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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/resolution+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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    salus sts high resolution spatial transcriptomics  (Spatial Transcriptomics Inc)
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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.
    Salus Sts High Resolution Spatial Transcriptomics, 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
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    <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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    resolution spatial transcriptomics st  (Spatial Transcriptomics Inc)
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    Spatial Transcriptomics Inc resolution spatial transcriptomics st
    a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial <t>transcriptomics</t> data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.
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    spatial transcriptomics (st) spot size and resolution  (Spatial Transcriptomics Inc)
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    Spatial Transcriptomics Inc spatial transcriptomics (st) spot size and resolution
    a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial <t>transcriptomics</t> data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.
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    Image Search Results


    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

    a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial transcriptomics data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.

    Journal: Nature Communications

    Article Title: GNTD: reconstructing spatial transcriptomes with graph-guided neural tensor decomposition informed by spatial and functional relations

    doi: 10.1038/s41467-023-44017-0

    Figure Lengend Snippet: a Ground-truth segmentation of 6 cortical layers and white matter (WM) for simulated spatial transcriptomics data based on the annotation of the human dorsolateral prefrontal cortex (DLPFC) section 151673. b Spot clustering performance on the raw data and the imputed data by CoSTCo, DTD, FIST ( λ = 0 or 0.01), and GNTD ( λ = 0 or 0.1) at different ranks in the simulated spatial transcriptomics data with 40% or 80% zero inflation rate. c Visualization of the spatial domains detected by spot clustering on the raw data and the imputed data of the simulated spatial transcriptomics data with 40% and 80% zero inflation rates. The imputed data with the best rank by each tensor decomposition method was used in the visualization. d Spatially variable genes detection comparison. The plot shows the percentage of correctly detected spatially variable genes by the AUC thresholds of the recovered highly expressed spots in the more sparse simulated spatial transcriptomics data with 80% zero inflation rate. e Spatial patterns visualization of three example genes by their expression in the ground-truth data, raw data, and the imputation data of the simulated spatial transcriptomics data with 80% zero inflation rate. Note that in ( d ) and ( e ), a higher AUC indicates a better consistency between the imputed or raw expressions and the ground-truth expression over the spots for the gene. Source data for ( b ) and ( d ) are provided as a Source Data file.

    Article Snippet: These methods range from lower resolution Spatial Transcriptomics (ST) (commercialized as 10x Genomics Visium ), to higher resolution Slide-seq , or even sub-cellular resolution technologies such as high-definition spatial transcriptomics (HDST) and Spatio-temporal enhanced resolution omics-sequencing (Stereo-seq) .

    Techniques: Comparison, Expressing