Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney.
RNA sequencing
diabetic kidney disease
single-cell transcriptomics
urine
Journal
Journal of the American Society of Nephrology : JASN
ISSN: 1533-3450
Titre abrégé: J Am Soc Nephrol
Pays: United States
ID NLM: 9013836
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
29
05
2020
accepted:
24
11
2020
pubmed:
4
2
2021
medline:
22
9
2021
entrez:
3
2
2021
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 33531352
pii: 00001751-202103000-00013
doi: 10.1681/ASN.2020050757
pmc: PMC7920183
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
614-627Subventions
Organisme : NIDDK NIH HHS
ID : P30 DK019525
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK076077
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK087635
Pays : United States
Organisme : NIDDK NIH HHS
ID : R01 DK105821
Pays : United States
Investigateurs
Katalin Susztak
(K)
Raymond Townsend
(R)
Shira Blady
(S)
Matthew Palmer
(M)
Carine Boustany
(C)
Richard Urquhart
(R)
Paolo Guarnieri
(P)
Lea Sarov-Blat
(L)
Erding Hu
(E)
Lori Morton
(L)
Kishor Devalaraja
(K)
Uptal Patel
(U)
Shawn Badal
(S)
John Liles
(J)
Jonathan Rosen
(J)
Anil Karihaloo
(A)
Randy Luciano
(R)
Jonathan Hogan
(J)
Amy Mottl
(A)
Shweta Bansal
(S)
Salem Almaani
(S)
Christos Argyropoulos
(C)
Kirk Campbell
(K)
Tamara Isakova
(T)
Oliver Lenz
(O)
Harold Szerlip
(H)
Matthias Kretzler
(M)
Pietro Canetta
(P)
Jeffery Schelling
(J)
Rupali Avasare
(R)
Frank Brosius
(F)
Michael Ross
(M)
Nelson Kopyt
(N)
James Tumlin
(J)
Julia Scialla
(J)
Richard Lafayette
(R)
Manisha Singh
(M)
Yan Zhong
(Y)
Informations de copyright
Copyright © 2021 by the American Society of Nephrology.
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