Abstract: TH-PO022
Multimodal Spatial Transcriptomic Characterization of Mouse Kidney Injury and Repair
Session Information
- Augmented Intelligence for Prediction and Image Analysis
October 24, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Augmented Intelligence, Digital Health, and Data Science
- 300 Augmented Intelligence, Digital Health, and Data Science
Authors
- Xuanyuan, Qiao, Washington University in St Louis, St Louis, Missouri, United States
- Wu, Haojia, Washington University in St Louis, St Louis, Missouri, United States
- Kirita, Yuhei, Washington University in St Louis, St Louis, Missouri, United States
- Muto, Yoshiharu, Washington University in St Louis, St Louis, Missouri, United States
- Isnard, Pierre, Washington University in St Louis, St Louis, Missouri, United States
- Humphreys, Benjamin D., Washington University in St Louis, St Louis, Missouri, United States
Background
Repair after acute kidney injury (AKI) involves complex interactions among epithelial, stromal, and immune cells, with incomplete recovery potentially leading to fibrosis and chronic kidney disease (CKD). Traditional single-cell sequencing technologies fail to capture the spatial context of these interactions. This study leverages high-resolution in situ sequencing (Xenium) with whole-transcriptome spatial transcriptomics (Visium) to elucidate cellular networks across the full murine kidney injury and repair timecourse.
Methods
We collected mouse kidneys at multiple stages post bilateral ischemia-reperfusion injury (sham, 4 h, 12 h, 2 d, 14 d, and 42 d). We performed analyses using Xenium (300 gene panel) and Visium on two serial sections of formalin-fixed, paraffin-embedded tissue from each time point. Data integration from both platforms mapped the spatial and temporal gene expression across the timeline.
Results
Aligning Xenium and Visium datasets revealed a strong correlation for transcript detection. Xenium analysis identified a total of 1,374,915 cells with an average detection of 123 ± 97 transcripts per cell. Unsupervised clustering resolved twenty distinct cell types and disease states. This single-cell resolution spatial data allowed us to spatially map disease cell states to different regions in the kidney throughout injury and repair. Importantly, we identified the formation of a fibrotic neighborhood dominated by failed repair proximal tubule (FR-PT) cells, immune cells, and fibroblasts during the repair phase through cell neighborhood analysis. Additionally, ligand-receptor interactions enabled the identification of FR-PT expression of Il34 (ligand) recruiting Csfr1+ (receptor) immune cells. Finally, integration with Visium data validated the co-expression of ligand-receptor pairs and elucidated potential mechanisms underlying the transition from AKI to CKD.
Conclusion
This study demonstrates the utility of combining high-resolution in situ sequencing with spatial transcriptomics to explore the cellular landscape during renal injury and repair.
A) Spatial cell type map in Xenium and Visium. B) Il34 (ligand) and Csf1r (receptor) expression.