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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/slide+seq+spatial+transcriptomics+experiment/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).



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    slide seq spatial transcriptomics experiment  (TaKaRa)
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    TaKaRa slide seq spatial transcriptomics experiment
    AIR-SPACE enables the mapping of adaptive immune receptor (AIR) clonotypes and <t>transcriptomics</t> in situ. ( A ) Schematic of the experimental design and methodology, including the generation of long-read (LR) and short-read (SR). ( B ) Spatial mapping of cell types across the LN sections at different time points postinfection. (Scale bar, 500 μm.) ( C ) Spatial mapping of AIR clonotypes across the LN sections, with immunoglobulin (IG) clones shown in blue and T cell receptor (TCR) clones shown in red; outlined with germinal center (GC) regions in LNs from D10PI to D21PI. ( D ) Multiplexed RNA FISH staining for T cell marker Trbc2 (green), B cell marker Ms4a1 (red), and DAPI (blue) across all samples on sister sections. (Scale bar 500 μm.)
    Slide Seq Spatial Transcriptomics Experiment, supplied by TaKaRa, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    slide seq spatial transcriptomics experiment - by Bioz Stars, 2026-09
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    seeker spatial transcriptomics kit  (TaKaRa)
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    AIR-SPACE enables the mapping of adaptive immune receptor (AIR) clonotypes and <t>transcriptomics</t> in situ. ( A ) Schematic of the experimental design and methodology, including the generation of long-read (LR) and short-read (SR). ( B ) Spatial mapping of cell types across the LN sections at different time points postinfection. (Scale bar, 500 μm.) ( C ) Spatial mapping of AIR clonotypes across the LN sections, with immunoglobulin (IG) clones shown in blue and T cell receptor (TCR) clones shown in red; outlined with germinal center (GC) regions in LNs from D10PI to D21PI. ( D ) Multiplexed RNA FISH staining for T cell marker Trbc2 (green), B cell marker Ms4a1 (red), and DAPI (blue) across all samples on sister sections. (Scale bar 500 μm.)
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    Image Search Results


    AIR-SPACE enables the mapping of adaptive immune receptor (AIR) clonotypes and transcriptomics in situ. ( A ) Schematic of the experimental design and methodology, including the generation of long-read (LR) and short-read (SR). ( B ) Spatial mapping of cell types across the LN sections at different time points postinfection. (Scale bar, 500 μm.) ( C ) Spatial mapping of AIR clonotypes across the LN sections, with immunoglobulin (IG) clones shown in blue and T cell receptor (TCR) clones shown in red; outlined with germinal center (GC) regions in LNs from D10PI to D21PI. ( D ) Multiplexed RNA FISH staining for T cell marker Trbc2 (green), B cell marker Ms4a1 (red), and DAPI (blue) across all samples on sister sections. (Scale bar 500 μm.)

    Journal: Proceedings of the National Academy of Sciences of the United States of America

    Article Title: A temporal and spatial atlas of adaptive immune responses in the lymph node following viral infection

    doi: 10.1073/pnas.2504742123

    Figure Lengend Snippet: AIR-SPACE enables the mapping of adaptive immune receptor (AIR) clonotypes and transcriptomics in situ. ( A ) Schematic of the experimental design and methodology, including the generation of long-read (LR) and short-read (SR). ( B ) Spatial mapping of cell types across the LN sections at different time points postinfection. (Scale bar, 500 μm.) ( C ) Spatial mapping of AIR clonotypes across the LN sections, with immunoglobulin (IG) clones shown in blue and T cell receptor (TCR) clones shown in red; outlined with germinal center (GC) regions in LNs from D10PI to D21PI. ( D ) Multiplexed RNA FISH staining for T cell marker Trbc2 (green), B cell marker Ms4a1 (red), and DAPI (blue) across all samples on sister sections. (Scale bar 500 μm.)

    Article Snippet: Slide-seq spatial transcriptomics experiment was performed using the Curio Seeker Kit (Curio Bioscience) according to manufacturer instructions.

    Techniques: In Situ, Clone Assay, Staining, Marker