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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
    Spatial Transcriptomics (St) Spot Size And Resolution, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/resolution+spatial+transcriptomics+st/spatial+transcriptomics+sequencing/pmc07391009-146-17-15
    Average 90 stars, based on 1 article reviews
    spatial transcriptomics (st) spot size and resolution - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: Single-cell transcriptome sequencing reveals new epithelial-stromal associated mesenchymal-like subsets in recurrent gliomas.
    Article Snippet: .. Spatial transcriptomics sequencing data was obtained from previous studies and is now publicly available (GSE194329) [19]. ..

    Article Title: Single-cell and spatial transcriptome analyses reveal MAZ(+) NPC-like clusters as key role contributing to glioma recurrence and drug resistance.
    Article Snippet: .. In response, the methods based on single-cell and spatial transcriptomics sequencing technology provide researchers with effective tools that allow them to delve deeper into the cellular composition of tumors and dynamic changes [8]. ..

    Article Title: Single-cell transcriptome sequencing reveals new epithelial-stromal associated mesenchymal-like subsets in recurrent gliomas
    Article Snippet: .. Spatial transcriptomics sequencing data was obtained from previous studies and is now publicly available ( GSE194329 ) [ ]. ..

    Article Title: Mevalonate pathway promotes liver cancer by suppressing ferroptosis through CoQ10 production and selenocysteine-tRNA modification.
    Article Snippet: .. Spatial transcriptomics (SpatialTME) further confirmed pathway activation (Fig. 1J) 15. ..

    Sequencing:

    Article Title: Single-cell transcriptome sequencing reveals new epithelial-stromal associated mesenchymal-like subsets in recurrent gliomas.
    Article Snippet: .. Spatial transcriptomics sequencing data was obtained from previous studies and is now publicly available (GSE194329) [19]. ..

    Article Title: Single-cell and spatial transcriptome analyses reveal MAZ(+) NPC-like clusters as key role contributing to glioma recurrence and drug resistance.
    Article Snippet: .. In response, the methods based on single-cell and spatial transcriptomics sequencing technology provide researchers with effective tools that allow them to delve deeper into the cellular composition of tumors and dynamic changes [8]. ..

    Article Title: Single-cell transcriptome sequencing reveals new epithelial-stromal associated mesenchymal-like subsets in recurrent gliomas
    Article Snippet: .. Spatial transcriptomics sequencing data was obtained from previous studies and is now publicly available ( GSE194329 ) [ ]. ..

    Single Cell:

    Article Title: Single-cell and spatial transcriptome analyses reveal MAZ(+) NPC-like clusters as key role contributing to glioma recurrence and drug resistance.
    Article Snippet: .. In response, the methods based on single-cell and spatial transcriptomics sequencing technology provide researchers with effective tools that allow them to delve deeper into the cellular composition of tumors and dynamic changes [8]. ..

    other:

    Article Title: Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory
    Article Snippet: Spatial transcriptomics (ST-seq) can also target the epithelium, providing insight into the evolutionary trajectory during the development and progression of tongue epithelial cells.

    Article Title: Spatial transcriptomics and multi-omics reveal relapse and resistance mechanisms of EndMT-derived CAFs mediated by TNC and FLNC in glioblastoma.
    Article Snippet: Spatial transcriptomics (ST) sequencing was performed, and then samples were subjected to hematoxylin and eosin (H&E) staining and tissue permeabilization.

    Article Title: SOAR elucidates biological insights and empowers drug discovery through spatial transcriptomics.
    Article Snippet: We present Spatial transcriptOmics Analysis Resource (SOAR), a comprehensive spatial transcriptomics platform with 3461 uniformly processed samples across 13 species, 42 tissue types, and 19 different spatial transcriptomics technologies.

    Article Title: CXCL12/CXCR4 axis governs Treg spatial dominance over CD8+ T cells via IL-2 sequestration: a dual therapeutic target in prostate cancer
    Article Snippet: The advent of high-throughput spatial transcriptomics technology introduces a novel dimension by providing insights into the spatial architecture and physical proximity of elements within the TIME, offering a transformative perspective on tumor immunology and therapeutic responses ( ).

    Activation Assay:

    Article Title: Mevalonate pathway promotes liver cancer by suppressing ferroptosis through CoQ10 production and selenocysteine-tRNA modification.
    Article Snippet: .. Spatial transcriptomics (SpatialTME) further confirmed pathway activation (Fig. 1J) 15. ..



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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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    https://www.bioz.com/product/resolution+spatial+transcriptomics+st/spatial+st+technologies+transcriptomics/pmc10719260-11-5-6
    Average 86 stars, based on 1 article reviews
    resolution spatial transcriptomics st - by Bioz Stars, 2026-09
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    spatial transcriptomics (st) spot size and resolution  (Spatial Transcriptomics Inc)
    90
    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.
    Spatial Transcriptomics (St) Spot Size And Resolution, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/resolution+spatial+transcriptomics+st/spatial+transcriptomics+sequencing/pmc07391009-146-17-15
    Average 90 stars, based on 1 article reviews
    spatial transcriptomics (st) spot size and resolution - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

    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