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stereo seq  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc stereo seq
    Stereo Seq, 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/stereo+seq+spatial+transcriptomics+data/seq+stereo/pm38473208-276-24-25
    Average 86 stars, based on 1 article reviews
    stereo seq - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?
    Article Snippet: .. Stereo-seq (Spatiotemporal Enhanced Resolution Omics-sequencing) is a high-resolution spatial transcriptomics technology that captures genome-wide gene expression with spatial fidelity using DNA nanoball (DNB)-patterned arrays [ ]. ..

    Article Title: Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.
    Article Snippet: .. 2) Image Registration: The fluorescence image taken during the Stereo-Seq Spatial Transcriptomics Assay was aligned to the count matrix to define the tissue area, filtering out DNA nano-balls that were not under the tissue from the count matrix. ..

    Article Title: Macrophage Plasticity and Regulatory Networks During the Transition from Inflammation to Fibrosis in the Kidney
    Article Snippet: .. Xing et al., 2025 , Spatial transcriptomics , Stereo-seq , Whole kidney (single nuclei) , LN , IMQ (N/A) , [ ] . ..

    Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching
    Article Snippet: .. Spatial transcriptomics (ST) technologies, such as Stereo-seq [ ], Xenium [ ], and Visium HD [ ], are transforming our understanding of tissue architecture by mapping gene expression at subcellular resolution across increasingly large fields of view. ..

    Genome Wide:

    Article Title: Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?
    Article Snippet: .. Stereo-seq (Spatiotemporal Enhanced Resolution Omics-sequencing) is a high-resolution spatial transcriptomics technology that captures genome-wide gene expression with spatial fidelity using DNA nanoball (DNB)-patterned arrays [ ]. ..

    Gene Expression:

    Article Title: Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?
    Article Snippet: .. Stereo-seq (Spatiotemporal Enhanced Resolution Omics-sequencing) is a high-resolution spatial transcriptomics technology that captures genome-wide gene expression with spatial fidelity using DNA nanoball (DNB)-patterned arrays [ ]. ..

    Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching
    Article Snippet: .. Spatial transcriptomics (ST) technologies, such as Stereo-seq [ ], Xenium [ ], and Visium HD [ ], are transforming our understanding of tissue architecture by mapping gene expression at subcellular resolution across increasingly large fields of view. ..

    Fluorescence:

    Article Title: Spatial FBA reveals heterogeneous Warburg niches in renal tumors and lactate consumption in colorectal cancer.
    Article Snippet: .. 2) Image Registration: The fluorescence image taken during the Stereo-Seq Spatial Transcriptomics Assay was aligned to the count matrix to define the tissue area, filtering out DNA nano-balls that were not under the tissue from the count matrix. ..

    Imaging:

    Article Title: Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.
    Article Snippet: .. Therefore, two additional types of data, including mFISH (imaging-based molecular data) [64,65] and Stereo-seq (high-resolution spatial transcriptomics data) [66], are selected (Table A in S1 Text). ..

    Generated:

    Article Title: Distinct populations of lung capillary endothelial cells and their functional significance
    Article Snippet: We utilized spatial transcriptomic data processed using Stomics Stereo-seq technology. .. The dataset generated with Stereo-seq technology was processed using the Stereo-seq pipeline, specifically utilizing the saw_v7.1 spatial_Transcriptomics_workflow through the stereoPipeline_v7.1.sh script. ..

    Article Title: Distinct populations of lung capillary endothelial cells and their functional significance.
    Article Snippet: We utilized spatial transcriptomic data processed using Stomics Stereo-seq technology. .. The dataset generated with Stereo-seq technology was processed using the Stereo-seq pipeline, specifically utilizing the saw_v7.1 spatial_Transcriptomics_workflow through the stereoPipeline_v7.1.sh script. ..



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    Single-nucleus transcriptome and spatial <t>transcriptomics</t> landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
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    Single-nucleus transcriptome and spatial <t>transcriptomics</t> landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
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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 <t>single</t> <t>cell</t> 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.
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    Image Search Results


    Single-nucleus transcriptome and spatial transcriptomics landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

    Journal: Frontiers in Immunology

    Article Title: Single-nucleus and spatial transcriptomics reveal intestinal cellular heterogeneity, differentiation, and cell communication mechanisms in SAP-induced intestinal injury

    doi: 10.3389/fimmu.2026.1719902

    Figure Lengend Snippet: Single-nucleus transcriptome and spatial transcriptomics landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

    Article Snippet: The spatial transcriptomics data were obtained according to the protocol of STOmics Gene Expression Set-S1 on the website ( https://www.stomics.tech/ ), which is an improved version of initial procedures.

    Techniques: Spatial Transcriptomics, Staining, Marker

    (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