spatial transcriptomics sequencing data  (Spatial Transcriptomics Inc)

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

Journal: Journal of Translational Medicine

Article Title: SGMS2+ macrophages enhance NR4A3hi NK cell infiltration to improve prognosis and PD-1 treatment efficacy in hepatocellular carcinoma

doi: 10.1186/s12967-025-07040-x

Figure Lengend Snippet: Investigations on SGMS2—related Cellular and Molecular Interactions in Hepatocellular Carcinoma. a Western blotting analysis of SGMS2 expression in THP—1 cells and differentiated macrophages. Each experiment was independently repeated three times. b , c Apoptosis levels of Huh7 tumor cells co—cultured with control macrophages and SGMS2—overexpressing macrophages were detected by flow cytometry (FCM). d Expression of SGMS2 in spatial transcriptomics sequencing data. e Abundance estimation of the CD56dimCD16highNR4A3high NK cell population by single—sample gene—set enrichment analysis (ssGSEA). f Multiplex immunofluorescence (mIF) images of SGMS2, CD68, CD16, CD56, and NR4A3 markers in 6 human HCC tissue samples. “Zoom macrophage” indicates the aggregation area of SGMS2—positive macrophages, and “Zoom NK cell” represents the CD56dimCD16highNR4A3high NK cells. The scale bar is 50 um or 20 um. g Scatter plots showing the density of CD56dimCD16highNR4A3high NK cells between patients with high and low infiltration of SGMS2—positive macrophages. Statistical analysis was performed using the Mann—Whitney U test. h Pearson correlation analysis of the density of CD56dimCD16highNR4A3high NK cells and the density of SGMS2—positive macrophages. i Kaplan—Meier analysis of OS, RFS, and early RFS in HCC patients with different infiltration densities of SGMS2—positive macrophages and CD56dimCD16highNR4A3high NK cells. Survival distributions were compared using the log—rank test. Statistical significance is indicated as follows: * P < 0.05, ** P < 0.01, *** P < 0.001; ns indicates no significant difference

Article Snippet: Spatial transcriptomics sequencing data were obtained from http://lifeome.net/supp/livercancer-st/data.htm and analyzed using Seurat in R. Subsequently, SCTtransform normalization was performed.

Techniques: Western Blot, Expressing, Cell Culture, Control, Flow Cytometry, Sequencing, Multiplex Assay, Immunofluorescence, MANN-WHITNEY