Fig. 2: Spatial characterization of renal cell carcinoma (RCC) through integrative spatial transcriptomics analysis.

A Schematic diagram of the spatial transcriptomics analysis workflow. All data were generated using the 10X Visium platform. Cell niche analysis was performed based on our spatial integration strategy to dissect the spatial organizations in RCC. For more details, see Supplementary Fig. 4A and Methods section. B–C UMAP projection of spatial domains (n = 835 domains) identified across 48 spatial transcriptomics samples. Each point represents a spatial domain defined via stLearn. In (B), spatial domains are colored by their assigned cell niche (CN1-CN23). One representative pie chart per CN indicates dominant cell types (>8%). In (C), each spatial domain is displayed as a pie chart showing its major cell-type composition (>8%). Pie chart colors correspond to the cell types shown in the legend (C). A complementary UMAP embedding colored by data source, sample type, and section identity is provided in Supplementary Fig. 5A to confirm effective batch correction. Detailed results are provided in Supplementary Data 5, summarizing dominant cell types and putative functions of each cell niche. D Bar plot showing the frequency of occurrence (x-axis) of each cell niche (y-axis) across 48 tumor sections. Colors in the bar and pie plots correspond to the cell niches in (B) and cell types in (C), respectively. E Network graph showing spatial associations of dominant cell types across all 23 CNs, delineating how distinct cell populations are spatially organized within the TME. Each node represents a dominant cell type, and an edge connects two cell types that were simultaneously observed within at least one CN. F Boxplot comparing the area of CN15 in PT samples from RCC patients with (n = 8) and without (n = 8) TT. P-values were determined using the two-sided unpaired t-test. Box plots display median, upper and lower quartiles, with whiskers indicating maximum and minimum data points within 1.5 × interquartile range. G Spatial mapping of cell niches (left) and dominant cell types of CN15 (right) in tumor thrombus (P33, n = 4,258 spots) and primary tumor (P30, n = 4,287 spots) sections from two RCC patients with TT. Arrows indicate the location of CN15. This panel provides a visual complement to Fig. 2F by depicting the spatial localization and cellular composition of CN15 at the tissue level. Data are representative of n  =  18 spatial transcriptomic slides. H Representative multiplex immunofluorescence images (left) and quantitative analysis (right) of CN15-like regions—defined by the spatial adjacency of fibroblasts (DCN, green) and EMT-like cancer cells (PLOD2, red)—in tumor sections from RCC patients without (n = 5) and with (n = 5) TT. DCN⁺ and PLOD2⁺ cells were annotated using QuPath, and their interface regions were manually delineated and quantified using Fiji software. The cumulative area of each interface region was then normalized to the total area of the corresponding tumor section. Box plots show the distribution of the interface area (% tissue area) between DCN⁺ cells and PLOD2⁺ cells across groups. The box represents the interquartile range (IQR, 25th–75th percentile), with the horizontal line indicating the median. Whiskers denote minimum and maximum values. This analysis provides spatial validation of the findings shown in Fig. 2F. Scale bar = 200 µm; Scale bar inset = 50 µm. P-values were determined using the two-sided Wilcoxon rank-sum test. Source data are provided as a Source Data file.