The orchestrated cellular and molecular responses of the kidney to endotoxin define a precise sepsis timeline.
Clinical sepsis is a highly dynamic state that progresses at variable rates and has life-threatening consequences. Staging patients along the sepsis timeline requires a thorough knowledge of the evolution of cellular and molecular events at the tissue level. Here, we investigated the kidney, an organ central to the pathophysiology of sepsis. Single cell RNA sequencing and spatial transcriptomics revealed the involvement of various cell populations in injury and repair to be temporally organized and highly orchestrated. We identified key changes in gene expression that altered cellular functions and can explain features of clinical sepsis. These changes converged towards a remarkable global cell-cell communication failure and organ shutdown at a well-defined point in the sepsis timeline. Importantly, this time point was also a transition towards the emergence of recovery pathways. This rigorous spatial and temporal definition of murine sepsis will uncover precise biomarkers and targets that can help stage and treat human sepsis. Overall design: The 129sv mouse was sacrificed 6 hours after cecal ligation puncture and tissue was placed in an OCT mold and stored at -80 C. A septic mouse kidney was immediately frozen in Optimal Cutting Temperature media (O.C.T.). A 10 µm frozen tissue section was cut and affixed to a Visium Spatial Gene Expression library preparation slide (Catalogue # 1000200, 10X Genomics, Pleasanton, California, USA). The specimen was fixed in methanol and stained with hematoxylin-eosin reagents. Images of hematoxylin-eosin-labeled tissues were collected as mosaics of 10x fields using a Keyence BZ-X810 fluorescence microscope equipped with a Nikon 10X CFI Plan Fluor objective. The tissue was then permeabilized for 12 minutes and RNA was isolated. The cDNA libraries were prepared and then sequenced on an Illumina NovaSeq 6000. Using Seurat 3.1.4, we identified anchors between the integrated single cell object and the spatial transcriptomics datasets and used those to transfer the cluster data from the single cell to the spatial transcriptomics. For each spatial transcriptomics spot, this transfer assigns a score to each single cell cluster. We selected the cluster with the highest score in each spot to represent its single cell associated cluster. Using a Loupe Browser, expression data was visualized overlying the hematoxylin-eosin image.