ArticleInternational journal of genomics2026
Single-Cell Transcriptomic Atlas Reveals Metabolic Reprogramming and Transdifferentiation Trajectories in Tubular Epithelial Cells During Chronic Kidney Disease Progression.
Article in International journal of genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Single-Cell Transcriptomic Atlas Reveals Metabolic Reprogramming and Transdifferentiation Trajectories in Tubular Epithelial Cells During Chronic Kidney Disease Progression.International journal of genomics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In chronic kidney disease (CKD), there is evidence of loss of function and fibrosis in the progression of tubular epithelial cells; however, the cellular heterogeneity and underlying molecular mechanisms are not well defined. Knowledge of the diversity among tubular cells is essential for precision medicine therapy. Methods: We subjected renal cells from CKD patients to single-cell RNA sequencing with a focus on tubular epithelial populations. Through unsupervised clustering, a computational pipeline that includes metabolic pathway scoring and pseudotemporal trajectory inference combined with machine learning classification, our analysis enabled the characterization of intratumoral cellular diversity and metabolic states coupled to differentiation trajectories. We analyzed segment-specific markers (SLC34A1, SLC5A2, LRP2, CUBN, ALDOB, and GATM) and metabolic enzymes (glycolysis and TCA cycle enzymes) in cell subsets. Results: Tubular cells showed marked heterogeneity and metabolic reprogramming from oxidative phosphorylation to glycolysis, clustering as OXPHOS-high, glycolytic, dormant, and intermediate cell states. The expression of segment-specific markers was differentially retained and lost, reflecting maintenance of fate as well as dedifferentiation. Pseudotemporal analysis demonstrated progressive cellular transitions driven by the expression of critical genes such as MALAT1 and ANXA1. Proximal tubule cells constituted ∼70% of the profiled cells with different transcriptomic signatures. Moderate classification results were obtained using a machine learning approach (ROC AUC = 0.673). Conclusions: This work furnishes a molecular atlas of tubular epithelial cell heterogeneity in CKD, highlighting metabolic reprogramming and transdifferentiation as key processes driving tubular dysfunction and fibrosis. These results also revealed potential therapeutic targets to retain tubular function and ameliorate the progression of CKD.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.