The prevalence of kidney disease in the United States is ∼14%, with more than 600,000 patients with kidney failure. Despite the large and growing need for new therapies to treat kidney disease, few have been developed over the past two decades. The kidney’s cellular complexity is partly to blame—its functional unit, the nephron, is composed of at least 13 different epithelial cell types, surrounded by an even larger array of supporting vascular, stromal, and immune cells. In disease states, various invading leukocytes and reactive parenchymal cell states further complicate the cellular landscape, making attempts to understand pathophysiology and identify therapeutic targets difficult. However, the advent of massively parallel single-cell RNA sequencing (scRNA-seq) is transforming our understanding of cellular diversity, offering an unbiased approach to profile not only complex tissues but entire organisms.
UPENN researchers apply scRNA-seq to develop the first comprehensive gene expression atlas of the mouse kidney, use innovative analyses to transcriptionally characterize known cell types, and identify a new progenitor cell type. They also map the expression of monogenic and complex trait disease-associated genes to specific kidney cell types, providing new insight about cell types driving many kidney diseases.
The researchers generated nearly 60,000 single-cell transcriptomes from adult mouse kidney and resolved them into 21 cell types based on unique transcriptional profiles. These include expected cell types—nine epithelial, three endothelial, one fibroblast, and five immune cell types—as well as three previously undescribed cell types. The authors mapped expression of human monogenic kidney disease genes onto cell types from their mouse data set. Proteinuria is a common manifestation of kidney disease, for example, and nearly all genes associated with inherited proteinuria were expressed only in podocytes. This is an epithelial cell type located in the glomerulus that helps to form the filtration barrier. Similarly, nearly all monogenic genes associated with blood pressure map to the distal parts of the nephron—segments that fine-tune sodium balance in the blood. These results validate scRNA-seq as a method to infer the cellular drivers of disease.
(A) Unsupervised clustering demonstrates 16 distinct cell types shown in a t-distributed stochastic neighbor embedding (tSNE) map (center). Left panels are subclusters of clusters 1, 3, and 7. Percentages of assigned cell types are summarized in the right panel. Endo, containing endothelial, vascular, and descending loop of Henle; Podo, podocyte; PT, proximal tubule; LOH, ascending loop of Henle; DCT, distal convoluted tubule; CD-PC, collecting duct principal cell; CD-IC, collecting duct intercalated cell; CD-Trans, collecting duct transitional cell; Fib, fibroblast; Macro, macrophage; Neutro, neutrophil; lymph, lymphocyte; NK, natural killer cell. (B and C) Violin plots showing the expression levels of representative marker genes across the 16 main clusters. The y axis shows the log-scale normalized read count. (C) Cluster 1 [from (A), left] separates into endothelial cells (Endo), pericytes and vascular smooth muscle cells (Peri), and descending loop of Henle (DLH) cells. Cluster 3 (proximal tubules) separates into S1, S2, and S3 segments or proximal convoluted tubules (PCT) and proximal straight tubules (PST). In cluster 7, intercalated cells (ICs) separate into types A and B.
(read more at www.sciencemag.org…)