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Abstract: TH-OR53

Single-Cell Profiling Reveals Sex-Based Transcriptional Programs in Healthy Human Kidney

Session Information

Category: Transplantation

  • 1901 Transplantation: Basic

Authors

  • McEvoy, Caitriona M., University Health Network, Toronto, Ontario, Canada
  • Murphy, Julia M., University Health Network, Toronto, Ontario, Canada
  • Zhang, Lin, Vector Institute, Toronto, Ontario, Canada
  • Mathews, Jessica A., University Health Network, Toronto, Ontario, Canada
  • Clotet Freixas, Sergi, University Health Network, Toronto, Ontario, Canada
  • An, James, University Health Network, Toronto, Ontario, Canada
  • Karimzadeh, Mehran, Vector Institute, Toronto, Ontario, Canada
  • Pouyabahar, Delaram, University of Toronto, Toronto, Ontario, Canada
  • Su, Shenghui, University Health Network, Toronto, Ontario, Canada
  • Wang, Bo, University of Toronto, Toronto, Ontario, Canada
  • Bader, Gary, University of Toronto, Toronto, Ontario, Canada
  • Crome, Sarah Q., University Health Network, Toronto, Ontario, Canada
  • Konvalinka, Ana, University Health Network, Toronto, Ontario, Canada
Background

Single-cell transcriptomics provide unprecedented insight into disease states in the kidney, yet our understanding of the transcriptional programs of human kidney cells at homeostasis is limited by difficulty accessing healthy, fresh tissue. Sex-based dichotomy in human kidney cells remains unaddressed, but may underpin acute and chronic kidney diseases e.g. progressive diabetic kidney disease and IRI which exhibit a male preponderance.

Methods

We sequenced single-cell suspensions of 19 pre-implantation living donor biopsies (9 male,10 female)(10X Genomics). Analyses were performed with Cellranger and Seurat in R. Sex-based transcriptomic differences were examined using varimax-rotated principal component analysis, machine learning approaches and differential expression analysis.

Results

27677 high-quality cells forming 23 clusters were identified with several immune populations and all anticipated parenchymal populations. Individual kidney populations were examined for separation due to donor sex, with clear separation observed for the PT population alone using varimax-rotated principal component analysis (Fig1a). Machine learning identified the most discriminant subset of genes (Model1: 80 genes) that could correctly classify cell sex (AUC 0.98). 75 genes were differentially expressed between males and females (p-value <0.05, LogFC>0.25). Anti-oxidant metallothionein genes were increased in females. Pathway analysis revealed metabolism-related processes (oxidative phosphorylation, and the TCA cycle) as increased in males (Fig1B).

Conclusion

We report striking sex-based transcriptional differences in PT cells, suggesting higher baseline metabolic activity in males, and increased anti-oxidant metallothionein genes in females.These sex-based differences in PT gene expression may provide insights into the well-recognized, but previously unexplained sexual dimorphism observed in kidney diseases.