Single-Cell Transcriptomic Atlas Reveals Metabolic Reprogramming and Transdifferentiation Trajectories in Tubular...

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.

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.

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).

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.