Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model

Significance Statement AKI is characterized by a sudden loss of kidney function often followed by the restoration of function through repair. Single-cell RNA sequencing efforts have aided in the identification of cell types and states important in injury, repair, and failed repair. These transcriptomic atlases lack spatial information, complicating interpretation of relevant cellular interactions. Therefore, we generated a spatial time course of female ischemic injury, defining temporal and location-specific gene expression patterns of injury markers. Gene expression and cell type interactions were resolved across time points using computational tools Giotto and SPOTlight. These results have been packaged into an interactive data visualization tool for gene target discovery and validation throughout the kidney and AKI research communities. Background Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. Methods We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. Results An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. Conclusions In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).