Abstract: SA-PO067
Transcriptomic Mapping of Early Cellular Responses to Renal Ischemia-Reperfusion at Single-Cell Resolution
Session Information
- AKI: Mechanisms - Primary Injury and Repair - II
November 09, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Acute Kidney Injury
- 103 AKI: Mechanisms
Authors
- Ide, Shintaro, Duke University, Durham, North Carolina, United States
- Kobayashi, Yoshihiko, Duke University, Durham, North Carolina, United States
- Strausser, Sarah A., Duke University, Durham, North Carolina, United States
- Watwe, Anisha, Duke University, Durham, North Carolina, United States
- Tata, Purushothama rao, Duke University, Durham, North Carolina, United States
- Souma, Tomokazu, Duke University, Durham, North Carolina, United States
Background
Therapeutic options for treating acute kidney injury (AKI) and the subsequent development of chronic kidney disease (CKD) are limited. The lack of a clear molecular understanding of its pathogenesis and renal reparative pathways contributes to this scarcity of targeted therapeutics. Recent technological advancements in single-cell RNA sequencing have revolutionized our understanding of complex and dynamic tissues such as the kidney. However, optimization is still required to successfully apply this technology to rodent AKI models. Understanding cellular events in AKI at single-cell resolution will guide us to develop new therapeutic strategies.
Methods
We have optimized the kidney digestion protocol to achieve high viability (>95%) and very few doublet formations to avoid flow-cytometry-based cell isolation. We used our unilateral ischemia-reperfusion injury (uIRI) mouse model, which causes severe renal atrophy at 21 days after IRI. Droplet-based single-cell RNA-seq libraries were created and sequenced from a total of 10,000 cells from both injured (IRI) and contralateral kidneys (CLK) using a Drop-Seq platform. Single-cell transcriptome profiles were clustered and annotated based on the expression patterns of known marker genes.
Results
Our tSNE analyses identified at least 25 clusters in our combined dataset of IRI and CLK. We captured podocytes in 1.97% of total cells, which is close to the published single-nucleus RNA-seq dataset (2.4%; Wu et al., JASN 2019). There was clear separation among epithelial cells between IRI and CLK kidneys. We successfully mapped the known expression pattern of epithelial injury marker genes such as Havcr1 (encoding kidney injury molecule1), Lcn2 (encoding neutrophil gelatinase-associated lipocalin), and cytokeratins. Gene ontology analyses identified unique cell-type specific signaling such as oxidative stress responses in the KIM1-expressing proximal tubular segment.
Conclusion
We have developed an optimized platform for generating and analyzing the single-cell transcriptome of mouse kidneys which underwent IRI. Future studies using this platform will inform us as to how the cell-type specific and shared gene signatures change during the course of the disease and guide us to identify novel therapeutic approaches for AKI and its transition to CKD.
Funding
- Commercial Support –