Evidence map›Paper›PMID 40723524›Full record

ArticleAnimals : an open access journal from MDPI2025

Renal Single-Cell RNA Sequencing and Digital Cytometry in Dogs with X-Linked Hereditary Nephropathy.

Candice P Chu, Daniel Osorio, Mary B Nabity

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Candice P ChuDepartment of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0001-8543-7233
Daniel OsorioDepartment of Veterinary Integrative Biosciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.
Mary B NabityDepartment of Veterinary Pathobiology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0001-6787-822X

Funding

Texas A&M University Callie Cook Endowment FundTexas A&M University Triads for Transformation Grant
6 · The paper itself

Abstract

Chronic kidney disease (CKD) significantly affects canine health, but the precise cellular mechanisms of this condition remain elusive. In this study, we used single-cell RNA sequencing (scRNA-seq) to profile renal cellular gene expression in a canine model of X-linked hereditary nephropathy (XLHN). Dogs with this condition exhibit juvenile-onset CKD similar to that seen in human Alport syndrome. Post-mortem renal cortical tissues from an affected male dog and a heterozygous female dog were processed to obtain single-cell suspensions. In total, we recovered up to 13,190 cells and identified 11 cell types, including major kidney cells and immune cells. Differential gene expression analysis comparing the affected male and heterozygous female dogs identified cell-type specific pathways that differed in a subpopulation of proximal tubule cells. These pathways included the integrin signaling pathway and the pathway for inflammation mediated by chemokine and cytokine signaling. Additionally, using machine learning-empowered digital cytometry, we deconvolved bulk mRNA-seq data from a previous canine study, revealing changes in cell type proportions across CKD stages. These results underline the utility of single-cell methodologies and digital cytometry in veterinary nephrology.

Indexed as

Alport syndromedigital cytometrydogkidneyRNA-seqscRNA-seqX-linked hereditary nephropathy

Identifiers

PMID40723524
PMCPMC12291704

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.