A screen for transcription factors that can promote transdifferentiation of stroma into epithelia
Diabetic nephropathy (DN) is the leading cause of end stage renal disease in the United States. DN is characterized by fibrosis within the glomeruli and tubular interstitium. Although the precise causes of DN are not well understood, the ultimate effect is the loss of functional nephrons eventually leading to renal failure. Outside of organ transplant and dialysis, there are no treatments for this disease. Unfortunately, there is a gross shortage of kidneys available for transplant and dialysis has limited effectiveness and has a significant negative effect on quality of life. New therapies for renal replacement are needed. In this study, we propose performing a screen to identify transcription factors that are sufficient to re-program fibroblasts into nephron progenitors. The screen will initially be carried out by infecting stromal cell lines with pools of lenti-viruses expressing 18 transcription factors that are specifically expressed in the nephron progenitors and/or required for their development. By fractionating the pools, we will identify the minimum number of factors that gives the strongest response. Once we have identified an active set of factors, we will test their ability to transform stromal progenitors in cultured embryonic kidneys. Ultimately, we hope to test the efficacy of this active pool in mouse models of DN. Our ultimate goal is to identify factors that are sufficient to convert fibroblasts into functional nephrons in the context of the diseased kidney. This technology will serve to alleviate the gross shortage and/or inadequacy of current renal replacement therapies and could represent the earliest phase of a new type of therapy for chronic kidney disease.

Spatial Representation of Single Cell RNA-sequencing Data in Embryonic Kidneys
Single cell RNA sequencing datasets have become ubiquitous. Although these datasets contain an enormous amount of data that can be used for hypothesis generation, the data is frequently provided in the form of tSNE plots or Excel datasheets that have limited utility. Neither of these methods provide significant spatial information. We have adapted a technique that allows us to present single cell data in a manner that includes spatial information. This technique requires in situ hybridization of a relatively small number of cell type specific genes to function as landmarks. However, these landmark genes must be hybridized to the same or adjacent tissue sections to allow image registration. This cannot be accomplished using current techniques. Here we propose to establish a protocol for multiplexing mRNA expression (so-called in situ single cell RNA-Seq) allowing us to visualize up to 1024 genes simultaneously on a single tissue section. By carefully selecting a small number of genes that represent a single cell sequencing cluster, we can the map the entire transcriptome from one or more single cell datasets onto a reference image of a kidney thus giving spatial resolution for all expressed genes. Ultimately, this data will be provided as a searchable website that will allow users to make multiple queries including simple gene expression data (virtual in situ hybridization), spatial readout of signaling pathway or transcription factor activity and spatial relationship of ligands and receptors. We feel development of this technique will increase access to single cell RNA-seq data and the facilitation of hypothesis generation and thus accelerate discovery and potential cures for kidney disease.

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