Abstract: TH-OR128

Single Cell Transcriptome Atlas of the Mouse Kidney Reveals Important Cell Diversity

Session Information

Category: Diabetes

  • 501 Diabetes Mellitus and Obesity: Basic - Experimental

Authors

  • Park, Jihwan, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Shrestha, Rojesh, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Qiu, Chengxiang, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Kondo, Ayano, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Li, Szu-Yuan, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Susztak, Katalin, University of Pennsylvania, Philadelphia, Pennsylvania, United States
Background

A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve precise cellular states. These methods also provide a stunningly high-resolution view of transitions between states. Single-cell transcriptomics will allow us to identify cell type specific expression changes, discover novel disease associated cell types and trace cell composition changes in complex disease such as diabetic kidney disease (DKD).

Methods

Using droplet-based single-cell barcoding and sequencing methods we cataloged mouse kidney cell types in an unbiased manner. We have developed a novel cell isolation method and individually profiled over 30,000 cells from the kidneys. Computational analysis included normalization, quality control, dimension reduction and clustering followed by identification of cell types using known markers and bulk RNAsequencing data of kidney segments.

Results

Main clustering analysis identified 15 major cell populations in normal mouse kidneys; three distinct ureteric bud- and 7 metanephric mesenchyme-derived epithelial clusters, in addition to endothelial cells, fibroblasts, different immune cell types and two novel epithelial cell populations that have not been described before. Cell trajectory analysis highlighted cell type conversion in the collecting duct and discovered novel transient cell type. Furthermore we have developed methods for single cell marker based in silico deconvolution of bulk RNAsequencing datasets. We found that most transcript level changes previously reported in kidney disease are resulted from cell type proportion changes in kidney samples, such as accumulation of immune cells and fibroblasts and decrease of tubule epithelial cells in disease development. Finally, we identified key transcription factors, mapped GWAS candidates, known drug targets, and nephrotic syndrome genes in the clusters showing their cell type-specific expression patterns.

Conclusion

In conclusion, our first single cell transcriptome of the entire mouse kidney could have a transformative impact to understand transcriptional networks maintaining cell identity and development of DKD.

Funding

  • NIDDK Support