Modeling the Renal Interstitium for Nephron Regeneration
The revolution in pluripotent stem cell biology, and subsequent insight regarding the differentiation of these cells into discrete tissues, has shown that self-organization during development is a biologic principle across organs. In vitro, embryonic stem cells can be coaxed to differentiate into nephron-like structures in a petri-dish - pointing towards a new strategy for generating fully differentiated nephrons ex vivo. Progress to date in generating self-organizing nephron-like structures in vitro has been limited to the epithelial compartment, including tubules and glomeruli-like structures. Ultimately, functioning nephrons will require a surrounding interstitial component, including stromal cells such as pericytes and fibroblasts, as well as pertubular capillaries. But there has been no study of regneration or modeling this compartment in vitro to date. This pilot application will address this knowledge gap with the goal of generating a renal interstitial environment in vitro. Our unique advance is that we have identified a previously unrecognized kidney resident cell type defined by expression of the transcription factor Gli1 which marks resident kidney mesenchymal stem cells (MSC). These cells are CD31-, F4/80-, CD45-, PDGFRß+, Sca1+, CD29+, CD105+, representing a consensus MSC surface profile. Moreover, they possess trilineage differentiation capacity and tightly associate with vascular endothelial cells both in vivo and in vitro. Our central hypothesis is that Gli1+ mesenchymal stem cells from kidney serve as an interstitial stem cell population. We further hypothesize that these cells will function to organize and regenerate the interstitial compartment by stabilizing endothelial tubes, giving rise to supportive pericytes and fibroblasts and interacting with epithelial basement membrane. We have designed a series of experiments to assess the ability of these stem cells to recapitulate and model the kidney interstitium in vitro, a critical step toward regenerating functional nephrons in vitro.

Defining Cellular Injury in Diabetic Nephropathy by Single Cell RNA Sequencing
Despite almost universal implementation within the last 20 years of treatments that were presumed to be reno-protective, diabetes continues to rank as the #1 cause of ESRD. Diabetic nephropathy (DN) is characterized by glomerulopathy, albuminuria and progressive tubulointerstitial fibrosis. Understanding the precise transcriptional changes that occur in single podocytes, tubular epithelium and interstitial cells during diabetic nephropathy may allow us to infer novel cell states and heterogeneity among these cells that will inform our understanding of disease pathogenesis. Single cell RNA-sequencing (scRNA-seq) has a unique advantage in characterizing cell transcriptomes because it can detect them comprehensively on a genomic scale. We have successfully implemented scRNA-seq in human kidney tissue, but we are limited by two critical hurdles. First, our dissociation protocols selectively enrich for proximal tubule epithelia but lack podocyte, fibroblast and endothelial cells, precluding their study. Second, our microfluidic-based scRNA-seq protocol requires very fresh tissue and cannot be performed on archival material. In this application we will attempt to overcome these substantial barriers by performing single nucleus RNA-seq (sNuc-seq) on archival, fresh frozen human kidney tissue of known histology and diagnosis. We believe that the nuclear isolation protocol will free all nuclei within our frozen sample, not just proximal tubule nuclei as with our single cell dissociation experience to date. We have already identified two normal and two diabetic nephropathy kidney samples in collaboration with the Boston Nephrectomy Biobank, led by Sus Waikar MD, MPH. The ability to perfrom sNuc-seq on archival material would also represent an enormous advance and open many new opportunities to study human diabetic nephropathy. We predict that measuring the gene expression repertoire of single nuclei has tremendous power to reveal stochastic gene expression and unappreciated differences in cell states during diabetic nephropathy.

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