Evidence map›Paper›PMID 42327115›Full record

ArticlebioRxiv : the preprint server for biology2026

A multi-omics map of diabetic nephropathy reveals c-Jun as a driver of tubular injury and metabolic stress.

Qiwen Deng, Yu Liu, Nathan A Bracey, Yi-Chuan Wang, Paul Alexander Wadsworth, Marjia Afrin, Matteo Santoro, Shih-Yu Chen, Joseph C Wu, Vivek Charu and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Qiwen DengDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.
Yu LiuCardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA.
Nathan A BraceyStanford Institute for Immunology, Transplantation and Infection, Stanford University, Stanford, CA, USA.
Yi-Chuan WangInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Paul Alexander WadsworthDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.
Marjia AfrinDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.
Matteo SantoroDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.
Shih-Yu ChenInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Joseph C WuCardiovascular Institute, Stanford School of Medicine, Stanford, CA, USA.
Vivek CharuDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.
Gerlinde WernigDepartment of Pathology, Stanford School of Medicine, Stanford, CA, USA.

Funding

Stanford Islet Research CoreP30DK116074 · NIDDK · STANFORD UNIVERSITY · PI Seung K Kim · 2017 to 2026
$19.5M
NIDDK NIH HHS P30 DK116074
6 · The paper itself

Abstract

Diabetic nephropathy (DN) is a major cause of end-stage renal disease, yet the molecular mechanisms driving tubular injury and fibrosis remain poorly defined. Here, we integrated single-cell multiplexed protein imaging, spatial transcriptomics, single-nucleus and single-cell RNA sequencing and chromatin accessibility profiling to comprehensively characterize human DN pathology. Our multi-modal analysis precisely maps kidney cell types and their spatial distributions, immune-fibrotic interactions, and key transcriptional regulators. We identified eight distinct cellular neighborhoods defining the immune-fibrotic microenvironment and uncovered molecular networks driving tubular injury and fibrosis.

Identifiers

PMID42327115
PMCPMC13278112

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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 OpenQuestion graph.