Decoding phenotypes of kidney disease at the single-cell level

In this work, we leveraged integrated single-cell transcriptomic and epigenomic analyses to decode various phenotypes along the AKI-to-CKD continuum. We identified Hnf4a and Maf as drivers of successful recovery from AKI, while sustained ferroptosis and pyroptosis were implicated in maladaptive repair post-AKI. We found that profibrotic PT cells attract basophils, leading to fibro-inflammation and CKD, and that sGC modulation in PT and stroma cells offers NO-independent improvement of oxidative stress regulation in DKD. Additionally, we demonstrated that understanding the intricate temporal dynamics of kidney development can help infer causal cell types and genes from kidney function GWAS loci, informing molecular mechanisms. Finally, we highlighted how single-cell technology can identify suitable preclinical models that accurately represent human disease phenotypes, thereby overcoming translational obstacles in the search for novel kidney drug targets. Single-cell methodologies have revolutionized kidney disease research by providing unprecedented insights into the cellular and molecular mechanisms underlying disease progression. They enable the integration of vast orthogonal data layers such as transcriptomics, epigenomics, proteomics, metabolomics, etc. While offering deeper insights and more comprehensive data, these approaches also present significant computational biology challenges, necessitating advanced methods to effectively integrate and interpret these diverse datasets, ultimately aiding in the decoding of patient-relevant kidney phenotypes.