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Kidney Week

Abstract: SA-OR16

Comprehensive Single-Cell Transcriptomic, Epigenomic, and Metabolomic Profiling Reveals Anatomical and Metabolic Heterogeneity in Human Kidneys

Session Information

Category: Augmented Intelligence, Digital Health, and Data Science

  • 300 Augmented Intelligence, Digital Health, and Data Science

Authors

  • Li, Haikuo, Washington University in St Louis, St Louis, Missouri, United States
  • Li, Dian, Washington University in St Louis, St Louis, Missouri, United States
  • Xuanyuan, Qiao, Washington University in St Louis, St Louis, Missouri, United States
  • Ledru, Nicolas, Washington University in St Louis, St Louis, Missouri, United States
  • Wu, Haojia, Washington University in St Louis, St Louis, Missouri, United States
  • Humphreys, Benjamin D., Washington University in St Louis, St Louis, Missouri, United States
Background

The human kidney has a distinct spatial organization, with multiple anatomic structures constituted by diverse cell types and performing unique metabolic functions. We lack a large-scale spatially-resolved multimodal (transcriptome, epigenome, metabolome) single-cell atlas for the human kidney.

Methods

We developed an optimized, split-pool barcoding-based multimodal profiling method based upon SHARE-seq (concurrent scATAC/RNA-seq). We profiled 50 human kidney samples from cortex, medulla, papilla, ureter and renal artery. Computational analysis (e.g. gene regulatory network, module enrichment analysis) identified molecular signature of different anatomic regions. Mass spectrometry spatial metabolomics was used to identify region-specific metabolites at single-cell resolution. Clinical data were used to identify novel genes in kidney disease progression.

Results

We generated transcriptomes of 446,000 cells, chromatin accessibility profiles of 401,000 cells & spatially resolved metabolomes of 408,000 cells. Multiomic analysis revealed cell types in different anatomic regions are characterized by markedly different transcriptome & chromatin accessibility profiles depending on the region (e.g. cortical vs. medullary vs. papillary TAL cells). Healthy PT (proximal tubule)-to-maladaptive PT transition is accompanied by acquisition of a distinct metabolic signature (e.g. reduced fatty acid oxidation, elevated lipid accumulation). Tubular cell types had non-overlapping metabolomic profiles. We developed MALDIpy, a package for single-cell analysis of imaging mass spectrometry data.

Conclusion

Our optimized SHARE-seq achieved single-cell multiome profiling at 20-fold lower cost compared to 10X Genomics. We developed a comprehensive single-cell multiomics atlas of human kidneys covering diverse anatomic structures. Distinct signatures of cells in different regions suggest cellular plasticity and metabolic heterogeneity. Genes (e.g. PPFIBP1) correlated with disease severity were identified as potential therapeutic targets.

1/2) UMAPs for scRNA/ATAC-seq. 3) Metabolomics clustering of kidney cortex.

Funding

  • NIDDK Support