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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/resolution+spatial+transcriptomics+st/spatial+st+technologies+transcriptomics/pmc10502701-94-0-0
    Average 86 stars, based on 1 article reviews
    spatial transcriptomics st - by Bioz Stars, 2026-09
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    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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    Spatial Transcriptomics:

    Article Title: A technical comparison of spatial transcriptomics platforms across six cancer types.
    Article Snippet: .. Background Spatial transcriptomics (ST) technologies are reshaping our understanding of tissue organization and cellular context in health and disease. ..

    Article Title: Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases.
    Article Snippet: .. Spatial transcriptomics (ST) technologies have addressed this gap by enabling the quantification of gene expression in intact tissue sections, preserving both spatial localization and, in some cases, the three-dimensional (3D) architecture of molecular processes. ..

    Article Title: SpaLLM: a general framework for spatial domain identification with large language models
    Article Snippet: .. Spatial transcriptomics (ST) technologies have revolutionized our understanding of tissue architecture by enabling simultaneous measurement of gene expression and spatial location information ( ; ; ). ..

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    Article Snippet: .. Spatial transcriptomics (ST) technologies enable the profiling of gene expression while preserving spatial context, offering unprecedented insights into tissue organization. ..

    Article Title: Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable highthroughput gene expression profiling while preserving spatial context within tissues in situ [ 1–5 ]. ..

    Article Title: A technical comparison of spatial transcriptomics platforms across six cancer types.
    Article Snippet: .. Spatial transcriptomics (ST) technologies are reshaping our understanding of tissue organization and cellular context in health and disease. ..

    other:

    Article Title: Comprehensive benchmarking of batch integration methods for spatial transcriptomics using a large-scale cancer atlas
    Article Snippet: Spatial transcriptomics (ST) technologies capture mRNA counts with spatial context, complementing singlecell and bulk transcriptomics.

    Gene Expression:

    Article Title: Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases.
    Article Snippet: .. Spatial transcriptomics (ST) technologies have addressed this gap by enabling the quantification of gene expression in intact tissue sections, preserving both spatial localization and, in some cases, the three-dimensional (3D) architecture of molecular processes. ..

    Article Title: SpaLLM: a general framework for spatial domain identification with large language models
    Article Snippet: .. Spatial transcriptomics (ST) technologies have revolutionized our understanding of tissue architecture by enabling simultaneous measurement of gene expression and spatial location information ( ; ; ). ..

    Article Title: Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics data.
    Article Snippet: .. 24 Spatial transcriptomics (ST) links tissue morphology with gene expression values, opening new avenues for digital pathology. ..

    Article Title: SpaLLM: a general framework for spatial domain identification with large language models
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable the profiling of gene expression while preserving spatial context, offering unprecedented insights into tissue organization. ..

    Article Title: Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable highthroughput gene expression profiling while preserving spatial context within tissues in situ [ 1–5 ]. ..

    Preserving:

    Article Title: Mapping biology in space: from spatial transcriptomics platforms to analytical tools and databases.
    Article Snippet: .. Spatial transcriptomics (ST) technologies have addressed this gap by enabling the quantification of gene expression in intact tissue sections, preserving both spatial localization and, in some cases, the three-dimensional (3D) architecture of molecular processes. ..

    Article Title: SpaLLM: a general framework for spatial domain identification with large language models
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable the profiling of gene expression while preserving spatial context, offering unprecedented insights into tissue organization. ..

    Article Title: Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable highthroughput gene expression profiling while preserving spatial context within tissues in situ [ 1–5 ]. ..

    High Throughput Screening Assay:

    Article Title: Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable highthroughput gene expression profiling while preserving spatial context within tissues in situ [ 1–5 ]. ..

    In Situ:

    Article Title: Reconstructing Coherent Functional Landscape From Multi-Modal Multi-Slice Spatial Transcriptomics by a Variational Spatial Gaussian Process.
    Article Snippet: .. Spatial transcriptomics (ST) technologies enable highthroughput gene expression profiling while preserving spatial context within tissues in situ [ 1–5 ]. ..



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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.

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    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.

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    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