Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease
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Abstract
Touring the kidney, cell by cell: Our kidneys play a critical role in keeping us healthy, a fact of which we are reminded several times each day. This organ's cellular complexity has hindered progress in understanding the mechanisms underlying chronic kidney disease, which affects 10% of the world's population. Using single-cell transcriptional profiling, Park et al. produced a comprehensive cell atlas of the healthy mouse kidney (see the Perspective by Humphreys). An unexpected cell type in the collecting duct appears to be a transitional state between two known cell types. The transition from one cell type to the other is regulated by the Notch signaling pathway and is associated with metabolic acidosis. The authors also find that genetically distinct kidney diseases with common clinical features share common cellular origins. Science , this issue p. 758 ; see also p. 709
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Manual validation of cell type annotation was performed and was consistent with previous studies.
“After classification, we performed a manual validation of cell type annotation consistent with previous studies (Supplemental Methods; Park et al. 2018; The Tabula Muris Consortium 2018; Angelidis et al. 2019).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Park et al. 2018
The recovered cell-type proportions differ from the Tabula Muris but are not outside expected ranges based on previous comparisons between scRNA-seq data sets.
“The cell-type proportions that we recovered differ from the Tabula Muris (e.g., we recovered comparatively more immune cells in the kidney and lung), but are not outside expected ranges based on previous comparisons between scRNA-seq data sets (Park et al. 2018; The Tabula Muris Consortium 2018).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Park et al. 2018
In “Single cell profiling and unbiased clustering of mouse kidney cells” (page unavailable), Park et al. report that analyzing fewer cells increases uncertainty and can lose rare cell types. Our copy does not compare cell-type proportions across scRNA-seq datasets or establish an expected range for differences from Tabula Muris (searched: proportions, cross-dataset comparisons, expected ranges). (LLM, conf=0.76)
Experimental — accuracy not yet measuredDock4 is expressed in mouse kidney proximal tubule cells and distal convoluted tubule cells.
“DOCK4 is expressed in kidney in GTEx v8, and Dock4 is expressed in mouse kidney proximal tubule cells and distal convoluted tubule cells.”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [53]
The ‘Classification of kidney cells based on cell-type specific marker genes’ section identifies proximal tubule and distal convoluted tubule cells, but does not mention Dock4. Searches for Dock4 and its expression in those cell types found no passage in our copy establishing the claimed expression pattern. The GTEx statement is not the part attributed to this source. (LLM, conf=0.72)
Experimental — accuracy not yet measuredDOCK4 is expressed in kidney in GTEx v8.
“DOCK4 is expressed in kidney in GTEx v8, and Dock4 is expressed in mouse kidney proximal tubule cells and distal convoluted tubule cells.”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [53]
The passages on kidney cell-type expression (Classification of kidney cells and Mendelian disease genes show cell-type specificity; page unavailable) do not mention DOCK4 or GTEx v8. Searches for DOCK4, GTEx v8, and the specified kidney cell types found no passage addressing DOCK4 expression in human kidney in GTEx v8. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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“…clear evidence of expressing Dock4) and distal convoluted tubule cells (DCT) (27 DCT cells of 8544 assayed have clear evidence of expressing Dock4) (Park et al. 2018). c Mouse brain single-cell data show that Dock4 expression is reduced in microglia when mice have a neuroinflammatory response…”
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- Authors
- Park, J., Shrestha, R., Qiu, C., Kondo, A., Huang, S., Werth, M., Li, M., Barasch, J., & Suszták, K.
- Affiliations
- University of Pennsylvania (US)
- Columbia University (US)
- Corresponding author
- Suszták, Katalin
- ORCID
- Venue
- Science
- Published
- 2018-05-18
- Volume
- 360
- Issue
- 6390
- Pages
- 758-763
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6188645
- OpenAlex
- W2795687816
- Cited by
- 1,065
- References
- 5
- Citations by year
- 2026: 69 · 2025: 88 · 2024: 150 · 2023: 147 · 2022: 125 · 2021: 177 · 2020: 145 · 2019: 103 · 2018: 57 · 2017: 1
- FWCI
- 42.87
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (DP3 DK108220, DK105821)
- American Diabetes Association (1-17-PDF-036)
- National Institute of Diabetes and Digestive and Kidney Diseases (DK076077, DK087635)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Renal and related cancers
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- kidney cell types, kidney disease pathogenesis, collecting duct, intercalated cells, principal cells, Notch signaling, cell fate transitions, metabolic acidosis, hereditary kidney disease, lineage tracing, cell trajectory analysis, mouse kidney
- MeSH terms
- Cell Plasticity, Animals, Genetic Markers, Humans, Kidney Diseases, Kidney Tubules, Collecting, Signal Transduction, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Receptors, Notch, Cell Tracking, Single-Cell Analysis
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