(PLEASE KEEP IN WORD, DO NOT PDF) Enter the text here that is the new abstract information for your application. This section must be no longer than 30 lines of text. Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We pioneered the development of novel tissue processing methods in the KPMP that uses a limited amount of human kidney biopsy tissue for single nucleus (sn) omics and spatial technologies across institutions while allowing parallel histological evaluation. Using our TISAC-approved snRNA-seq assay on reference, AKI and CKD biopsies, we generated a snRNA atlas from 200K nuclei covering cortex to papilla and revealed 53 healthy and 47 altered cell states. These represent cycling, degenerative, adaptive/maladaptive and transitioning cells in tubular and interstitial compartments. We augmented these efforts by integrating these data with newer technologies that measure RNA and epigenome in the same nucleus and defined key transcription factors associated with gene expression changes in transition of healthy cell to altered cell states. In a collaborative effort in KPMP and HuBMAP, we integrated snRNA-seq, scRNA-seq, SNARE-seq2, two different spatial transcriptomic technologies and 3D cytometry. From this we were able to define biologically meaningful cell-cell interactions, cognate molecular interactions and genes associated with adaptive states that correlate with worse CKD outcomes. Deeper sampling and integrated analyses ensuring tissue economy of AKI and CKD biopsies is still needed to overcome gaps in clinicopathological-molecular determinants, and to assess effect of race, sex, ethnicity, clinical attributes, mechanisms of gene regulation, shifts in cell states, and characterization of biologically relevant niches. To this end we will use paired snRNA-seq and snATAC-seq (chromatin accessibility) to define mechanisms associated with altered state transitions, which will enable discovery of potential causative epigenetic factors driving RNA or protein changes in disease (Aim 1). To further define the spatial contexts of injury niches for biological insights, we will evaluate and implement highly multiplexed spatial transcriptomics technologies (Aim 2) and integrate knowledge from our transcriptomic and epigenomic atlas with other orthogonal technologies in the KPMP and other consortia (Aim 3). This collective effort will enable the creation of a high resolution spatial molecular atlas of healthy and diseased kidneys that can be used as a benchmark to interrogate and interpret molecular information in a single patient’s biopsy that can inform clinical care.