Journal: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: Single‐cell and spatial transcriptome landscape of healthy and fibrotic kidneys after unilateral ischemia‐reperfusion injury (UIRI). a) Schematic representation of single‐cell RNA sequencing (scRNA‐seq) and spatial transcriptomics (ST) of kidneys from the sham and 10‐day UIRI mice, graphically designed with Biorender ( https://www.biorender.com/ ). b) t‐SNE plot illustrating the intricate cellular diversity in fibrotic kidneys, demonstrating distinct clusters representing glomerular endothelial cells (GEC), podocytes (Podo), mesangial cells (Mesa), Bowman's capsule epithelium (BC), proximal tubules (PT), descending limbs of Henle (DLOH), ascending limbs of Henle (ALOH), distal tubules (DT), principal cells (PC), intercalated cells (IC), fibroblasts (Fib), smooth muscle cells (SMC), extraglomerular endothelial cells (EGEC), monocytes (Mono), dendritic cells (DC), macrophages (Mϕ), plasmacytoid dendritic cells (pDC), proliferating mononuclear lineage (Prolif mono_L), and neutrophils (Neu), B cells (B), T cells (T), proliferating T cells (prolif T), and natural killer cells (NK). These cell types were further categorized into four major compartments: Glomerular, Renal, Interstitium, and Immune, as indicated by color grouping in the plot. c) Bubble plot illustrating the relative proportions of major kidney cell types in sham and UIRI samples. Each dot represents the proportion of a given cell type in a specific sample group, with dot size corresponding to its relative proportion. d) A comprehensive heatmap depicting the unique marker gene signature of major renal cell types. e) UMAP plot illustrating the inferred renal cell region distribution based on integrated spatial transcriptomics data from normal (Sham) and UIRI 10D mouse kidneys, generated using the 10x Genomics Visium platform. The identified regions include glomerular cells (Glom), distinct segments of the proximal tubule (PTS1, PTS1S2, PTS2), injured proximal tubules (InjPT), ascending limbs of Henle in cortex (ALOH(C)), distal tubules (DT), connecting tubules and collecting ducts (CNT_CD), cells at the corticomedullary junction (CMJ), fibrogenic niche regions (Niche1, Niche2), the inner stripe of the outer medulla (IOM), inner medulla (IM), renal capsule (RC), and perirenal tissue (Perirenal). f) Spatial maps illustrating the anatomical distribution of renal cell regions in Sham and UIRI 10D mouse kidneys. Region colors correspond to the classifications defined in panel (e). g) Bubble plot illustrating the relative proportions of major renal cell regions in spatial transcriptomics data from sham and UIRI 10D mouse kidneys. h) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in spatial transcriptomics data. Dot color indicates the average gene expression level within each region, while dot size represents the proportion of spatial spots expressing the gene. i) Schematic diagram of nephron segmentation by cell types. j) Comparison of kidney anatomical regions and spatial transcriptomic clusters, showing clusters in kidney tissue (top) and the corresponding Visium H&E‐stained section (bottom). k) Renal tissue structure alterations at the corticomedullary junction (CMJ) in UIRI samples, showing the formation of two distinct fibrogenic niches, Niche1 and Niche2. l) A heatmap showing the deconvolution scores of cell type compositions across different regions in Visium spatial transcriptomics data, obtained using the RCTD method. m) Spatial FeaturePlots of RCTD‐derived cell type scores in the sham (top) and UIRI (bottom) groups, with paired panels sharing a common legend.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: RNA Sequencing, Marker, Generated, Expressing, Gene Expression, Comparison, Staining, Derivative Assay

Journal: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: High‐resolution spatial transcriptomics and immunostaining reveal the TNC‐enriched fibroblast‐macrophage niche organization in fibrotic kidneys. a) Schematic diagram of the Visium HD workflow applied to kidney tissues from sham and UIRI model mice. b) UMAP visualization of integrated Visium HD spatial transcriptomics data from control mice (obtained from the 10x Genomics public dataset) and UIRI mice (this study), processed using canonical correlation analysis (CCA). This dimensionality reduction visualization reveals distinct clusters representing various renal parenchymal and stromal cell populations, including: Glomerulus, Vasculature, PTS1, PTS2, PTS1S2, InjPT, ascending limbs of Henle in cortex [ALOH(Cortex)], distal tubule and connecting tubule (DT_CNT), connecting tubule and collecting duct (CNT_CD), collecting duct in cortex [CD(Cortex)], PTS3, injured PTS3 (InjPTS3), Fibrogenic Niche, Vasa recta, loop of Henle in outer medulla [LOH(IOM)], collecting duct in outer medulla [CD(IOM)], collecting duct in inner medulla [CD(IM)], thin ascending limbs of Henle in inner medulla [tALOH(IM)], renal capsule (RC), Perirenal Fibrous tissue, and Perirenal Adipose tissue. c) Bubble plot comparing the regional distribution in Control versus UIRI 10d kidneys (Visium HD). d) Bubble plot depicting the expression patterns of marker genes across distinct renal cell regions in Visium HD data. e) Spatial maps generated using Visium HD illustrate the inferred anatomical distribution of renal cell regions in kidney tissues from Control and UIRI mice. f) Spatial Feature Plots of Visium HD data showing the spatial distribution of selected renal cell types in controls (top) and UIRI mice (bottom), based on cell‐type deconvolution using RCTD. g) A heatmap showing the correlation between NMF factors and cell‐type deconvolution scores in standard Visium spatial transcriptomics data. h) Spatial distribution of gene scores associated with the NMF factors most correlated with the fibrogenic niche, along with the contribution of key genes to each factor. i) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in standard Visium. j) A heatmap showing the correlation between NMF factors and cell type deconvolution scores in Visium HD spatial transcriptomics data. k) Spatial distribution of NMF factors (NMF3 and NMF11) associated with the fibrogenic niche in Visium HD data, along with their corresponding high‐contributing genes. l) Spatial FeaturePlots showing the anatomical distribution of Tnc expression in Visium HD datasets. m) Immunofluorescence staining demonstrates colocalization of TNC with macrophages (F4/80⁺) in the CMJ interstitial region. From top to bottom: an overview merged image (Merge), followed by magnified views of TNC, Vimentin, and F4/80 staining in the same region, and an enlarged merged image (Enlarged Merge) at the bottom.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: Immunostaining, Control, Expressing, Marker, Generated, Immunofluorescence, Staining

Journal: Advanced Science

Article Title: Single Cell and Spatial Transcriptomics Define a Proinflammatory and Profibrotic Niche After Kidney Injury

doi: 10.1002/advs.202503691

Figure Lengend Snippet: TLR4 knockout in macrophages attenuates renal inflammation and renal fibrosis in vivo. a) The diagram shows the experimental protocol. Bone marrow chimera models were established by transplanting the WT bone marrow to WT mice, or TLR4 KO bone marrow to WT mice. Mice were irradiated at a single dose of 1100 Rads and then underwent bone marrow transplantation. After 8 weeks of successful transplantation, a unilateral ischemia‐reperfusion (UIRI) model was established. b) PCR‐based identification of kidney genotypes in the recipient mice of bone marrow transplantation models using TLR4 mutation site primers and wild‐type site primers, respectively. c,d) Graphic presentations show serum creatinine (Scr) (c) and blood urea nitrogen (BUN) (d) levels in different groups as indicated at 11 days after IRI. * p < 0.05 versus WT‐WT (n = 4–6). e,f) Western blot analyses show renal expression of TLR4, p‐P65, and P65 in different groups as indicated. Representative Western blot (e) and quantitative data (f) are shown. * p < 0.05 versus WT‐WT (n = 4–6). g) Representative micrographs show renal expression and co‐localization of TLR4 and F4/80 by immunofluorescence staining in different groups as indicated. The areas between the dashed lines represent the corticomedullary junction of the kidney. h,i) Western blot analyses show renal expression of MR, Arg‐1, iNOS, TNF‐α, and CCL2 in different groups as indicated. Representative Western blot (h) and quantitative data (i) are shown. * p < 0.05 versus WT‐WT (n = 4–6). j,k) Western blot analyses show renal expression of TNC, FN, and α‐SMA in different groups as indicated. Representative Western blot (j) and quantitative data (k) are shown. * p < 0.05 versus WT‐WT (n = 4–6). l) A schematic diagram shows a crucial role of TNC in organizing the proinflammatory and profibrotic niche. By integrating single‐cell RNA sequencing and spatial transcriptomics, we unveil TNC as a central organizer of the proinflammatory and profibrotic niche in kidney fibrosis. TNC promotes macrophage activation through TLR4/NF‐κB signaling, leading to macrophage activation, proliferation, and cytokine production.

Article Snippet: For the preparation of sections for Visium Spatial Transcriptomics sequencing, samples were equilibrated at −18 °C and a 10 μm thick section was cut onto the active sequencing area (6 mm x 6 mm) of a spatial barcoded slide.

Techniques: Knock-Out, In Vivo, Irradiation, Transplantation Assay, Mutagenesis, Western Blot, Expressing, Immunofluorescence, Staining, RNA Sequencing, Activation Assay

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Acquisition of paired breast cancer spatial transcriptomics datasets and histology images from 10x Visium and Xenium. B. Co-registration of Visium and Xenium histology slides into a common coordinate system. The green box highlights the overlapping region retained between the two technologies. C. Rasterization of gene counts onto a uniform grid matched to Visium spot resolution, followed by extraction of the overlapping tissue region. Expression is visualized as patches. D. Training of deep learning models to predict per-patch gene expression from histology image patches. E. Performance evaluation on held-out replicates, comparison across technologies, and ablation experiments of inputs.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Extraction, Expressing, Gene Expression, Comparison

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the held-out test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation coefficients of predictions from Visium and Xenium data. The gray dotted line denotes x=y, and select genes corresponding to (C) labeled. C. Representative examples of ground truth and predicted gene expression for HDC , ANKRD30A , AHSP , and GZMK in both the Visium and Xenium datasets. Predicted gene expressions are visualized for the full dataset, while the performance metrics (PCC and normalized rMSE) are computed from the held-out test set only.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Gene Expression, Labeling

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of normalized rMSE for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the normalized rMSE of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Gene Expression

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram of Pearson correlation coefficients for gene expression predictions using Visium and Xenium data with the Visium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. B. Scatterplot comparing PCC values from Visium and Xenium data with the Visium image on the test set, averaged across five models. The gray dotted line denotes x=y. C. Histogram of PCC values for predictions using Visium and Xenium data with the Xenium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. D. Scatterplot comparing PCC values from Visium and Xenium data with the Xenium image on the test set, averaged across five models. The gray dotted line denotes x=y. E. Scatterplot comparing PCC values between Xenium, an increasing amount of sparsity in the Xenium dataset, and the Visium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs. The histogram below denotes the total number of genes used to calculate the mean PCC. F. Scatterplot comparing PCC values between Xenium, an increasing amount of Poisson noise in the Xenium dataset, and the Visium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs. G. Scatterplot comparing PCC values between Visium, various imputation methods on the Visium dataset, and the Xenium results on the test and replicate 2 Xenium data. The dotted line indicates the dataset used, and error bars represent the standard error across five runs.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Gene Expression

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Scatterplots of normalized rMSE for models trained on varied molecular inputs, evaluated on the held-out test set and averaged across five independent runs, using (A) the Visium histology image and (B) the Xenium histology image. The gray dotted line denotes x=y.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Violin plots of the per-patch fraction of zero counts in Visium and Xenium molecular data. The shape of each violin reflects the density of values along the y-axis, and the overlaid boxplot indicates the median and the 25th and 75th percentiles.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using Visium data with the Visium and Xenium images. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation coefficients of predictions from Visium data with the Visium and Xenium images, based on the test set and averaged over five models. The gray dotted line denotes x=y. C. Histogram showing the distribution of Pearson correlation coefficients for gene expression predictions using the Xenium data with the Visium and Xenium image. The dotted vertical line denotes the mean PCC, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. D. Scatterplot comparing the Pearson correlation coefficients of predictions from Xenium data with the Visium and Xenium image, based on the test set and averaged over five models. The gray dotted line denotes x=y. E. Scatterplot of mean Pearson correlation coefficients on both the test set and the Replicate 2 Xenium section, comparing the Xenium, Xenium images with increasing Gaussian blur, and Visium results (all applied with the same blur levels). The dotted line indicates the dataset used, and error bars represent the standard error of the mean across five independent model runs. F. Grad-CAM heatmaps for two select genes: CD4 (T-cell marker) and PDGFRA (fibroblast marker).

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Gene Expression, Marker

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: Scatterplots of normalized RMSE for models trained on varied image inputs, evaluated on the held-out test set and averaged across five independent runs, using (A) the Visium molecular data and (B) the Xenium molecular data. The gray dotted line denotes x=y.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques:

Journal: bioRxiv

Article Title: Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images

doi: 10.1101/2025.09.04.674228

Figure Lengend Snippet: A. Histogram showing the distribution of Pearson correlation for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the Pearson correlation of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y. C. Histogram showing the distribution of normalized rMSE for gene expression predictions using Visium and Xenium data. The dotted vertical line denotes the mean rMSE, and the solid curved line traces the density estimate. Results are computed on the test set and represent the average performance across five independently trained models. B. Scatterplot comparing the normalized rMSE of predictions from Visium and Xenium data, based on the test set and averaged over five models. The gray dotted line denotes x=y.

Article Snippet: Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images.

Techniques: Gene Expression

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A HE-stained image of the Visium tissue section and adjacent Xenium section, alongside their alignment. One repeat is performed for the publicly available data. B Pearson correlation between FineST and iStar for all input genes, calculated after aggregating super-resolution data to spot resolution. C Spatial expression plots for OPRPN from left to right: Visium, Xenium, FineST and iStar. FineST enhances the signal relative to Visium and yields results more comparable to Xenium. D Pearson correlation for OPRPN in FineST, corresponding to panel ( C ). Each dot represents a Visium spot ( n = 4992) or overlapping Xenium pseudo-spot ( n = 3958). E Ground-truth cell type annotations at spot (Visium) and single-cell (Xenium) resolution, as reported previously . F FineST's predicted cell types at single-nucleus and sub-spot levels. G FineST accurately identifies the DCIS 2 cell type in a triple-positive receptor ROI. H Pearson correlation for cell type abundance and mean gene expression across each cell type, comparing FineST and iStar. Each dot represents a cell type (Left, n = 19) or gene (Right, n = 65), lines connect matched pairs. Diamond indicates the mean. Statistical significance was assessed by a paired two-sided t -test. I Three marked regions (ROI 1, ROI 2 and ROI 3) dominated by DCIS 1, DCIS 2 and Invasive tumor cells. J Cell type deconvolution from FineST, compared with Xenium ground truth, demonstrates FineST's results visually match the ground truth and outperform Visium’s lower resolution (see Supplementary Fig. ). Alongside, single-cell resolved CCC patterns identified using SparseAEH (cluster number 2) and pathway enrichment analysis for Pattern 0 correspond to interesting cell distributions. K Venn plot of significant LR pairs (FDR < 0.05) interacting in >25% (ROI 1, 5589 cells) or >20% (ROI 2, 3330 cells; ROI 3, 5853 cells) of cells. In total, 103, 146 and 159 pairs were selected for spatial clustering analysis in the three ROIs, respectively. L Comparative analysis of region- and cell-specific LR pairs reveals two unique pairs specific to DCIS 2 and Invasive tumor cells. For panels ( B ), ( H – J ), source data are provided in the Source Data file. Scare bars, 1 mm.

Article Snippet: The raw and processed Visium spatial sequencing data of human hepatocellular carcinoma (HCC) tissues were downloaded from Mendeley Data under accession number skrx2fz79n [ https://data.mendeley.com/datasets/skrx2fz79n/1 ], while the cell type annotations and high-resolution HE-stained images were provided by the original corresponding author.

Techniques: Staining, Expressing, Single Cell, Gene Expression

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A , B Spatial plot of seven cell types estimated by cell2location (spot resolution) and FineST (single-cell resolution). A' , B' Zoon-in views of A and B . B'' Zoon-in view of the region marked in B' , where FineST increases resolution from 8 spots to 869 single cells. C Cell type composition from reference scRNA-seq and deconvolution at Visium spot and FineST single-cell levels. D Distribution and PCC of cell type proportions ( n = 7) for Visium spot (PCC = 0.24) and FineST single-cell (PCC = 0.64) resolutions vs reference scRNA-seq. E Pathologists identified 36 spots (of 1331) co-localized with tertiary lymphoid structure (TLS), important for antigen presentation and T cell activation. FineST's single-cell views validate TLS by T and B cell co-localization (see B'' ). F Three example spatially co-expressed LR pairs detected at FineST's single-cell resolution. G Visualization of PVR - TIGIT interaction among local single cells ( z -score FDR < 0.05). H Clustering of 633 significant LR pairs, from single-cell resolution, within selected ROI into three spatial patterns using SpatialDE. I Spatial co-localization of tumor and Treg cells, estimated by cell2location . J Spatially co-expressed LR pairs detected at FineST's single-cell resolution. Scatter plot of global Moran’s R and one-sided z -score p -values (orange: significant, FDR < 0.05, Benjamini-Hochberg correction), with examples highlighted. K Communication strength of CD70 - CD27 at single-cell resolution (color: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1-{p}_{{{{\rm{local}}}}{z}_{p}}$$\end{document} 1 − p local z p ; mean strength: 0.45, interacting cells: 5961, occupancy: 49.2%). L Detection of local CCC patterns by clustering significant LR pairs. Top: original spot resolution (332 pairs, SpatialDE); Middle: sub-spot resolution (957 pairs, SparseAEH) and Bottom: single-nucleus resolution (931 pairs, SparseAEH). M Dot plots of enriched pathways in Pattern 0 and Pattern 1 (from L , Bottom), 2-by-2 contingency tables for MHC-I and WNT. Statistical significance was assessed using a one-sided Fisher’s exact test; dot size indicates p -value. N Sankey plot of selected L-R-TF-TG communication pathways. For panels (D , K ), box plots show median (center), IQR (box), whiskers at 1.5 × IQR, and points for seven cell types; violin plots show density, median (white line), and IQR (thick bar). For panels ( A ), ( J , L ), source data are provided in the Source Data file.

Article Snippet: The raw and processed Visium spatial sequencing data of human hepatocellular carcinoma (HCC) tissues were downloaded from Mendeley Data under accession number skrx2fz79n [ https://data.mendeley.com/datasets/skrx2fz79n/1 ], while the cell type annotations and high-resolution HE-stained images were provided by the original corresponding author.

Techniques: Single Cell, Immunopeptidomics, Activation Assay

Journal: Nature Communications

Article Title: FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis

doi: 10.1038/s41467-026-70528-7

Figure Lengend Snippet: A , B HE staining and cell type annotation of spatial transcriptomic spots in tumor tissues from an ICB non-responder (P1_T) and responder (P7_T) from the original study . In the non-responder (P1_T), a tumor immune barrier (TIB) structure is formed by SPP1 + macrophages and cancer-associated fibroblasts (CAFs). C , D Spatial signature score of SPP1 + macrophages and CAFs, spatial gene expression of CCR1 , and Pearson correlation between SPP1 + macrophage score and CCR1 expression across all spots (Visium vs FineST). The red line represents the fitted linear regression, and the shaded area corresponds to the 95% confidence interval. Statistical significance was assessed using a two-sided Pearson correlation test. E Scatterplot of global Moran’s R and one-sided z -score p -values for spatially co-expressed LR pairs detected using FineST-enhanced gene expression at spot-level resolution in ROI. Significant pairs (orange) were identified using FDR < 0.05 (Benjamini-Hochberg correction). The CD274-PDCD1 interaction was detected in P1_T only. F Spatial gene expression of ligand CD274 and receptor PDCD1 across all spots (Visium vs FineST) for P1_T (non-responder) and P7_T (responder), respectively. For panels ( A , B , E ), source data are provided in the Source Data file. Part of the panels ( A , B ) is created in BioRender. Huang, Y. (2026) https://BioRender.com/fv0byvk .

Article Snippet: The raw and processed Visium spatial sequencing data of human hepatocellular carcinoma (HCC) tissues were downloaded from Mendeley Data under accession number skrx2fz79n [ https://data.mendeley.com/datasets/skrx2fz79n/1 ], while the cell type annotations and high-resolution HE-stained images were provided by the original corresponding author.

Techniques: Staining, Gene Expression, Expressing