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st seq  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc st seq
    St 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/spatial+transcriptomics+sequencing+(st-seq)/seq+st/pmc12528271-58-16-0
    Average 86 stars, based on 1 article reviews
    st 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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    spatial transcriptomics sequencing st seq  (Spatial Transcriptomics Inc)
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    Spatial Transcriptomics Inc spatial transcriptomics sequencing st seq
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    spatial transcriptome sequencing (st-seq) dataset  (Mendeley Ltd)
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    Mendeley Ltd spatial transcriptome sequencing (st-seq) dataset
    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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    spatial transcriptomics sequencing (st-seq)  (Spatial Transcriptomics Inc)
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    Spatial Transcriptomics Inc spatial transcriptomics sequencing (st-seq)
    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.

    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: 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.

    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: Dissection, Expressing, Gene Expression