ArticleThe American journal of pathology2025
Histopathologic Analysis of Human Kidney Spatial Transcriptomics Data: Toward Precision Pathology.
Article in The American journal of pathology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
The trial behind it
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Who cites it
9 citing papers in PubMed.
- Particulate Matter Exposure and Diabetic Kidney Dysfunction: Insights from Integrated Transcriptomic and Bioinformatics Analyses.International journal of molecular sciences · 2026Article
- Optimizing Early Detection of Diabetic Kidney Disease through Synergistic Biomarkers and Serum Metabolites in Humans.Diabetes & metabolism journal · 2026Article
- SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.Briefings in bioinformatics · 2026Article
- Decoding the hypoxic injury landscape and hypoxic risk model construction in diabetic kidney disease: a multi-omics study.Frontiers in bioinformatics · 2026Article
- Delactylase effects of SIRT3 on a positive feedback loop involving the RUNX1-glycolysis-histone lactylation in diabetic kidney disease.International journal of biological sciences · 2026Article
- Novel insights into kidney disease: the scRNA-seq and spatial transcriptomics approaches: a literature review.BMC nephrology · 2025Review
- Landscape analysis of m6A modification regulators reveals LRPPRC as a key modulator in tubule cells for DKD: a multi-omics study.Frontiers in pharmacology · 2025Article
- Decoding Kidney Pathophysiology: Omics-Driven Approaches in Precision Medicine.Journal of personalized medicine · 2024Review
- Multi-Omics Integration in Nephrology: Advances, Challenges, and Future Directions.Seminars in nephrology · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The application of spatial transcriptomics (ST) technologies is booming and has already yielded important insights across many different tissues and disease models. In nephrology, ST technologies have helped to decipher the cellular and molecular mechanisms in kidney diseases and have allowed the recent creation of spatially anchored human kidney atlases of healthy and diseased kidney tissues. During ST data analysis, the computationally annotated clusters are often superimposed on a histologic image without their initial identification being based on the morphologic and/or spatial analyses of the tissues and lesions. Herein, histopathologic ST data from a human kidney sample were modeled to correspond as closely as possible to the kidney biopsy sample in a health care or research context. This study shows the feasibility of a morphology-based approach to interpreting ST data, helping to improve our understanding of the lesion phenomena at work in chronic kidney disease at both the cellular and the molecular level. Finally, the newly identified pathology-based clusters could be accurately projected onto other slides from nephrectomy or needle biopsy samples. Thus, they serve as a reference for analyzing other kidney tissues, paving the way for the future of molecular microscopy and precision pathology.
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Registered trials
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.