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Spatial Transcriptomics Inc spatial transcriptomics st data
Spatial Transcriptomics St Data, 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/data+sequencing+spatial+transcriptomics/pm41610146-199-7-7
Average 86 stars, based on 1 article reviews
spatial transcriptomics st data - by Bioz Stars, 2026-09
86/100 stars

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Expressing:

Article Title: Multiomic analysis of CCNE1 amplification associated molecular and immune features in gynecological cancers
Article Snippet: .. C Spatial transcriptomics ( GSM8207499 ) revealed diffuse CCNE1 expression without distinct clustering, with elevated VEGFB and FBLN2 levels observed in CCNE1-high regions. ..

Spatial Transcriptomics:


Article Title: Multi-omics integration and machine learning define robust molecular subtypes and prognostic signatures in hepatocellular carcinoma.
Article Snippet: .. Single-cell RNA-seq data were obtained from GSE166635 [20] and spatial transcriptomics data (HCC1R and HCC4R) from GSE238264 [21]. ..

Article Title: SpaConTDS: A multimodal contrastive learning framework for identifying spatial domains by applying tuple disturbing strategy
Article Snippet: .. In-depth exploration of the multimodal information within Spatial Transcriptomics (ST) data is essential for understanding the heterogeneity of tissue structure, investigating biological functions and tracking disease progression. ..

Article Title: Single-cell and spatial transcriptomics unveils key regulators governing cell differentiation for Schistosoma sexual development
Article Snippet: .. The sequencing data from Stereo-seq spatial transcriptomics was processed using Stereo-seq Analysis Workflow (SAW) v8.0 ( https://en.stomics.tech ). ..

Article Title: SGMS2+ macrophages enhance NR4A3hi NK cell infiltration to improve prognosis and PD-1 treatment efficacy in hepatocellular carcinoma
Article Snippet: .. Spatial transcriptomics sequencing data were obtained from http://lifeome.net/supp/livercancer-st/data.htm and analyzed using Seurat in R. Subsequently, SCTtransform normalization was performed. ..

Article Title: SpaConTDS: A multimodal contrastive learning framework for identifying spatial domains by applying tuple disturbing strategy.
Article Snippet: .. In-depth exploration of the multimodal information within Spatial Transcriptomics (ST) data is essential for understanding the heterogeneity of tissue structure, investigating biological functions and tracking disease progression. ..

Single Cell:

Article Title: Multi-omics integration and machine learning define robust molecular subtypes and prognostic signatures in hepatocellular carcinoma.
Article Snippet: .. Single-cell RNA-seq data were obtained from GSE166635 [20] and spatial transcriptomics data (HCC1R and HCC4R) from GSE238264 [21]. ..

RNA Sequencing:

Article Title: Multi-omics integration and machine learning define robust molecular subtypes and prognostic signatures in hepatocellular carcinoma.
Article Snippet: .. Single-cell RNA-seq data were obtained from GSE166635 [20] and spatial transcriptomics data (HCC1R and HCC4R) from GSE238264 [21]. ..

Gene Expression:

Article Title: Multiomic analysis of CCNE1 amplification associated molecular and immune features in gynecological cancers
Article Snippet: .. C Spatial transcriptomics ( GSM7019835 ) depicting the gene expression patterns of CCNE1, EPCAM (epithelial marker), and the colocalized genes ARHGAP1 and STK24, which are involved in structural remodeling and cell motility. ..

Marker:

Article Title: Multiomic analysis of CCNE1 amplification associated molecular and immune features in gynecological cancers
Article Snippet: .. C Spatial transcriptomics ( GSM7019835 ) depicting the gene expression patterns of CCNE1, EPCAM (epithelial marker), and the colocalized genes ARHGAP1 and STK24, which are involved in structural remodeling and cell motility. ..

Biomarker Discovery:

Article Title: SpaConTDS: A multimodal contrastive learning framework for identifying spatial domains by applying tuple disturbing strategy
Article Snippet: .. In-depth exploration of the multimodal information within Spatial Transcriptomics (ST) data is essential for understanding the heterogeneity of tissue structure, investigating biological functions and tracking disease progression. ..

Article Title: SpaConTDS: A multimodal contrastive learning framework for identifying spatial domains by applying tuple disturbing strategy.
Article Snippet: .. In-depth exploration of the multimodal information within Spatial Transcriptomics (ST) data is essential for understanding the heterogeneity of tissue structure, investigating biological functions and tracking disease progression. ..

Sequencing:

Article Title: Single-cell and spatial transcriptomics unveils key regulators governing cell differentiation for Schistosoma sexual development
Article Snippet: .. The sequencing data from Stereo-seq spatial transcriptomics was processed using Stereo-seq Analysis Workflow (SAW) v8.0 ( https://en.stomics.tech ). ..

Article Title: SGMS2+ macrophages enhance NR4A3hi NK cell infiltration to improve prognosis and PD-1 treatment efficacy in hepatocellular carcinoma
Article Snippet: .. Spatial transcriptomics sequencing data were obtained from http://lifeome.net/supp/livercancer-st/data.htm and analyzed using Seurat in R. Subsequently, SCTtransform normalization was performed. ..



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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 <t>transcriptomics</t> 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.
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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.
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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