Fig 1.
Single-Cell Profiling and Diagnostic Model Workflow for DKD.
Abbreviations: scRNA-seq. Single-cell RNA sequencing; DKD, diabetic kidney disease; GSVA, Gene set variation analysis; LASSO, Least Absolute Shrinkage and Selection Operator; PPI, protein-protein interaction; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology.
Fig 2.
Single-cell profiling of the human kidney in DKD.
(A) Dot plot displaying the expression of canonical marker genes used to identify the 11 major renal cell types. (B) UMAP projection of 20,220 high-quality cells from the integrated dataset (GSE131882; n = 3 controls, n = 3 DKD patients), color-coded by annotated cell type. Unsupervised clustering identified 11 distinct renal cell subpopulations, with key types abbreviated as follows: CD-PC (collecting duct principal cell); PTC (proximal tubule cell); Inj-PTC (injured PTC); DCT (distal convoluted tubule cell); LOH (loop of Henle); CD-ICA/CD-ICB (collecting duct intercalated cells); EC (endothelial cell); Podo (podocyte); Mes (mesenchymal cell); Imm (immune cell). (C) Stacked bar plot showing the proportional abundance of each cell type in control and DKD groups. (D) Stacked bar plot comparing the proportions of injured proximal tubule cells (Inj-PTC) and normal PTCs between control and DKD groups. The difference in proportion was assessed using a Chi-square test for categorical variables, and the p-value is unadjusted. (E-F) Violin plots showing the expression levels of injury markers (E) HAVCR1 and VCAM1, and functional genes (F) SLC34A1, CUBN, and SLC22A6 in Inj-PTC (n = 1794) versus normal PTC (n = 3794) clusters. Differential expression analysis for each gene was performed using the Wilcoxon rank-sum test; p-values are unadjusted. The black horizontal bar within each violin represents the median expression value.
Fig 3.
Identification and exclusion of non-renal cells in DKD urinary sediment single-cell data.
(A) UMAP projection of 3,421 cells from DKD urinary sediments (GSE266146) and healthy controls (GSE157640), colored by initial unsupervised clustering. The 9 clusters (labeled 0-8) represent groups of cells with similar gene expression profiles, identified computationally prior to biological annotation. (B) Feature plots demonstrating the expression of bladder/urethral epithelial markers (PSCA, KRT13, FXYD3, PLAT) in specific clusters. (C) Integrated UMAP projection combining cells from kidney tissue (green) and urinary sediment (yellow). Kidney tissue cells (green, from GSE131882) are pre-annotated with “K_” prefixes (e.g., K_Podo for podocytes). Urinary sediment cells (yellow) are labeled with “U_” prefixes and cluster numbers, showing their spatial relationship to known kidney cell types. (D-E) Stacked bar plots illustrating the proportion of contaminating (bladder/urethral) cells versus renal-origin cells in the urinary sediments of control, early-stage DKD, and late-stage DKD groups. (F) Dot plot of canonical marker genes used to annotate the renal cell types within the urinary sediment data after quality control and the exclusion of non-renal clusters.
Fig 4.
Characterization of renal cell subtypes in DKD urinary sediments.
(A) UMAP projection of 2,089 high-quality, kidney-derived cells from urinary sediments of DKD patients (n = 4 early-stage, n = 4 late-stage). Cells are color-coded by annotated type. Key populations include injured and proliferative proximal tubule cells (Inj-PTC, prolif-PTC), podocytes (PODO), loop of Henle cells (LOH, Inj-LOH), collecting duct principal cells (CD-PC), and macrophage subtypes (M2-Mac, Inf-Mac). (B) Stacked bar plot depicting the relative abundance of each annotated renal cell type in early and late stages of DKD. (C-D) Violin plots comparing the expression of injury markers (C) HAVCR1 and VCAM1, and proliferation markers (D) TOP2A and CENPF between Inj-PTC (n = 385) and prolif-PTC (n = 98) clusters. The black horizontal bar represents the median expression value. Differential expression for each gene was performed using the Wilcoxon rank-sum test; p-values are unadjusted. (E-F) Transcriptional dynamics during proximal tubule injury. (E) Pseudotime ordering of cells from a proliferative (Prolif-PTC, purple) to an injured state (Inj-PTC, blue). (F) Corresponding heatmap shows gene expression changes (blue: low; red: high) along this trajectory, revealing genes activated or suppressed during injury. (G) Gene Set Variation Analysis (GSVA) displaying pathways enriched in prolif-PTC (top, purple) and Inj-PTC (bottom, blue) clusters.
Fig 5.
Machine learning-based discovery and validation of diagnostic biomarkers for DKD.
(A) LASSO regression coefficient profile of genes differentially expressed in early vs. advanced DKD urinary cells. (B-G) Receiver operating characteristic (ROC) curves evaluating the diagnostic performance of PDK4, RHCG, and FBP1 in distinguishing DKD from controls in the training set (n = 40 DKD, 21 controls) and independent validation sets (n = 30 DKD, 42 controls). (H-I) Performance evaluation of multi-gene diagnostic models built using four machine learning algorithms in the training set (H) and independent validation set (n = 27 DKD, 9 controls) (I). (J) Box plots showing the expression levels of PDK4, RHCG, and FBP1 in control versus DKD groups (n = 21 controls, 40 DKD). Statistical significance was determined using the Wilcoxon rank-sum test; p-values are unadjusted. (*P < 0.05, **P < 0.01, ***P < 0.001). (K) Scatter plot with regression line showing the correlation between FBP1 expression and estimated glomerular filtration rate (eGFR) in DKD patients from the Nephroseq database.
Fig 6.
Expression patterns of diagnostic biomarkers in DKD kidney tissues.
(A-B) Pseudotime and pathway analysis of kidney PTCs, GSVA enrichment scores showing pathways upregulated in normal PTC (top, blue) and Inj-PTC (bottom, blue).
Fig 7.
Spatial localization and functional enrichment of DKD diagnostic markers.
(A) UMAP projection of DKD kidney single cells (GSE131882), with feature plots showing the expression patterns of PDK4, RHCG, and FBP1. (B) Protein-protein interaction (PPI) network of the three hub genes and their co-expressed genes. (C) Bar plot of the top enriched KEGG pathways for the gene network. (D) Bar plot of the top enriched Gene Ontology (GO) terms, categorized into Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). (E-G) Representative immunohistochemistry images from the Human Protein Atlas (HPA) database showing the protein expression of (E) PDK4, (F) FBP1, and (G) RHCG in normal human kidney tissue.