Comprehensive single cell RNAseq analysis of the kidney reveals novel cell types and unexpected cell plasticity
We characterized 57,979 cells from healthy mouse kidneys using unbiased single-cell RNA sequencing. We show that genetic mutations that present with similar phenotypes mostly affect genes that are expressed in a single unique differentiated cell type. On the other hand, we found unexpected cell plasticity of epithelial cells in the final segment of the kidney (collecting duct) that is responsible for final composition of the urine. Using computational cell trajectory analysis and in vivo linage...
Provenance — who produced it, who reused it
Linked to 19 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Bulk tissue cell type deconvolution with multi-subject single-ce... 2019 · 1,006 cites
- A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility 2018 · 889 cites
- Trans-ethnic association study of blood pressure determinants in... 2018 · 500 cites
- Spatiotemporal immune zonation of the human kidney 2019 · 455 cites
- A single-nucleus RNA-sequencing pipeline to decipher the molecul... 2019 · 300 cites
- Single-Cell RNA Profiling of Glomerular Cells Shows Dynamic Chan... 2019 · 213 cites
- Mapping the genetic architecture of human traits to cell types i... 2021 · 163 cites
- Single cell transcriptomics identifies focal segmental glomerulo... 2020 · 162 cites
- Iterative transfer learning with neural network for clustering a... 2020 · 156 cites
- Genetic studies of urinary metabolites illuminate mechanisms of... 2020 · 147 cites
8 further papers cite this accession but reuse could not be confirmed.
Deep data QC
83/100 · BStandardized, field-standard QC computed by touching the data — every metric states how it was obtained · evidence: measured
This is mouse RNA-seq with excellent base quality and minimal sequencing artifacts, but at only 26 bp mean read length, it's unusually short. The short reads will complicate accurate mapping and splice junction detection, raising the risk of ambiguous alignments that could skew expression estimates. For gene-level quantification this will work acceptably; for isoform-level work or fine-grained transcript analysis, you should seek a dataset with longer reads (typically 50+ bp).
The B grade is a transparent weighted average. Each metric below scored from 0–100% against the published bulk-RNA-seq thresholds, weighted by its importance; nothing is hidden or subjective.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0
Scientific quality
Based on hands-on reproduction of the papers that use this dataset. A reproducible paper that stands on this data is positive evidence; a flagged one is a prompt to look closer — never a verdict on the dataset itself without the evidence.
- Trans-ethnic association study of blood pressure determinant... L1 No data access