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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+transcriptomic+(st)+dataset/spatial+st+technologies+transcriptomics/pmc10502701-94-0-0
    Average 86 stars, based on 1 article reviews
    spatial transcriptomics st - by Bioz Stars, 2026-09
    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

    Related Articles

    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.

    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 ( ; ; ). ..

    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 ]. ..

    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. ..

    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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    Transcription programs of ccRCC cells in response to cuproptosis. A UMAP showing the 10 subtypes of 26,981 Epithelial cells; B heatmap showing inferred CNV of scRNA-seq dataset; C dot plot of the relative cellular proportions of Epithelial subtypes in each group; D GSEA analysis revealed the activated CRGs enriched in Normal Epithelial cells; E violin plot showing the relative CRGs score in each cancer subtype; F survival plot of HILPDA + ccRCC1 signature high and low group in the KIRC samples; G violin plots of HILPDA expression levels and hypoxia scores in each cancer subtypes; H spatial <t>transcriptome</t> displayed the distribution of CRGs, HILPDA + ccRCC1 signatures, hypoxia scores and HILPDA expression; I the regulon specificity scores of TFs in HILPDA + ccRCC1 subtype. The top 5 TFs ordered by scores were listed; J violin plot showing the expression levels of the top 5 TFs in HILPDA + ccRCC1 subtype across stage I–IV.
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    Transcription programs of ccRCC cells in response to cuproptosis. A UMAP showing the 10 subtypes of 26,981 Epithelial cells; B heatmap showing inferred CNV of scRNA-seq dataset; C dot plot of the relative cellular proportions of Epithelial subtypes in each group; D GSEA analysis revealed the activated CRGs enriched in Normal Epithelial cells; E violin plot showing the relative CRGs score in each cancer subtype; F survival plot of HILPDA + ccRCC1 signature high and low group in the KIRC samples; G violin plots of HILPDA expression levels and hypoxia scores in each cancer subtypes; H spatial <t>transcriptome</t> displayed the distribution of CRGs, HILPDA + ccRCC1 signatures, hypoxia scores and HILPDA expression; I the regulon specificity scores of TFs in HILPDA + ccRCC1 subtype. The top 5 TFs ordered by scores were listed; J violin plot showing the expression levels of the top 5 TFs in HILPDA + ccRCC1 subtype across stage I–IV.
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    Image Search Results


    Transcription programs of ccRCC cells in response to cuproptosis. A UMAP showing the 10 subtypes of 26,981 Epithelial cells; B heatmap showing inferred CNV of scRNA-seq dataset; C dot plot of the relative cellular proportions of Epithelial subtypes in each group; D GSEA analysis revealed the activated CRGs enriched in Normal Epithelial cells; E violin plot showing the relative CRGs score in each cancer subtype; F survival plot of HILPDA + ccRCC1 signature high and low group in the KIRC samples; G violin plots of HILPDA expression levels and hypoxia scores in each cancer subtypes; H spatial transcriptome displayed the distribution of CRGs, HILPDA + ccRCC1 signatures, hypoxia scores and HILPDA expression; I the regulon specificity scores of TFs in HILPDA + ccRCC1 subtype. The top 5 TFs ordered by scores were listed; J violin plot showing the expression levels of the top 5 TFs in HILPDA + ccRCC1 subtype across stage I–IV.

    Journal: Discover Oncology

    Article Title: Characterization of cuproptosis signature in clear cell renal cell carcinoma by single cell and spatial transcriptome analysis

    doi: 10.1007/s12672-024-01162-2

    Figure Lengend Snippet: Transcription programs of ccRCC cells in response to cuproptosis. A UMAP showing the 10 subtypes of 26,981 Epithelial cells; B heatmap showing inferred CNV of scRNA-seq dataset; C dot plot of the relative cellular proportions of Epithelial subtypes in each group; D GSEA analysis revealed the activated CRGs enriched in Normal Epithelial cells; E violin plot showing the relative CRGs score in each cancer subtype; F survival plot of HILPDA + ccRCC1 signature high and low group in the KIRC samples; G violin plots of HILPDA expression levels and hypoxia scores in each cancer subtypes; H spatial transcriptome displayed the distribution of CRGs, HILPDA + ccRCC1 signatures, hypoxia scores and HILPDA expression; I the regulon specificity scores of TFs in HILPDA + ccRCC1 subtype. The top 5 TFs ordered by scores were listed; J violin plot showing the expression levels of the top 5 TFs in HILPDA + ccRCC1 subtype across stage I–IV.

    Article Snippet: The spatial transcriptome sequencing (ST-seq) dataset was obtained from Mendeley Data platform ( https://data.mendeley.com/datasets/g67bkbnhhg/1 ) and input to python environment.

    Techniques: Expressing

    Dissection of immunosuppressive cells of cuproptosis-related tumor microenvironment. A UMAP showing the 16 subtypes of 99,210 Immune cells; B violin plot of the relative expression levels of the canocial markers in each subtype; C heatmap showing the enrichment of immune checkpoint and suppressive genes; D spatial transcriptome displayed the distribution of Treg, CD8_Exhausted and TAM signature scores; E heatmap showing the four gene expression patterns deduced by TDEseq analysis; F violin plots showing the relative expression levels of CRG scores in the immunosuppressive cells across different stages; G Chord diagram showing the number of interactions among the four subtypes; H Bubble plot showing the ligand-receptor pairs in the main subtype; I Heatmap showing the relative expression levels of key genes of the four subtypes among the different stages. The paired ligand-receptor shown in H were connected by lines.

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    Figure Lengend Snippet: Dissection of immunosuppressive cells of cuproptosis-related tumor microenvironment. A UMAP showing the 16 subtypes of 99,210 Immune cells; B violin plot of the relative expression levels of the canocial markers in each subtype; C heatmap showing the enrichment of immune checkpoint and suppressive genes; D spatial transcriptome displayed the distribution of Treg, CD8_Exhausted and TAM signature scores; E heatmap showing the four gene expression patterns deduced by TDEseq analysis; F violin plots showing the relative expression levels of CRG scores in the immunosuppressive cells across different stages; G Chord diagram showing the number of interactions among the four subtypes; H Bubble plot showing the ligand-receptor pairs in the main subtype; I Heatmap showing the relative expression levels of key genes of the four subtypes among the different stages. The paired ligand-receptor shown in H were connected by lines.

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    Techniques: Dissection, Expressing, Gene Expression