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tissue sections  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc tissue sections
    Tissue Sections, 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
    https://www.bioz.com/product/high+resolution+slide+seq+spatial+transcriptomics+map/sections+tissue/pm41375325-94-15-0
    Average 86 stars, based on 1 article reviews
    tissue sections - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment
    Article Snippet: scRNA-seq , Analyzing Transcriptomic Features at the Single-Cell Level , (1) Reveals cellular heterogeneity and subpopulation structure; (2) Identifies rare cells and transient state cells; (3) Suitable for developmental trajectory and cell lineage tracing analysis; (4) Constructs intercellular communication networks. , (1) High cost and limited sequencing depth; (2) Sparse data, high noise levels, small sample size, and limited representativeness; (3) Difficulty in preserving spatial information and disruption of tissue structure.. .. Spatial transcriptomics , Transcriptome sequencing preserving spatial information of tissue sections. , (1) Reveals the spatial distribution characteristics of gene expression; (2) Preserves tissue morphology and structural information; (3) Identifies functional regions and intercellular spatial interactions; (4) Aids in understanding the spatial ecology of the tumor microenvironment. , (1) Limited resolution, with partial signal mixing; (2) Large data volume and complex analysis algorithms; (3) High cost and relatively low technological maturity; (4) Difficulty in direct matching with large clinical samples.. .. Bulk RNA-seq + scRNA-seq , CIBERSORT MuSiC SCDC EPIC , Reference-based deconvolution using scRNA-seq-derived cell-type signatures , Immune composition profiling; Prognostic modeling; Cohort stratification..

    Article Title: Netrin-1 blockade inhibits tumour growth and EMT features in endometrial cancer.
    Article Snippet: .. Spatial transcriptomics using Visium FFPE technology FFPE tissue sections were placed on Visium slides and prepared according to the 10x Genomics protocols. .. After H&E staining, imaging and de-crosslinking steps, tissue sections were incubated with human-specific probes targeting 17,943 genes (10x Genomics, Visium Human Transcriptome Probe Set v.1.0).

    Sequencing:

    Article Title: Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment
    Article Snippet: scRNA-seq , Analyzing Transcriptomic Features at the Single-Cell Level , (1) Reveals cellular heterogeneity and subpopulation structure; (2) Identifies rare cells and transient state cells; (3) Suitable for developmental trajectory and cell lineage tracing analysis; (4) Constructs intercellular communication networks. , (1) High cost and limited sequencing depth; (2) Sparse data, high noise levels, small sample size, and limited representativeness; (3) Difficulty in preserving spatial information and disruption of tissue structure.. .. Spatial transcriptomics , Transcriptome sequencing preserving spatial information of tissue sections. , (1) Reveals the spatial distribution characteristics of gene expression; (2) Preserves tissue morphology and structural information; (3) Identifies functional regions and intercellular spatial interactions; (4) Aids in understanding the spatial ecology of the tumor microenvironment. , (1) Limited resolution, with partial signal mixing; (2) Large data volume and complex analysis algorithms; (3) High cost and relatively low technological maturity; (4) Difficulty in direct matching with large clinical samples.. .. Bulk RNA-seq + scRNA-seq , CIBERSORT MuSiC SCDC EPIC , Reference-based deconvolution using scRNA-seq-derived cell-type signatures , Immune composition profiling; Prognostic modeling; Cohort stratification..

    Preserving:

    Article Title: Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment
    Article Snippet: scRNA-seq , Analyzing Transcriptomic Features at the Single-Cell Level , (1) Reveals cellular heterogeneity and subpopulation structure; (2) Identifies rare cells and transient state cells; (3) Suitable for developmental trajectory and cell lineage tracing analysis; (4) Constructs intercellular communication networks. , (1) High cost and limited sequencing depth; (2) Sparse data, high noise levels, small sample size, and limited representativeness; (3) Difficulty in preserving spatial information and disruption of tissue structure.. .. Spatial transcriptomics , Transcriptome sequencing preserving spatial information of tissue sections. , (1) Reveals the spatial distribution characteristics of gene expression; (2) Preserves tissue morphology and structural information; (3) Identifies functional regions and intercellular spatial interactions; (4) Aids in understanding the spatial ecology of the tumor microenvironment. , (1) Limited resolution, with partial signal mixing; (2) Large data volume and complex analysis algorithms; (3) High cost and relatively low technological maturity; (4) Difficulty in direct matching with large clinical samples.. .. Bulk RNA-seq + scRNA-seq , CIBERSORT MuSiC SCDC EPIC , Reference-based deconvolution using scRNA-seq-derived cell-type signatures , Immune composition profiling; Prognostic modeling; Cohort stratification..

    Gene Expression:

    Article Title: Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment
    Article Snippet: scRNA-seq , Analyzing Transcriptomic Features at the Single-Cell Level , (1) Reveals cellular heterogeneity and subpopulation structure; (2) Identifies rare cells and transient state cells; (3) Suitable for developmental trajectory and cell lineage tracing analysis; (4) Constructs intercellular communication networks. , (1) High cost and limited sequencing depth; (2) Sparse data, high noise levels, small sample size, and limited representativeness; (3) Difficulty in preserving spatial information and disruption of tissue structure.. .. Spatial transcriptomics , Transcriptome sequencing preserving spatial information of tissue sections. , (1) Reveals the spatial distribution characteristics of gene expression; (2) Preserves tissue morphology and structural information; (3) Identifies functional regions and intercellular spatial interactions; (4) Aids in understanding the spatial ecology of the tumor microenvironment. , (1) Limited resolution, with partial signal mixing; (2) Large data volume and complex analysis algorithms; (3) High cost and relatively low technological maturity; (4) Difficulty in direct matching with large clinical samples.. .. Bulk RNA-seq + scRNA-seq , CIBERSORT MuSiC SCDC EPIC , Reference-based deconvolution using scRNA-seq-derived cell-type signatures , Immune composition profiling; Prognostic modeling; Cohort stratification..

    Functional Assay:

    Article Title: Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment
    Article Snippet: scRNA-seq , Analyzing Transcriptomic Features at the Single-Cell Level , (1) Reveals cellular heterogeneity and subpopulation structure; (2) Identifies rare cells and transient state cells; (3) Suitable for developmental trajectory and cell lineage tracing analysis; (4) Constructs intercellular communication networks. , (1) High cost and limited sequencing depth; (2) Sparse data, high noise levels, small sample size, and limited representativeness; (3) Difficulty in preserving spatial information and disruption of tissue structure.. .. Spatial transcriptomics , Transcriptome sequencing preserving spatial information of tissue sections. , (1) Reveals the spatial distribution characteristics of gene expression; (2) Preserves tissue morphology and structural information; (3) Identifies functional regions and intercellular spatial interactions; (4) Aids in understanding the spatial ecology of the tumor microenvironment. , (1) Limited resolution, with partial signal mixing; (2) Large data volume and complex analysis algorithms; (3) High cost and relatively low technological maturity; (4) Difficulty in direct matching with large clinical samples.. .. Bulk RNA-seq + scRNA-seq , CIBERSORT MuSiC SCDC EPIC , Reference-based deconvolution using scRNA-seq-derived cell-type signatures , Immune composition profiling; Prognostic modeling; Cohort stratification..

    Imaging:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    Fluorescence:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    In Situ Hybridization:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    Fluorescence In Situ Hybridization:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    In Situ:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    Labeling:

    Article Title: Single-Cell Omics in Legumes: Research Trends and Applications.
    Article Snippet: .. Spatial transcriptomics can be categorized into (1) RNA capture-based approaches (e.g., 10× Visium, Slide-seq) where tissue sections are placed on slides patterned with spatially barcoded primers that capture RNA molecules and tag them to record their spatial locations; (2) in probe- or imaging-based approaches [e.g., multiplexed error-robust fluorescence in situ hybridization (MERFISH), sequential FISH (seqFISH), Xenium in situ], where fluorescently labeled probes hybridize target transcripts on a tissue cross-section, enabling direct visualization of transcripts locations at high spatial resolution [71]. ..

    Formalin-fixed Paraffin-Embedded:

    Article Title: Netrin-1 blockade inhibits tumour growth and EMT features in endometrial cancer.
    Article Snippet: .. Spatial transcriptomics using Visium FFPE technology FFPE tissue sections were placed on Visium slides and prepared according to the 10x Genomics protocols. .. After H&E staining, imaging and de-crosslinking steps, tissue sections were incubated with human-specific probes targeting 17,943 genes (10x Genomics, Visium Human Transcriptome Probe Set v.1.0).



    Similar Products

    high resolution slide seq spatial transcriptomics map  (Spatial Transcriptomics Inc)
    86
    Spatial Transcriptomics Inc high resolution slide seq spatial transcriptomics map
    Fig. 1 | Single-cell and spatial <t>transcriptomics</t> of cardiac and ileum tissue of reovirus-infected neonatal mice. a, Experiment and analysis workflow. Four- day-old neonatal mice weighing 3 g per pup were infected (per os) with reovirus T1L. Neonatal mice infected with 1× PBS were used as mock controls. Ileum tissue (1 dpi and 4 dpi) and heart tissues (4 dpi, 7 dpi and 10 dpi) were assayed and used for scRNA-seq and spatial transcriptomics. b, UMAP plot of 31,684 single-cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi (one animal per condition), clustered by gene expression and colored by cell type (left). UMAP plots showing cardiac cell type clusters across samples for the heart scRNA-seq data (right). c, 8,243 spatial transcriptomes of cardiac tissue sections from mock-infected and reovirus-infected mice at 4 dpi and 7 dpi (one animal per condition). H&E-stained image of reovirus-infected myocarditic
    High Resolution Slide Seq Spatial Transcriptomics Map, 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
    https://www.bioz.com/product/high+resolution+slide+seq+spatial+transcriptomics+map/seq+slide/pm36970396-158-20-22
    Average 86 stars, based on 1 article reviews
    high resolution slide seq spatial transcriptomics map - by Bioz Stars, 2026-09
    86/100 stars
    		  Buy from Supplier

    Image Search Results


    Fig. 1 | Single-cell and spatial transcriptomics of cardiac and ileum tissue of reovirus-infected neonatal mice. a, Experiment and analysis workflow. Four- day-old neonatal mice weighing 3 g per pup were infected (per os) with reovirus T1L. Neonatal mice infected with 1× PBS were used as mock controls. Ileum tissue (1 dpi and 4 dpi) and heart tissues (4 dpi, 7 dpi and 10 dpi) were assayed and used for scRNA-seq and spatial transcriptomics. b, UMAP plot of 31,684 single-cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi (one animal per condition), clustered by gene expression and colored by cell type (left). UMAP plots showing cardiac cell type clusters across samples for the heart scRNA-seq data (right). c, 8,243 spatial transcriptomes of cardiac tissue sections from mock-infected and reovirus-infected mice at 4 dpi and 7 dpi (one animal per condition). H&E-stained image of reovirus-infected myocarditic

    Journal: Nature cardiovascular research

    Article Title: Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis.

    doi: 10.1038/s44161-022-00138-1

    Figure Lengend Snippet: Fig. 1 | Single-cell and spatial transcriptomics of cardiac and ileum tissue of reovirus-infected neonatal mice. a, Experiment and analysis workflow. Four- day-old neonatal mice weighing 3 g per pup were infected (per os) with reovirus T1L. Neonatal mice infected with 1× PBS were used as mock controls. Ileum tissue (1 dpi and 4 dpi) and heart tissues (4 dpi, 7 dpi and 10 dpi) were assayed and used for scRNA-seq and spatial transcriptomics. b, UMAP plot of 31,684 single-cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi (one animal per condition), clustered by gene expression and colored by cell type (left). UMAP plots showing cardiac cell type clusters across samples for the heart scRNA-seq data (right). c, 8,243 spatial transcriptomes of cardiac tissue sections from mock-infected and reovirus-infected mice at 4 dpi and 7 dpi (one animal per condition). H&E-stained image of reovirus-infected myocarditic

    Article Snippet: UMAP plot showing the expression of myocyte-specific genes that are upregulated in the border zone of myocarditic regions (right). g, High-resolution Slide-seq spatial transcriptomics map of cardiac ventricular tissue from reovirus infected mice at 7 dpi colored by Slide-seq bead clusters.

    Techniques: Infection, Gene Expression, Staining

    Fig. 3 | Cytotoxic T cells recruited by inflamed endothelial cells induce pyroptosis in myocarditic tissue. a, UMAP plot of 9,786 single-cell endothelial cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by endothelial cell subtype clusters (phenotypes) (top) and condition (bottom). b, Heat map showing top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for endothelial cell subtypes. c, UMAP plot showing the expression of genes upregulated in Cxcl9-high endothelial cells. d, Spatial transcriptomic maps of cardiac tissue from reovirus infected hearts at 7 dpi showing gene module scores calculated for four GO terms enriched in Cxcl9-high endothelial cells. e, UMAP plot of 2,205 single-cell T cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by T cell subtype clusters (top) and condition (bottom). f, Heat map showing top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for T cell subtypes. g, UMAP plot showing the expression of genes upregulated in cytotoxic T cells from myocarditic heart at 7 dpi. h, Spatial transcriptomics maps of cardiac tissue from reovirus infected hearts at 7 dpi

    Journal: Nature cardiovascular research

    Article Title: Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis.

    doi: 10.1038/s44161-022-00138-1

    Figure Lengend Snippet: Fig. 3 | Cytotoxic T cells recruited by inflamed endothelial cells induce pyroptosis in myocarditic tissue. a, UMAP plot of 9,786 single-cell endothelial cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by endothelial cell subtype clusters (phenotypes) (top) and condition (bottom). b, Heat map showing top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for endothelial cell subtypes. c, UMAP plot showing the expression of genes upregulated in Cxcl9-high endothelial cells. d, Spatial transcriptomic maps of cardiac tissue from reovirus infected hearts at 7 dpi showing gene module scores calculated for four GO terms enriched in Cxcl9-high endothelial cells. e, UMAP plot of 2,205 single-cell T cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by T cell subtype clusters (top) and condition (bottom). f, Heat map showing top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for T cell subtypes. g, UMAP plot showing the expression of genes upregulated in cytotoxic T cells from myocarditic heart at 7 dpi. h, Spatial transcriptomics maps of cardiac tissue from reovirus infected hearts at 7 dpi

    Article Snippet: UMAP plot showing the expression of myocyte-specific genes that are upregulated in the border zone of myocarditic regions (right). g, High-resolution Slide-seq spatial transcriptomics map of cardiac ventricular tissue from reovirus infected mice at 7 dpi colored by Slide-seq bead clusters.

    Techniques: Infection, Expressing

    Fig. 4 | Myocarditic regions and the border zone have distinct transcriptomic profiles and cell-type-specific signatures. a, Spatial transcriptomics map of cardiac tissue section from reovirus-infected mice at 7 dpi colored by spot clusters representing transcriptionally distinct tissue regions. b, Spatial transcriptomics maps of cardiac tissue sections from reovirus-infected mice at 7 dpi showing the expression of differentially expressed genes of interest in the myocarditic and the border zone. c, Changes in average predicted cell type proportions across the infected ventricle, for cell types enriched in the myocarditic region and the border zone. d, UMAP plot of 502 single-cell cardiomyocyte cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by myocyte cell subtype (phenotypes) (left) and condition (right). e, Heat map showing the top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for cardiomyocyte cell subtypes. f, Venn diagram showing myocyte-specific genes

    Journal: Nature cardiovascular research

    Article Title: Spatiotemporal transcriptomics reveals pathogenesis of viral myocarditis.

    doi: 10.1038/s44161-022-00138-1

    Figure Lengend Snippet: Fig. 4 | Myocarditic regions and the border zone have distinct transcriptomic profiles and cell-type-specific signatures. a, Spatial transcriptomics map of cardiac tissue section from reovirus-infected mice at 7 dpi colored by spot clusters representing transcriptionally distinct tissue regions. b, Spatial transcriptomics maps of cardiac tissue sections from reovirus-infected mice at 7 dpi showing the expression of differentially expressed genes of interest in the myocarditic and the border zone. c, Changes in average predicted cell type proportions across the infected ventricle, for cell types enriched in the myocarditic region and the border zone. d, UMAP plot of 502 single-cell cardiomyocyte cell transcriptomes from mock-infected and reovirus-infected hearts at 4 dpi, 7 dpi and 10 dpi colored by myocyte cell subtype (phenotypes) (left) and condition (right). e, Heat map showing the top five differentially expressed genes (two-sided Wilcoxon test, log fold change > 1.0 and P < 0.01) for cardiomyocyte cell subtypes. f, Venn diagram showing myocyte-specific genes

    Article Snippet: UMAP plot showing the expression of myocyte-specific genes that are upregulated in the border zone of myocarditic regions (right). g, High-resolution Slide-seq spatial transcriptomics map of cardiac ventricular tissue from reovirus infected mice at 7 dpi colored by Slide-seq bead clusters.

    Techniques: Infection, Expressing