Journal: Genes & Diseases

Article Title: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics

doi: 10.1016/j.gendis.2025.101983

Figure Lengend Snippet: Workflow of this study. (A) Cryosectioned prostate cancer (PCa) tissue samples were mounted on Superfrost slides (VWR) containing a spatial microarray of 4992 barcoded spots (55 μm in diameter, 100 μm center-to-center spacing) to enable spatially resolved gene expression profiling. (B) Transcriptomic read count matrices were generated following the CytAssist ST platform. (C) Dimensionality reduction and clustering of the spatial spots were performed through PCA, UMAP and Louvain clustering analyses. (D) The clusters were annotated as distinct histological structures based on pathological evaluation. (E) Malignancy-associated features of the classified clusters were assessed through inferCNV analysis. (F) Developmental trajectories and progression stages were further characterized using DPT and PAGA analyses. (G) To identify genes associated with PCa progression, DEG analysis was conducted by comparing GE clusters aligned with GS progression patterns that met all predefined criteria. (H) Commonly dysregulated DEGs across 12 PCa samples were identified and summarized. (I) The protein expression of candidate DEGs (H2AFJ, SLC4A4, TFF3) was validated in PCa tissues spanning a spectrum of GSs and PIN using IHC staining.

Article Snippet: Next, the slides with tissue sections were processed using the Visium CytAssist instrument (10x Genomics, CA, USA), during which the probes were transferred to the capture area of the Visium CytAssist spatial gene expression slide.

Techniques: Microarray, Gene Expression, Generated, Expressing, Immunohistochemistry

Journal: Genes & Diseases

Article Title: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics

doi: 10.1016/j.gendis.2025.101983

Figure Lengend Snippet: The transcriptomic classification of all spatially resolved spots across CytAssist_1 to CytAssist_4 prostate cancer (PCa) tissue sections into distinct histological structures. (A) Transcriptomic classifications across all spots were performed using PCA, UMAP, and Louvain clustering analysis. The resulting clusters were overlaid onto the histological image and annotated according to their corresponding histological structures (upper panel), and visualized in a UMAP embedding (lower panel). (B) The regions of distinct clusters were magnified and pathologically evaluated based on corresponding HE-stained images from each PCa section. (C) The expression of canonical marker genes—including TACSTD2 and EPCAM (epithelial), KRT5 and TP63 (basal), KRT8 and AR (luminal), and KLK3 and AMACR (PCa)—was analyzed across annotated clusters and visualized as bubble plots.

Article Snippet: Next, the slides with tissue sections were processed using the Visium CytAssist instrument (10x Genomics, CA, USA), during which the probes were transferred to the capture area of the Visium CytAssist spatial gene expression slide.

Techniques: Staining, Expressing, Marker

Journal: Genes & Diseases

Article Title: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics

doi: 10.1016/j.gendis.2025.101983

Figure Lengend Snippet: Evaluation of tumor malignancy in varying GE regions across CytAssist_1 to CytAssist_8 prostate cancer (PCa) tissue sections using inferCNV analysis. (A) The CNV scores were calculated for spatially resolved spots across eight samples using inferCNV. The scores are visualized as spatial activity maps overlaid on the histological images (left panel) and as UMAP embeddings (right panel). (B) The chromosomal landscapes derived from inferCNV values for distinguishing histological clusters across the samples are displayed separately as corresponding heatmaps. (C) The median CNV scores, along with their interquartile ranges (IQRs), for GE clusters are summarized using boxplots for each sample.

Article Snippet: Next, the slides with tissue sections were processed using the Visium CytAssist instrument (10x Genomics, CA, USA), during which the probes were transferred to the capture area of the Visium CytAssist spatial gene expression slide.

Techniques: Activity Assay, Derivative Assay

Journal: Genes & Diseases

Article Title: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics

doi: 10.1016/j.gendis.2025.101983

Figure Lengend Snippet: Prediction of progression stages and developmental trajectories across CytAssist_1 to CytAssist_8 prostate cancer (PCa) tissue sections. (A) Developmental progression was reconstructed using DPT analysis. Diffusion maps of spatially resolved spots for each sample are presented, displaying coordinates specific to varying GE clusters (left panel) and corresponding dpt_pseudotime values (right panel). (B) The quantitative distribution of dpt_pseudotime values within GE clusters for each sample is visualized as violin plots. (C) Developmental trajectories of GE clusters for each sample are visualized using PAGA graphs, displaying individual spatial spots (left panel) and GE clusters (right panel). (D) The CNV score (left panel) and dpt_pseudotime values (right panel) were integrated into the PAGA graph to illustrate developmental dynamics within each GE cluster.

Article Snippet: Next, the slides with tissue sections were processed using the Visium CytAssist instrument (10x Genomics, CA, USA), during which the probes were transferred to the capture area of the Visium CytAssist spatial gene expression slide.

Techniques: Diffusion-based Assay

Journal: Genes & Diseases

Article Title: Uncovering genes driving developmental stage progression in prostate cancer through spatial transcriptomics

doi: 10.1016/j.gendis.2025.101983

Figure Lengend Snippet: DEG analysis across varying clusters based on progression stages and developmental trajectories in CytAssist_1 to CytAssist_8 prostate cancer (PCa) tissue sections. (A) DEG analysis was performed for GE clusters, ensuring that the comparison objects exhibited consistent relative progression stages and developmental trajectories. The comparisons for each tissue section are illustrated using volcano plots, with key genes highlighted: oncogenes (TFF3, OR51E2, FOLH1, AMACR, FOS, SLC4A4, EGR1, NDUFB9, and H2AFJ) in dark magenta and antioncogenes (MME, PTGDS, TTN, ENG, and TGM2) in dark blue. (B) The numbers of genes that were significantly and concurrently up-regulated (upper panel) or down-regulated (lower panel) in the above comparisons for each tissue section are depicted in Venn diagrams.

Article Snippet: Next, the slides with tissue sections were processed using the Visium CytAssist instrument (10x Genomics, CA, USA), during which the probes were transferred to the capture area of the Visium CytAssist spatial gene expression slide.

Techniques: Comparison