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single-cell and spatial transcriptome data for cancer tissue  (10X Genomics)

 
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    10X Genomics single-cell and spatial transcriptome data for cancer tissue
    Single Cell And Spatial Transcriptome Data For Cancer Tissue, supplied by 10X Genomics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/single-cell+spatial+transcriptomics+data/single+cell+and+spatial+transcriptome+data+for+cancer+tissue/pmc12045154-227-7-12
    Average 90 stars, based on 1 article reviews
    single-cell and spatial transcriptome data for cancer tissue - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: A systematic evaluation of state-of-the-art deconvolution methods in spatial transcriptomics: insights from cardiovascular disease and chronic kidney disease
    Article Snippet: These results additionally have been highlighted by 10× Genomics guides for use with Visium data ( https://www.10xgenomics.com/analysis-guides/benchmarking-methods-to-integrate-spatial-and-single-cell-transcriptomics-data ).

    Article Title: Deconvolution of spatial transcriptomics data via graph contrastive learning and partial least square regression.
    Article Snippet: Spatial transcriptomics data Spot Single-cell data Cell Simulated dataset 490 Mouse brain (Smart-seq) 10260 Human dorsolateral prefrontal cortex (DLPFC, 10X Visium) 3639 Human dorsolateral prefrontal cortex (10X Genomics Chromium snRNA-seq) 11594 Mouse hippocampus (Slide-seq V2) 53208 Mouse hippocampus (Drop-seq) 15095 Zebrafish embryo (stereo-seq) 2330 Zebrafish embryo (Droplet based scRNA-seq) 14882 Mouse brain (10X Visium) 1702 Mouse brain cortex (Multi-omics, ISSAAC-seq [42]) 4281 where D (·) is a discriminator composed of dual neural network:Rd× R d → R. Parameter sharing for scRNA-seq data Dimensionality reduction is crucial for scRNA-seq data to extract informative features, as the data is usually noisy and sparse [44, 45].



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    (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

    Journal: bioRxiv

    Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

    doi: 10.1101/2025.07.25.666708

    Figure Lengend Snippet: (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

    Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

    Techniques: RNA Sequencing, Sequencing, RNAscope, Ligation

    (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

    Journal: bioRxiv

    Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

    doi: 10.1101/2025.07.25.666708

    Figure Lengend Snippet: (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

    Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

    Techniques: Genome Wide

    Journal: Cell

    Article Title: Spatiotemporal analysis of human intestinal development at single-cell resolution

    doi: 10.1016/j.cell.2020.12.016

    Figure Lengend Snippet:

    Article Snippet: STAR-FINDer Single Cell and Spatial Transcriptomics Data Portal , This study , https://simmonslab.shinyapps.io/FetalAtlasDataPortal.

    Techniques: Conjugation Assay, Recombinant, Saline, Modification, Plasmid Preparation, Gene Expression, RNAscope, Generated, Software