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Abstract: FR-PO150

Deconvolving Bulk Disease Expression Datasets Using Markers Identified from Single-Cell Sequencing Data Defines CKD Associated Cell Types

Session Information

Category: CKD (Non-Dialysis)

  • 1903 CKD (Non-Dialysis): Mechanisms

Authors

  • Sealfon, Rachel S., Flatiron Institute, New York, New York, United States
  • Menon, Rajasree, University of Michigan, Ann Arbor, Michigan, United States
  • Azeloglu, Evren U., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Otto, Edgar A., University of Michigan, Ann Arbor, Michigan, United States
  • Hoover, Paul J., Broad Institute, Cambridge, Massachusetts, United States
  • Troyanskaya, Olga, Princeton University, Princeton, New Jersey, United States
  • Kretzler, Matthias, U.Michigan, Ann Arbor, Michigan, United States
Background

Elucidating changes in cell type composition across diseases can provide insight into pathology-related alterations in kidney tissue. Single-cell data from tumor nephrectomy tissue allows the identification of cell type markers in the adult human kidney. By integrating this information with bulk expression data from 223 biopsies from the European Renal cDNA Bank cohort (ERCB), we identify cell type proportion alterations across 9 diverse glomerular diseases.

Methods

We first identify cell type specific genes in an unbiased, data-driven manner from eight adult kidney single cell 10X Genomics expression datasets from tumor nephrectomy tissue. We extract markers from the single-cell expression data using the Seurat R package (Butler et al., 2018). We then expand these cell type specific marker sets by identifying additional cell type specific marker genes using computational nanodissection (Ju et al., 2013) and perform pathway enrichment analysis. Using our expanded set of marker genes, we deconvolve microdissected glomerular bulk expression data from the ERCB cohort using CellCODE (Chikina et al., 2015) to identify differences in cell type composition across patients with diverse kidney diseases.

Results

Both the cell type specific marker genes identified from single-cell data and the additional cell type marker genes identified by nanodissection show relevant functional enrichments. Consistent with prior knowledge, we find that podocyte markers are lost in diseased patients compared to living donors, monocyte markers are elevated in immune-mediated diseases such as lupus nephritis relative to other kidney diseases, and mesangial/vascular smooth muscle cell markers are elevated in diabetic nephropathy. Multiple cell type markers are correlated with GFR, with podocyte markers positively correlated and parietal epithelial cell markers negatively correlated

Conclusion

Integrating single-cell and bulk expression data provides insight into cell composition changes across diverse kidney diseases. Our approach illustrates the power of leveraging and integrating multimodal data types to understand the molecular underpinnings of disease processes.

Funding

  • NIDDK Support