Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney.

Journal Article (Journal Article)

BACKGROUND: Microscopic analysis of urine sediment is probably the most commonly used diagnostic procedure in nephrology. The urinary cells, however, have not yet undergone careful unbiased characterization. METHODS: Single-cell transcriptomic analysis was performed on 17 urine samples obtained from five subjects at two different occasions, using both spot and 24-hour urine collection. A pooled urine sample from multiple healthy individuals served as a reference control. In total 23,082 cells were analyzed. Urinary cells were compared with human kidney and human bladder datasets to understand similarities and differences among the observed cell types. RESULTS: Almost all kidney cell types can be identified in urine, such as podocyte, proximal tubule, loop of Henle, and collecting duct, in addition to macrophages, lymphocytes, and bladder cells. The urinary cell-type composition was subject specific and reasonably stable using different collection methods and over time. Urinary cells clustered with kidney and bladder cells, such as urinary podocytes with kidney podocytes, and principal cells of the kidney and urine, indicating their similarities in gene expression. CONCLUSIONS: A reference dataset for cells in human urine was generated. Single-cell transcriptomics enables detection and quantification of almost all types of cells in the kidney and urinary tract.

Full Text

Duke Authors

Cited Authors

  • Abedini, A; Zhu, YO; Chatterjee, S; Halasz, G; Devalaraja-Narashimha, K; Shrestha, R; S Balzer, M; Park, J; Zhou, T; Ma, Z; Sullivan, KM; Hu, H; Sheng, X; Liu, H; Wei, Y; Boustany-Kari, CM; Patel, U; Almaani, S; Palmer, M; Townsend, R; Blady, S; Hogan, J; Morton, L; Susztak, K; TRIDENT Study Investigators,

Published Date

  • March 2021

Published In

Volume / Issue

  • 32 / 3

Start / End Page

  • 614 - 627

PubMed ID

  • 33531352

Pubmed Central ID

  • PMC7920183

Electronic International Standard Serial Number (EISSN)

  • 1533-3450

Digital Object Identifier (DOI)

  • 10.1681/ASN.2020050757


  • eng

Conference Location

  • United States