Evidence mapPaperPMID 40547113Full record

ArticleCell insight2025

Single cell sequencing and spatial multiomics of diabetic kidney segmentation insights zonation-specific therapeutic metabolic pathways.

Shi Qiu, Zhibo Wang, Sifan Guo, Dandan Xie, Ying Cai, Xian Wang, Chunsheng Lin, Songqi Tang, Yiqiang Xie, Aihua Zhang

Abstract read
In one paragraph

Article in Cell insight, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Integrated Omics RevealPharmaceuticals (Basel, Switzerland) · 2025
    Article
  7. 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

10 authors.

Shi QiuInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Zhibo WangInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Sifan GuoInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Dandan XieInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Ying CaiInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Xian WangInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Chunsheng LinGraduate School, Second Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin 150040, Heilongjiang, China.
Songqi TangInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Yiqiang XieInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.
Aihua ZhangInternational Advanced Functional Omics Platform, Scientific Experiment Center, Hainan Engineering Research Center for Biological Sample Resources of Major Diseases, Hainan Medical University, Xueyuan Road 3, Haikou 571199, Hainan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic nephropathy (DN) exhibits profound spatial metabolic heterogeneity across kidney regions, yet how compartmentalized pathways drive disease progression remains poorly defined. A deeper understanding of the organizational spatial environment and metabolic pathways of diabetic kidney damage will provide new insights to develop new therapies. By integrating high-resolution spatial multi-omics and single-cell transcriptomics, we mapped region-specific metabolic dysregulation in diabetic kidneys, identifying glutathione metabolism, pentose phosphate, and glycolytic pathways as zonally disrupted in cortical and medullary regions. Spatial metabolomics revealed distinct anatomical clustering of ten clinically associated metabolites, while spatial proteomic profiling uncovered sixty-four region-enriched proteins linked to these pathways. Specifically, depending on anatomic location, spatial protein signatures across multiple regions of diabetic mouse kidneys were enriched in each segmentation, respectively. Cross-species integration identified GPX3 as a fibroblast-enriched biomarker strongly correlated with kidney dysfunction and closely related to clinical indicators. Notably, astragaloside IV (ASIV) treatment reversed spatial metabolic perturbations in diabetic mice, restoring glutathione and glycolytic pathway activity in a compartment-specific manner. Single-cell analyses identified five cell types-endothelial cells, fibroblasts, epithelial cells, macrophages and neutrophils-and further revealed fibroblasts as key contributors to regulatory effects via GPX3 overexpression. Importantly, the higher expression of Gpx3 in fibroblasts compared to other cell types, Gpx3 (AUC = 0.995), was further validated, demonstrating the high sensitivity and specificity for DN patients. This multimodal atlas establishes the spatially resolved metabolic blueprint of DN, bridging molecular zoning with anatomical localization of renal tissue to unveil actionable therapeutic targets for metabolic disorders in kidney disease.

Indexed as

Metabolic pathwayMetabolic zonationMetabolismSpatial metabolomicsSpatial proteomicsTarget

Identifiers

PMID40547113
PMCPMC12179620

What Socratic holds

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LicenceCC BY-NC-ND
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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.