Abstract: SA-PO192

Whole Glomerular Transcriptome Analysis from CKD Biopsies Predicts Cell Lineage-Specific Transcripts

Session Information

  • Glomerular: Cell Biology
    November 04, 2017 | Location: Hall H, Morial Convention Center
    Abstract Time: 10:00 AM - 10:00 AM

Category: Glomerular

  • 1003 Glomerular: Cell Biology

Authors

  • Eddy, Sean, University of Michigan, Ann Arbor, Michigan, United States
  • Eichinger, Felix H., University of Michigan, Ann Arbor, Michigan, United States
  • Godfrey, Brad A., University of Michigan, Ann Arbor, Michigan, United States
  • Chen, Yu, Eli Lilly and Company, Indianapolis, Indiana, United States
  • Conway, James, MedImmune, Gaithersburg, New Jersey, United States
  • Lindenmeyer, Maja, Ludwig-Maximilians-Universität München, München, Germany
  • Cohen, Clemens D., Ludwig-Maximilians-Universität München, München, Germany
  • Badal, Shawn S., Gilead Sciences, Inc., Foster City, California, United States
  • Wesley, Johnna D, Novo Nordisk Research Center, Seattle, Seattle, Washington, United States
  • Patel, Uptal D., Gilead Sciences, Inc., Foster City, California, United States
  • Breyer, Matthew D., Eli Lilly and Company, Indianapolis, Indiana, United States
  • Nair, Viji, University of Michigan, Ann Arbor, Michigan, United States
  • Duffin, Kevin L., Eli Lilly and Company, Indianapolis, Indiana, United States
  • Magnone, Maria chiara, AstraZeneca, Mölndal, Sweden
  • Moreno Quinn, Carol Patricia, MedImmune, Gaithersburg, New Jersey, United States
  • Kretzler, Matthias, University of Michigan, Ann Arbor, Michigan, United States
  • Wei, Tao, Eli Lilly and Company, Indianapolis, Indiana, United States
  • Reznichenko, Anna, AstraZeneca, Mölndal, Sweden
  • Menon, Rajasree, University of Michigan, Ann Arbor, Michigan, United States
  • Hartman, John R, University of Michigan, Ann Arbor, Michigan, United States
  • Otto, Edgar A., University of Michigan, Ann Arbor, Michigan, United States
  • Tomilo, Mark, University of Michigan, Ann Arbor, Michigan, United States
  • Ju, Wenjun, University of Michigan, Ann Arbor, Michigan, United States
Background

Single cell transcriptome analysis yields a wealth of information on cell type specific transcripts but is not feasible on a large scale, while tissue-level transcriptomic analysis yields a wealth of information on gene regulation but lacks cell type specificity. Coupling whole tissue transcriptome analysis with single cell transcriptome analysis can yield insights into cell type markers and expression profiles.

Methods

Transcriptomic profiles of microdissected glomeruli from clinically indicated renal biopsies were generated from 170 CKD patients in the European Renal cDNA Bank cohort. Gene expression modules were generated using weighted gene co-expression network analysis. Cell lineage signatures curated from published literature were used to compare with modules.

Results

Across the glomerular transcriptome, 22 co-expression modules were identified ranging in size from 76 to 2012 genes. Limiting the analysis to the most variant transcripts yielded 16 co-expression modules ranging in size from 99 to 1046 genes. Re-sampling analysis indicated that modules were stable. From the most variant transcript analysis, eigengenes were compared to cell type specific signatures and two modules (698 genes) were strongly correlated (r>0.75, p<10E-30) with a podocyte signature generated from podocyte gene set (Ju et al., 2013). Functional enrichment of these modules identified biology associated with podocyte dysfunction, fibrosis, and glomerular toxicity. These two modules contained 62% (21/34) of the podocyte genes expressed in the variant filtered dataset including NPHS1 and WT1, and 69% (93/134) of previously predicted podocyte genes; 11-33% of the remaining genes in the podocyte signature-associated modules were expressed in podocytes from single cell RNA-seq analysis of human kidney tissue (based on different thresholding criteria for defining single cell expression).

Conclusion

This approach allows for the identification of cell-lineage specific transcripts in whole tissue transcriptome datasets and offers the opportunity to identify novel cell lineage transcripts.

Funding

  • NIDDK Support