Evidence map›Paper›PMID 37275529›Full record

ArticleiScience2023

A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery.

Claudio Novella-Rausell, Magda Grudniewska, Dorien J M Peters, Ahmed Mahfouz

Abstract read
In one paragraph

Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Insulin receptorNature communications · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. AZGP1 deficiency promotes chronic kidney fibrosis in aging mice.American journal of clinical and experimental urology · 2026
    Article
  9. Article
  10. bioRxiv : the preprint server for biology · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. Lack of a role of NHE4 in renal ammonia metabolism.American journal of physiology. Renal physiology · 2025
    Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Cytochrome bAging cell · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Claudio Novella-RausellDepartment of Human Genetics, Leiden University Medical Centre, 2333 ZA Leiden, the Netherlands.
Magda GrudniewskaGenomeScan, 2333 BZ Leiden, the Netherlands.
Dorien J M PetersDepartment of Human Genetics, Leiden University Medical Centre, 2333 ZA Leiden, the Netherlands.
Ahmed MahfouzDepartment of Human Genetics, Leiden University Medical Centre, 2333 ZA Leiden, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The kidney's cellular diversity is on par with its physiological intricacy; yet identifying cell populations and their markers remains challenging. Here, we created a comprehensive atlas of the healthy adult mouse kidney (MKA: Mouse Kidney Atlas) by integrating 140.000 cells and nuclei from 59 publicly available single-cell and single-nuclei RNA-sequencing datasets from eight independent studies. To harmonize annotations across datasets, we built a hierarchical model of the cell populations. Our model allows the incorporation of novel cell populations and the refinement of known profiles as more datasets become available. Using MKA and the learned model of cellular hierarchies, we predicted previously missing cell annotations from several studies. The MKA allowed us to identify reproducible markers across studies for poorly understood cell types and transitional states, which we verified using existing data from micro-dissected samples and spatial transcriptomics.

Indexed as

Biological sciencesCell biologyTranscriptomics

Identifiers

PMID37275529
PMCPMC10238935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.