Urine Proteomics Captures Unique Spatial Transcriptomic Signatures of ADPKD in a Mouse Model

Background: ADPKD is the most prevalent inherited kidney disease. Although the impairment of primary cilia is thought to be the cause of cyst formation, an insufficient understanding of the molecular mechanism has impeded the discovery of biomarkers reflecting PKD-specific pathology. Here, we performed multi-omics analysis to discover potential biomarkers for PKD. Methods: Spatial transcriptomics, kidney proteomics, and urine proteomics were conducted on normal and PKD model mice. For spatial transcriptomics, we applied unsupervised clustering, spatial segmentation by deep learning, and differentially expressing gene (DEG) analysis. Kidney and urine proteomics were subjected to DEG and gene ontology (GO) analysis. Results: Spatial transcriptomics revealed that the extent of transcriptomic change from normal mice to PKD mice varies depending on the kidney region, with the cortex along with the OSOM showing the most prominent changes. By stratifying the transcriptome with spatial information, we performed DEG analysis on the peri-cystic areas of each kidney region. Then, we investigated which DEG features could be captured with kidney or urine proteomics, but we found that there was little overlap between DEGs in kidney and urine proteomics. This suggests that these two types of proteomics capture different aspects of kidney pathology in PKD. In GO analysis, the common DEGs between kidney proteome and spatial transcriptome were associated with fibrosis, implying that kidney biopsy is likely to detect fibrotic status, a general signature of kidney inflammation. In contrast, the common DEGs between urine proteome and spatial transcriptome were associated with epithelium, suggesting that urine proteomics can effectively capture abnormalities in the tubular epithelium of PKD. Conclusion: Our study highlights the potential of urine proteomics in terms of not only its low invasiveness but also its effectiveness in detecting epithelial changes, which could lead to the identification of novel biomarkers reflecting the unique pathology of ADPKD.