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10x visium spatial transcriptomics data (Spatial Transcriptomics Inc)

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Spatial Transcriptomics Inc 10x visium spatial transcriptomics data

(a) Overview of the clustering approach and characterization of identified regions. i) Features are extracted from each tiles of H&E slides using the histology foundation model H-optimus-0. ii) Tile features from each slide are clustered using K-means, trained on the discovery cohort (cohort A) and applied to the validation cohorts (cohorts B and C). iii) Expert neuropathologists review and annotate each cluster to define distinct regions. (b) Methods overview. Left : Graphical representation of the methods used to align paired H&E and <t>Visium</t> spatial <t>transcriptomics</t> for cell type annotation in 31 samples from Cohort B from the MOSAIC consortium. Right: Overview of the method used to categorize tiles according to their tile score and tile prediction in 3 classes: high score + high prediction = Long Survival, high score + low prediction = Short Survival, all others = Non-informative. (c) Schematic representation of common spatial arrangements of the regions within tumor sections. (d) Proportion of tiles associated with short survival, long survival, and non-informative categories in the validation cohort in the different regions (excluding the 4 regions with necrosis, hemorrhage, and artefacts) (cohorts B and C). (e) Distribution of regions among Long Survival and Short Survival tiles in the validation cohorts (cohorts B and C). (f) Average of cell count per cell type in the different tissue regions as estimated using the output weights of the cell2loc deconvolution algorithm and 31 samples from cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq. Rare cell types (median counts < 0.1) were excluded.

10x Visium Spatial Transcriptomics Data, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma"

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

Journal: bioRxiv

doi: 10.1101/2025.10.08.681087

Figure Legend Snippet: (a) Overview of the clustering approach and characterization of identified regions. i) Features are extracted from each tiles of H&E slides using the histology foundation model H-optimus-0. ii) Tile features from each slide are clustered using K-means, trained on the discovery cohort (cohort A) and applied to the validation cohorts (cohorts B and C). iii) Expert neuropathologists review and annotate each cluster to define distinct regions. (b) Methods overview. Left : Graphical representation of the methods used to align paired H&E and Visium spatial transcriptomics for cell type annotation in 31 samples from Cohort B from the MOSAIC consortium. Right: Overview of the method used to categorize tiles according to their tile score and tile prediction in 3 classes: high score + high prediction = Long Survival, high score + low prediction = Short Survival, all others = Non-informative. (c) Schematic representation of common spatial arrangements of the regions within tumor sections. (d) Proportion of tiles associated with short survival, long survival, and non-informative categories in the validation cohort in the different regions (excluding the 4 regions with necrosis, hemorrhage, and artefacts) (cohorts B and C). (e) Distribution of regions among Long Survival and Short Survival tiles in the validation cohorts (cohorts B and C). (f) Average of cell count per cell type in the different tissue regions as estimated using the output weights of the cell2loc deconvolution algorithm and 31 samples from cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq. Rare cell types (median counts < 0.1) were excluded.

Techniques Used: Biomarker Discovery, Cell Counting

(a) Distributions of the number of nuclei per tile and the median nuclear area per tile across 457 samples in cohort A (training dataset). Only tiles with a tile score > 0 were included. Purple lines denote thresholds optimized on the training set; arrows indicate the direction associated with shorter survival. *** indicates p value < 0.001 for a Mann Whitney U test between two boxplots. (b) Proportion of biomarker-positive tiles defined as infiltrated white matter (liWM) regions with >30 nuclei and a median nuclear area <40 µm² among all tumor tiles in short- and long-surviving patients from cohorts B and C (testing dataset). (c) Representative H&E slide and corresponding Visium spatial transcriptomics overlay (validation dataset) showing biomarker positive liWM tiles (dark purple) in comparison to adjacent liWM tissue (purple). (d) Cell type composition per tile in biomarker-positive liWM tiles versus non informative liWM tiles in the n=31 patients of the MOSAIC dataset. Cell types with median count per tile <0.1 were excluded for clarity. (e) Pathways enriched in short survival liWM vs non informative liWM. Raster plot of GSEA showing leading genes of each pathways by log2FoldChange. Only pathways with FDR<0.2 were included (See GSEA results and all genes in Supplementary table I and Table J [Supplementary material]). (f) Volcano plot for DEA of short survival liWM vs non informative liWM.

Figure Legend Snippet: (a) Distributions of the number of nuclei per tile and the median nuclear area per tile across 457 samples in cohort A (training dataset). Only tiles with a tile score > 0 were included. Purple lines denote thresholds optimized on the training set; arrows indicate the direction associated with shorter survival. *** indicates p value < 0.001 for a Mann Whitney U test between two boxplots. (b) Proportion of biomarker-positive tiles defined as infiltrated white matter (liWM) regions with >30 nuclei and a median nuclear area <40 µm² among all tumor tiles in short- and long-surviving patients from cohorts B and C (testing dataset). (c) Representative H&E slide and corresponding Visium spatial transcriptomics overlay (validation dataset) showing biomarker positive liWM tiles (dark purple) in comparison to adjacent liWM tissue (purple). (d) Cell type composition per tile in biomarker-positive liWM tiles versus non informative liWM tiles in the n=31 patients of the MOSAIC dataset. Cell types with median count per tile <0.1 were excluded for clarity. (e) Pathways enriched in short survival liWM vs non informative liWM. Raster plot of GSEA showing leading genes of each pathways by log2FoldChange. Only pathways with FDR<0.2 were included (See GSEA results and all genes in Supplementary table I and Table J [Supplementary material]). (f) Volcano plot for DEA of short survival liWM vs non informative liWM.

Techniques Used: MANN-WHITNEY, Biomarker Discovery, Comparison

(a). In cohort A, proportion of tiles associated with short survival, long survival, and non-informative categories in pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1. (b). Within pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1, distributions of nuclear morphology features like nuclear density (number of nuclei per tile) and nuclear size (median nuclear area in µm²) and median nuclear circularity. (c) Representative images of the annotated vessels subtypes patterns on H&E slides (20X): (i) thin endothelium capillary (TEC), (ii) hyperplasic endothelium capillary (HEC), (iii) Microvascular Proliferation (MVP) (iv) Thin Endothelium Wide Lumen (TEWL) (v) Hyperplasic Endothelium Wide Lumen (HEWL) (d) Relative ratios of tiles with vessels or specific vessels subtypes within tumor regions (1.0, 1.1 and 2.1) for each patients across the two survival group (2y < OS and OS > 3y). Distributions are computed for the training set (cohort A) and validation set (cohort B&C). Vessels presence and vessels subtypes detection are obtained after inference by the vessels detection and vessels subtypes classifier models on the entire cohorts. (e) Distribution of cell count per cell type in the tumor regions (1.0, 1.1 and 2.1) for short-survival, long-survival and non-informative tiles. Distributions of cell counts statistically significantly different (Mann–Whitney test; threshold at 0.05) are indicated with a star. 31 samples from Cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq were used. Cell types with low range of mean count variations across tissue regions were removed for clarity. (f) Illustration of the pathway enrichment analyses of the differential expressed genes between long- and short-survival. Negative fold change is associated with worse survival outcomes and positive with better survival outcomes. (See GSEA results and all genes in supplementary table L [Supplementary material])

Figure Legend Snippet: (a). In cohort A, proportion of tiles associated with short survival, long survival, and non-informative categories in pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1. (b). Within pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1, distributions of nuclear morphology features like nuclear density (number of nuclei per tile) and nuclear size (median nuclear area in µm²) and median nuclear circularity. (c) Representative images of the annotated vessels subtypes patterns on H&E slides (20X): (i) thin endothelium capillary (TEC), (ii) hyperplasic endothelium capillary (HEC), (iii) Microvascular Proliferation (MVP) (iv) Thin Endothelium Wide Lumen (TEWL) (v) Hyperplasic Endothelium Wide Lumen (HEWL) (d) Relative ratios of tiles with vessels or specific vessels subtypes within tumor regions (1.0, 1.1 and 2.1) for each patients across the two survival group (2y < OS and OS > 3y). Distributions are computed for the training set (cohort A) and validation set (cohort B&C). Vessels presence and vessels subtypes detection are obtained after inference by the vessels detection and vessels subtypes classifier models on the entire cohorts. (e) Distribution of cell count per cell type in the tumor regions (1.0, 1.1 and 2.1) for short-survival, long-survival and non-informative tiles. Distributions of cell counts statistically significantly different (Mann–Whitney test; threshold at 0.05) are indicated with a star. 31 samples from Cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq were used. Cell types with low range of mean count variations across tissue regions were removed for clarity. (f) Illustration of the pathway enrichment analyses of the differential expressed genes between long- and short-survival. Negative fold change is associated with worse survival outcomes and positive with better survival outcomes. (See GSEA results and all genes in supplementary table L [Supplementary material])

Techniques Used: Biomarker Discovery, Cell Counting, MANN-WHITNEY

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a . A Xenium ST data from human breast carcinoma with single-cell level gene expression of 313 genes in 167,780 cells and an H&E image. Standard NMF is applied to derive a single-cell level embedding as the silver standard high-resolution embedding in the simulation. b . Generation of spots and spot-level gene expression. Spots of radius r are first generated based on a 10x Visium-style mesh grid, and spot-level gene expression is generated by summing cell expressions within spots. A proportion ρ of the spots are randomly excluded to increase the spatial sparsity. c . Error bar plots of MAEs between the silver standard and inferred embedding intensities at the pixel-wise level when varying the exclusion rate and radius of spots with n = 30 (replicates). Error bars: mean ± SD. d . Left panel: a tumour-associated embedding dimension in the single-cell level silver standard. Right panel: Zoomed-in views of three ROIs in the left panel. e . Zoomed-in views of the inferred embedding dimensions by different methods from a simulation dataset (spot radius: 30 pixels) in the ROIs under whole-spots and spots-masking scenarios. The whole-spots scenario means that expressions of all spots of the simulation dataset are used for embedding learning, and the spots-masking scenario means that the ST expressions in the light blue boxes are masked for embedding learning. Scale bars: 200 μm.

Journal: Nature Cell Biology

Article Title: The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics

doi: 10.1038/s41556-025-01838-z

Figure Lengend Snippet: a . A Xenium ST data from human breast carcinoma with single-cell level gene expression of 313 genes in 167,780 cells and an H&E image. Standard NMF is applied to derive a single-cell level embedding as the silver standard high-resolution embedding in the simulation. b . Generation of spots and spot-level gene expression. Spots of radius r are first generated based on a 10x Visium-style mesh grid, and spot-level gene expression is generated by summing cell expressions within spots. A proportion ρ of the spots are randomly excluded to increase the spatial sparsity. c . Error bar plots of MAEs between the silver standard and inferred embedding intensities at the pixel-wise level when varying the exclusion rate and radius of spots with n = 30 (replicates). Error bars: mean ± SD. d . Left panel: a tumour-associated embedding dimension in the single-cell level silver standard. Right panel: Zoomed-in views of three ROIs in the left panel. e . Zoomed-in views of the inferred embedding dimensions by different methods from a simulation dataset (spot radius: 30 pixels) in the ROIs under whole-spots and spots-masking scenarios. The whole-spots scenario means that expressions of all spots of the simulation dataset are used for embedding learning, and the spots-masking scenario means that the ST expressions in the light blue boxes are masked for embedding learning. Scale bars: 200 μm.

Article Snippet: 10x Visium mouse posterior brain sagittal section data are available for MPBS-01 at https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-1-sagittal-posterior-1-standard-1-1-0 and for MPBS-02 at https://www.10xgenomics.com/resources/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard-1-1-0 .

Techniques: Single Cell, Gene Expression, Generated

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a) Overview of the clustering approach and characterization of identified regions. i) Features are extracted from each tiles of H&E slides using the histology foundation model H-optimus-0. ii) Tile features from each slide are clustered using K-means, trained on the discovery cohort (cohort A) and applied to the validation cohorts (cohorts B and C). iii) Expert neuropathologists review and annotate each cluster to define distinct regions. (b) Methods overview. Left : Graphical representation of the methods used to align paired H&E and Visium spatial transcriptomics for cell type annotation in 31 samples from Cohort B from the MOSAIC consortium. Right: Overview of the method used to categorize tiles according to their tile score and tile prediction in 3 classes: high score + high prediction = Long Survival, high score + low prediction = Short Survival, all others = Non-informative. (c) Schematic representation of common spatial arrangements of the regions within tumor sections. (d) Proportion of tiles associated with short survival, long survival, and non-informative categories in the validation cohort in the different regions (excluding the 4 regions with necrosis, hemorrhage, and artefacts) (cohorts B and C). (e) Distribution of regions among Long Survival and Short Survival tiles in the validation cohorts (cohorts B and C). (f) Average of cell count per cell type in the different tissue regions as estimated using the output weights of the cell2loc deconvolution algorithm and 31 samples from cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq. Rare cell types (median counts < 0.1) were excluded.

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: Biomarker Discovery, Cell Counting

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a) Distributions of the number of nuclei per tile and the median nuclear area per tile across 457 samples in cohort A (training dataset). Only tiles with a tile score > 0 were included. Purple lines denote thresholds optimized on the training set; arrows indicate the direction associated with shorter survival. *** indicates p value < 0.001 for a Mann Whitney U test between two boxplots. (b) Proportion of biomarker-positive tiles defined as infiltrated white matter (liWM) regions with >30 nuclei and a median nuclear area <40 µm² among all tumor tiles in short- and long-surviving patients from cohorts B and C (testing dataset). (c) Representative H&E slide and corresponding Visium spatial transcriptomics overlay (validation dataset) showing biomarker positive liWM tiles (dark purple) in comparison to adjacent liWM tissue (purple). (d) Cell type composition per tile in biomarker-positive liWM tiles versus non informative liWM tiles in the n=31 patients of the MOSAIC dataset. Cell types with median count per tile <0.1 were excluded for clarity. (e) Pathways enriched in short survival liWM vs non informative liWM. Raster plot of GSEA showing leading genes of each pathways by log2FoldChange. Only pathways with FDR<0.2 were included (See GSEA results and all genes in Supplementary table I and Table J [Supplementary material]). (f) Volcano plot for DEA of short survival liWM vs non informative liWM.

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: MANN-WHITNEY, Biomarker Discovery, Comparison

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a). In cohort A, proportion of tiles associated with short survival, long survival, and non-informative categories in pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1. (b). Within pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1, distributions of nuclear morphology features like nuclear density (number of nuclei per tile) and nuclear size (median nuclear area in µm²) and median nuclear circularity. (c) Representative images of the annotated vessels subtypes patterns on H&E slides (20X): (i) thin endothelium capillary (TEC), (ii) hyperplasic endothelium capillary (HEC), (iii) Microvascular Proliferation (MVP) (iv) Thin Endothelium Wide Lumen (TEWL) (v) Hyperplasic Endothelium Wide Lumen (HEWL) (d) Relative ratios of tiles with vessels or specific vessels subtypes within tumor regions (1.0, 1.1 and 2.1) for each patients across the two survival group (2y < OS and OS > 3y). Distributions are computed for the training set (cohort A) and validation set (cohort B&C). Vessels presence and vessels subtypes detection are obtained after inference by the vessels detection and vessels subtypes classifier models on the entire cohorts. (e) Distribution of cell count per cell type in the tumor regions (1.0, 1.1 and 2.1) for short-survival, long-survival and non-informative tiles. Distributions of cell counts statistically significantly different (Mann–Whitney test; threshold at 0.05) are indicated with a star. 31 samples from Cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq were used. Cell types with low range of mean count variations across tissue regions were removed for clarity. (f) Illustration of the pathway enrichment analyses of the differential expressed genes between long- and short-survival. Negative fold change is associated with worse survival outcomes and positive with better survival outcomes. (See GSEA results and all genes in supplementary table L [Supplementary material])

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: Biomarker Discovery, Cell Counting, MANN-WHITNEY

HBC 10x Visium data: (a) The spatial domains identified by BISON and competing methods. (b) Heatmap of gene groups identified by BISON. Pattern 0 represents the non-DGs.

Journal: Bioinformatics

Article Title: BISON: bi-clustering of spatial omics data with feature selection

doi: 10.1093/bioinformatics/btaf495

Figure Lengend Snippet: HBC 10x Visium data: (a) The spatial domains identified by BISON and competing methods. (b) Heatmap of gene groups identified by BISON. Pattern 0 represents the non-DGs.

Article Snippet: The mouse olfactory bulb (MOB) spatial transcriptomics (ST) data were obtained from the public domain through the Spatial Research Lab ( https://www.spatialresearch.org/resources-published-datasets/doi-10-1126science-aaf2403/ ), and the human breast cancer (HBC) 10x Visium data were obtained from the public domain through 10x Genomics ( https://support.10xgenomics.com/spatial-gene-expression/datasets ).

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