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scatac seq  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc scatac seq
    Scatac 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/spatial+transcriptomics+st/seq+st/pmc12492631-1-5-7
    Average 86 stars, based on 1 article reviews
    scatac seq - by Bioz Stars, 2026-09
    86/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: AEBP1 drives fibroblast-mediated T cell dysfunction in tumors.
    Article Snippet: .. Spatial transcriptomics data analysis The stRNA-seq slides of two human COAD samples were printed with two identical capture regions. ..

    Article Title: Integrating multi-modal transcriptomics identifies cellular subtypes with distinct roles in PDAC progression.
    Article Snippet: .. Spatial transcriptomics (ST-seq) data, including the HTANPDAC and GEO datasets GSE233293 and GSE202740, totaling 20 ST-seq samples, were jointly analyzed via the R packages Seurat and BayesSpace in R4.1.1 [41]. ..

    Article Title: Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment
    Article Snippet: Spatial Transcriptomics , 2016 , corrFISH , Single-cell , [ ] . .. Spatial Transcriptomics , 2017 , Geo-seq , Single-cell , [ ] . .. Spatial Transcriptomics , 2018 , Visium , 55 μm , [ ] .

    Article Title: Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment
    Article Snippet: Spatial Transcriptomics , 2019 , HDST , 2 μm , [ ] . .. Spatial Transcriptomics + Proteomics , 2020 , DBiT-seq , 10–50 μm , [ ] . .. Spatial Transcriptomics + Proteomics , 2020 , GeoMx DSP , Single-cell , [ ] .

    Article Title: Biology-driven insights into the power of single-cell foundation models
    Article Snippet: .. scGPT [ ] , scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics , 50 M , 33 M , 1200 HVGs , 512 , Value binning , Lookup Table (512d) , × , Encoder with attention mask , Iterative MGM with MSE loss (gene-prompt + cell-prompt), generative pretraining. .. UCE [ ] , scRNA-Seq , 650 M , 36 M , 1024 non-unique genes sampled (with replacement) by expression and ordered by genomic positions , 1280 , / , ESM-2 [ ] based protein embedding (5120d) , ✓ , Encoder , Modified MGM: binary CE loss for predicting whether a gene is expressed or not.

    Gene Expression:

    Article Title: Application of transcriptomics techniques in skin cancer
    Article Snippet: Single-cell RNA sequencing technology (scRNA-seq) , Gene expression profiles in single cell samples , Gene expression abundance and cell type , 1. Revealing cellular heterogeneity. 2. Building cell atlases. 3. Inferring differentiation trajectories. 4. Resolving the cellular composition of complex tissues. , 1. Loss of original spatial location information. 2. Dissociation process may result in the loss of specific cells. 3. High cost. 4. Complex data analysis.. .. Spatial Transcriptomics (ST-seq) , Organization of gene expression profiles retaining spatial location information , Gene expression abundance, cell type and spatial location information , 1. Preserve and interpret the spatial context of gene expression. 2. Locate specific gene types within tissues. 3. Study tissue microenvironments and link molecular phenotypes to tissue morphology. , 1. Data analysis is more complex and requires the integration of images and multi-omics. 2. The cost of technology is extremely high. 3. The number of genes covered may be limited.. .. MM , RNA-seq , MITF, AXL , BRAF/MEK, anti-PD-1 antibody , ( ) .

    Biomarker Discovery:

    Article Title: Application of transcriptomics techniques in skin cancer
    Article Snippet: Single-cell RNA sequencing technology (scRNA-seq) , Gene expression profiles in single cell samples , Gene expression abundance and cell type , 1. Revealing cellular heterogeneity. 2. Building cell atlases. 3. Inferring differentiation trajectories. 4. Resolving the cellular composition of complex tissues. , 1. Loss of original spatial location information. 2. Dissociation process may result in the loss of specific cells. 3. High cost. 4. Complex data analysis.. .. Spatial Transcriptomics (ST-seq) , Organization of gene expression profiles retaining spatial location information , Gene expression abundance, cell type and spatial location information , 1. Preserve and interpret the spatial context of gene expression. 2. Locate specific gene types within tissues. 3. Study tissue microenvironments and link molecular phenotypes to tissue morphology. , 1. Data analysis is more complex and requires the integration of images and multi-omics. 2. The cost of technology is extremely high. 3. The number of genes covered may be limited.. .. MM , RNA-seq , MITF, AXL , BRAF/MEK, anti-PD-1 antibody , ( ) .

    Sequencing:

    Article Title: Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics
    Article Snippet: .. Spatial transcriptomics sequencing (ST-seq) enables the identification of distinct cell populations while preserving their spatial context, offering essential insights into cell function, phenotype, and positional relationships within the tissue microenvironment [ , ]. ..

    Preserving:

    Article Title: Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics
    Article Snippet: .. Spatial transcriptomics sequencing (ST-seq) enables the identification of distinct cell populations while preserving their spatial context, offering essential insights into cell function, phenotype, and positional relationships within the tissue microenvironment [ , ]. ..

    Cell Function Assay:

    Article Title: Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics
    Article Snippet: .. Spatial transcriptomics sequencing (ST-seq) enables the identification of distinct cell populations while preserving their spatial context, offering essential insights into cell function, phenotype, and positional relationships within the tissue microenvironment [ , ]. ..

    Single Cell:

    Article Title: Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment
    Article Snippet: Spatial Transcriptomics , 2016 , corrFISH , Single-cell , [ ] . .. Spatial Transcriptomics , 2017 , Geo-seq , Single-cell , [ ] . .. Spatial Transcriptomics , 2018 , Visium , 55 μm , [ ] .



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    Spatial Transcriptomics Inc salus sts high resolution spatial transcriptomics
    <t>Salus-STS</t> <t>high-resolution</t> spatial <t>transcriptomics</t> enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.
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    Image Search Results


    Spatial organization and cell-cell communication networks in the tumor microenvironment. (A–C) The developmental trajectories of cell sub-populations from a spatial perspective are investigated. (D, E) Heatmap and network diagrams displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data. (F) The interaction heatmap visualized the intensity of intercellular interactions mediated by the ligand-receptor pairs. (G) The spatial cell communication network diagram illustrates that NUhighepi exhibit a higher intensity of cell communication with other cells. (H) Circos plot summarizing cell-type-specific interaction patterns.

    Journal: Frontiers in Oncology

    Article Title: Spatial transcriptome and single-cell sequencing reveal the role of nucleotide metabolism in breast cancer progression and tumor microenvironment

    doi: 10.3389/fonc.2025.1703778

    Figure Lengend Snippet: Spatial organization and cell-cell communication networks in the tumor microenvironment. (A–C) The developmental trajectories of cell sub-populations from a spatial perspective are investigated. (D, E) Heatmap and network diagrams displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data. (F) The interaction heatmap visualized the intensity of intercellular interactions mediated by the ligand-receptor pairs. (G) The spatial cell communication network diagram illustrates that NUhighepi exhibit a higher intensity of cell communication with other cells. (H) Circos plot summarizing cell-type-specific interaction patterns.

    Article Snippet: Breast cancer spatial transcriptomics (ST) data were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) and 10x Genomics official website ( https://www.10xgenomics.com/ ).

    Techniques:

    Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Salus-STS high-resolution spatial transcriptomics enables effective cell identification at the subcellular level. (A) Schematics illustrating of the study. (B) Results of cell segmentation via the Salus Cellbins Algorithm. (C–F) Distributions and medians (red text in the figures) of the area (in pixel 2 ) (C) , UMI counts (D) , gene numbers (E) , and proportions of mitochondrial UMIs (F) of segmented cellbins.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques:

    Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: Cellbin-based analysis enables accurate identification of distinct cell types in the mouse testis. (A) RCTD-annotated distinct cell types and their proportions. (B) UMAP visualization of the Salus-STS Cellbin data with scRNA-Seq data. (C) Spatial distribution of distinct cell types in the mouse testis. (D) Integrated distribution map of cell distributions in the mouse testis. (E) Markers of distinct cell types and their expression levels. Scaled expression: the average expression level scaled across genes to eliminate the effect of total expression level differences among genes. Percentage: for each cell type, the percentage of cellbins that express the specific gene out of all cellbins of the same type.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing

    High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Journal: Frontiers in Reproductive Health

    Article Title: Spatiotemporal dynamics of spermatogenesis: insights from high-resolution spatial transcriptomics and pseudotime trajectories in mouse testes

    doi: 10.3389/frph.2025.1747902

    Figure Lengend Snippet: High-resolution spatial transcriptomics uncovers spatiotemporal markers of spermatogenesis. (A) Pseudotime trajectory analysis. (B) Randomly selected seminiferous tubules. (C,D) Top 6 genes with expression levels positively (C) and negatively (D) correlated with the axis from the tubule basement membrane (epithelium) to the lumen center respectively.

    Article Snippet: In this study, we used Salus-STS high-resolution spatial transcriptomics (∼1 μm resolution) and Salus Cellbins Algorithm to characterize the spatial transcriptomic profile of mouse testes at single-cell level.

    Techniques: Expressing, Membrane