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Kidney Week

Abstract: FR-PO147

Strengths and Limitations of Integrating Single Cell and Spatial Transcriptomics to Study T Cells in Normal and AKI Kidneys

Session Information

  • AKI: Mechanisms - II
    November 04, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
    Abstract Time: 10:00 AM - 12:00 PM

Category: Acute Kidney Injury

  • 103 AKI: Mechanisms

Authors

  • Gharaie, Sepideh, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Lee, Kyungho, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Noller, Kathleen, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Lo, Emily K., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Jung, Hyun Jun, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Kurzhagen, Johanna T., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Singla, Nirmish, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Welling, Paul A., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Cahan, Patrick, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Noel, Sanjeev, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Rabb, Hamid, Johns Hopkins Medicine, Baltimore, Maryland, United States
Background

T cells are important in the pathogenesis of AKI but molecular mechanisms are poorly understood. We used single cell RNAseq as well as novel spatial transcriptomics to study T cells in normal and post AKI mouse kidneys. We also studied human kidneys and NIH KPMP data to assess clinical significance of our mouse data.

Methods

C57BL6 mice underwent bilateral ischemia reperfusion, then sacrificed at 24 hr. Kidney mononuclear cells from normal and ischemic mice isolated and sorted for CD4, CD8, and DN T cells. cDNA was synthesized and library preparation performed using 10X Chromium technology. scRNA-Seq was preprocessed on the Illumina NextSeq 500 platform. Sequencing reads were aligned to the mouse reference genome using the 10X Genomics CellRanger pipeline followed by downstream data analysis using Scanpy. Data was validated using qPCR. Spatial transcriptomics was performed using 10X visium platform. “Normal” sections of human post-ischemic kidneys removed for tumor nephrectomies were evaluated. The NIH KPMP AKI data base was studied.

Results

scRNA-seq revealed a distinct gene profile for CD4, CD8 and DN T cells of mouse kidneys. We then identified genes that were significantly up or downregulated in each population after AKI. We selected highest expressing genes in each category, focusing on the DN T cells, and confirmed the expression of those genes including Xcl1, Ly6c2, and Fcer1g using qPCR. Spatial transcriptomics in normal and AKI mouse tissue showed a localized cluster of T cells in the outer medulla expressing similar genes. However, spatial transcriptomics with the current technology did not provide resolution needed to adequately study kidney T cells. Novel T cell gene signatures that we identified, were confirmed in Human kidney AKI tissue using qPCR as well as the NIH KPMP data set.

Conclusion

Single cell RNAseq is a useful discovery technique to study T cell gene expression in normal and AKI kidneys, and can be validated in human samples. However, spatial transcriptomics at the current technology has resolution limitations to study kidney T cells, but is useful for epithelial cell, macrophage, neutrophil, cytokine and chemokine studies.

Funding

  • NIDDK Support