Transcriptomic, epigenomic, and spatial metabolomic cell profiling redefines regional human kidney anatomy

Haikuo Li, Dian Li, Nicolas Ledru, Qiao Xuanyuan, Haojia Wu, Amish Asthana, Lori N. Byers, Stefan G. Tullius, Giuseppe Orlando, Sushrut S. Waikar, Benjamin D. Humphreys

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A large-scale multimodal atlas that includes major kidney regions is lacking. Here, we employed simultaneous high-throughput single-cell ATAC/RNA sequencing (SHARE-seq) and spatially resolved metabolomics to profile 54 human samples from distinct kidney anatomical regions. We generated transcriptomes of 446,267 cells and chromatin accessibility profiles of 401,875 cells and developed a package to analyze 408,218 spatially resolved metabolomes. We find that the same cell type, including thin limb, thick ascending limb loop of Henle and principal cells, display distinct transcriptomic, chromatin accessibility, and metabolomic signatures, depending on anatomic location. Surveying metabolism-associated gene profiles revealed non-overlapping metabolic signatures between nephron segments and dysregulated lipid metabolism in diseased proximal tubule (PT) cells. Integrating multimodal omics with clinical data identified PLEKHA1 as a disease marker, and its in vitro knockdown increased gene expression in PT differentiation, suggesting possible pathogenic roles. This study highlights previously underrepresented cellular heterogeneity underlying the human kidney anatomy.

Original languageEnglish
Pages (from-to)1105-1125.e10
JournalCell metabolism
Issue number5
StatePublished - May 7 2024


  • SHARE-seq
  • acute kidney injury
  • anatomy
  • chronic kidney disease
  • lipid metabolism
  • metabolism
  • multiomics
  • single-cell combinatorial indexing
  • spatial metabolomics


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