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spatial transcriptomics st  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc spatial transcriptomics st
    (A) Schematic of spatial <t>transcriptomics</t> study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).
    Spatial Transcriptomics St, 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/pmc10502701-94-0-0?v=Spatial+Transcriptomics+Inc
    Average 86 stars, based on 1 article reviews
    spatial transcriptomics st - by Bioz Stars, 2026-08
    86/100 stars

    Images

    1) Product Images from "Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems"

    Article Title: Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems

    Journal: Science immunology

    doi: 10.1126/sciimmunol.abq7991

    (A) Schematic of spatial transcriptomics study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).
    Figure Legend Snippet: (A) Schematic of spatial transcriptomics study workflow. Table S1 contains metadata for each sample. (B) Schematic of skin, representative hematoxylin-eosin (H&E) image and corresponding ST plot (left-to-right). Scale bar = 440μm (C) UMAP visualization of 3,815 spots colored by cluster obtained from healthy skin samples (N=3, n=5). (D) Composition plots displaying relative abundance of each cluster by sample. Note up to two samples (labeled S) were collected from each Healthy Volunteer (HV). Replicate arrays are labeled “R” along the X axis. (E) Integration with a publicly-sourced single cell RNA-seq data set (dataset 1) with a representative ST spatial feature plot. See Figure S4 for UMAP of annotated cell type clusters. SMC=smooth muscle cell. Scale bar = 520μm (F) Multimodal intersection analysis (MIA) of overlap between data from datasets 1 and 2 and our ST-generated clusters. A sample hypergeometric distribution of keratinocyte cluster from dataset 1 and our epidermis cluster (cluster 6). MIA enrichment heatmaps of non-immune cell types in dataset 1 (G) and dataset 2 (H) and ST clusters from healthy skin. The X axis denotes the scRNA seq-identified cell types while the Y axis represents the ST-generated clusters. Differentiated keratinocytes (Diff KC) lymphatic endothelium (LE), proliferating keratinocytes (Prolif KC), vascular endothelium (VE), keratinocyte (KC). (I) MIA heatmap showing the enrichment of scRNA-seq-identified adipose- cell types from Hildreth et al. within pooled healthy skin ST clusters. (J) KEGG pathway analysis of the adipose cluster (cluster 2).

    Techniques Used: Labeling, RNA Sequencing, Generated



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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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    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