phenotype and function of immune cells is highly variable, depending on the cystic model used. As such, there is a significant unmet need to understand how the molecular and cellular pathways found in ADPKD patients correlate to mouse models. Further, an understanding of inter- and- intra- cellular signaling networks that are present in ADPKD patients and how they correlate to mouse models is also lacking. In this application, we propose to address these major gaps by generating new single cell RNA sequencing data in the Pax8 rtTA tetocre Pkd1f/f model and merging this newly generated single cell data with our existing mouse PKD single cell atlas. We will also apply our novel deep learning model to the newly created single cell atlas. Using this atlas, we will leverage our knowledge to understand molecular signatures associated with animal models and how they relate to ADPKD patients. We will also identify inter- and- intra- cellular signaling networks that are present in ADPKD patients and investigate whether those signaling networks are conserved in mouse models. Finally, to assist the community, we will generate a user-friendly web portal using our newly generated atlas that will allow researchers to query their genes of interest in mouse models. Overall, we believe that this atlas and tool will serve as a critical resource for PKD researchers and will drive the discovery of novel therapeutics in the ADPKD field.