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

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 10X 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 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 non-negative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we find that injured proximal tubule cells are characterized by increasing macrophage and lymphocyte interactions even at 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 a data visualization web application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/; login: humphreyslab_visium password: irivisium).