The function of the kidney depends on the coordinated action of multiple specialized cell types organized in a particular spatial arrangement. Progress in understanding disease pathogenesis and in developing biomarkers and therapies for kidney disease has been hindered by a lack of deep molecular phenotyping. Tissue interrogation techniques of kidney biopsies have not changed in several decades. To advance our understanding of kidney development and disease, there is a need to develop tools to define cell-type specific molecular properties in normal kidney tissue, in response to injury, and during repair and regeneration. While progress has been made in single cell transcriptomic analysis of kidney tissue, applying new genomic technologies to define the epigenome has lagged behind. This gap in knowledge is crucial because most disease variants associated with CKD map to distal regulatory elements, which are often cell-type specific genomic enhancers. Determining the function of these regulatory regions and their effect on gene expression is limited by lack of detailed information on the chromatin state of cells in specific nephron segments in normal and diseased human kidney tissue. Chromatin Immunoprecipitation with sequencing (ChIP-seq), a standard method for mapping epigenetic modifications, requires significantly more starting material than is available in a kidney biopsy. Moreover, the method has high background and lacks sensitivity to be readily used for chromatin profiling of single cells. To address these challenges, this application proposes to develop novel cutting edge technological and bioinformatic tools that have not yet been deployed in the kidney, which is the major objective of this R21 funding opportunity. We will establish a protocol for single cell chromatin profiling using a recently described approach, Cleavage Under Targets and Tagmentation (CUT & Tag). We will develop a bioinformatic pipeline to analyze single cell histone modifications, define how these epigenomic features change in specific cell types in disease states, and integrate these findings with single cell transcriptomic and ATAC-seq data. These technological innovations will have the potential to catalyze new mechanistic studies in the kidney, lead to discovery of novel therapeutic targets and identify biomarkers to predict disease progression. The technical and bioinformatic tools we develop will be broadly applicable to investigate kidney diseases in human samples, as well as disease models in mice and human organoids. Integration of genome-wide epigenetic data generated with these tools with gene expression atlases being developed for mouse and human kidneys will be extremely valuable to the research community. Because many genetic variants associated with kidney disease are localized to distal regulatory elements, the combination of transcriptomic and epigenomic data will significantly expand the power of these data sets to generate hypotheses about diseas...