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Spatial Transcriptomics Inc single cell rna sequencing scrna seq data
Single Cell Rna Sequencing Scrna Seq 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+sequencing/cell+single/pm40890608-181-5-0
Average 86 stars, based on 1 article reviews
single cell rna sequencing scrna seq data - by Bioz Stars, 2026-09
86/100 stars

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Article Title: A spatial and projection-based transcriptomic atlas of paraventricular hypothalamic cell types.
Article Snippet: We leveraged single-cell and spatial transcriptomics technologies to develop a high-resolution, spatially resolved atlas of the mouse PVH region.

Article Title: Immunological mechanisms underlying fibrotic diseases via single-cell technologies
Article Snippet: In a study that analyzed both the fibrotic progression and resolution phases of the bleomycin model using single-cell and spatial transcriptomics , CSMD1 + fibroblasts with ECM-secreting properties were induced during the progression phase, whereas CD248 + fibroblasts with tissue repair-promoting properties were induced during the resolution phase ( ).

Article Title: Advancing Cardiovascular Research With Single-Cell and Spatial Transcriptomics
Article Snippet: Single-cell and spatial transcriptomics technologies have transformed the landscape of cardiovascular research, leading to novel insights into cellular heterogeneity and tissue architecture in health and disease.. These technologies enable researchers to deconvolute complex tissues and map gene expression patterns within their spatial contexts, providing critical information on the interplay between cell types and pathways affecting tissue regeneration or progression to fibrosis.. This review presents an overview of the recently developed applications of single-cell and spatial transcriptomics methods and their impact on cardiovascular research.

Construct:

Article Title: Neuro‐Immune Crosstalk: Molecular Mechanisms, Biological Functions, Diseases, and Therapeutic Targets
Article Snippet: .. To overcome these challenges, researchers have developed cutting‐edge tools, including: (1) high‐resolution spatial multiomics platforms (e.g., integrated spatial transcriptomics‐proteomics) for spatiotemporal mapping of neuro‐immune interactions to construct comprehensive “neuro‐immune connectomes”; (2) optimized organoid coculture systems, particularly brain–immune cell interaction models; and (3) advanced in vivo imaging techniques (e.g., two‐photon microscopy coupled with specific reporter systems) for real‐time observation of neuro‐immune processes. ..

In Vivo Imaging:

Article Title: Neuro‐Immune Crosstalk: Molecular Mechanisms, Biological Functions, Diseases, and Therapeutic Targets
Article Snippet: .. To overcome these challenges, researchers have developed cutting‐edge tools, including: (1) high‐resolution spatial multiomics platforms (e.g., integrated spatial transcriptomics‐proteomics) for spatiotemporal mapping of neuro‐immune interactions to construct comprehensive “neuro‐immune connectomes”; (2) optimized organoid coculture systems, particularly brain–immune cell interaction models; and (3) advanced in vivo imaging techniques (e.g., two‐photon microscopy coupled with specific reporter systems) for real‐time observation of neuro‐immune processes. ..

Microscopy:

Article Title: Neuro‐Immune Crosstalk: Molecular Mechanisms, Biological Functions, Diseases, and Therapeutic Targets
Article Snippet: .. To overcome these challenges, researchers have developed cutting‐edge tools, including: (1) high‐resolution spatial multiomics platforms (e.g., integrated spatial transcriptomics‐proteomics) for spatiotemporal mapping of neuro‐immune interactions to construct comprehensive “neuro‐immune connectomes”; (2) optimized organoid coculture systems, particularly brain–immune cell interaction models; and (3) advanced in vivo imaging techniques (e.g., two‐photon microscopy coupled with specific reporter systems) for real‐time observation of neuro‐immune processes. ..

Single Cell:

Article Title: Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment.
Article Snippet: Ligand-receptor pair analysis, a key aspect of cell-to-cell interactions, is vital for understanding physiological and pathological processes in organisms.. However, most analytical tools fail to incorporate spatial in situ information, resulting in false positive predictions.. Spatial transcriptomics, which integrates gene expression data with cellular localization, has emerged as a powerful method for inferring cell-cell interactions.

Article Title: Immunometabolic reprogramming of macrophages: Emerging roles in skeletal muscle regeneration and therapeutic perspectives.
Article Snippet: .. Recent advances in single-cell and spatial transcriptomics technologies have revealed the remarkable heterogeneity of macrophage subpopulations within skeletal muscle. ..

Transcriptomics:

Article Title: Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment.
Article Snippet: Ligand-receptor pair analysis, a key aspect of cell-to-cell interactions, is vital for understanding physiological and pathological processes in organisms.. However, most analytical tools fail to incorporate spatial in situ information, resulting in false positive predictions.. Spatial transcriptomics, which integrates gene expression data with cellular localization, has emerged as a powerful method for inferring cell-cell interactions.

Cell Characterization:

Article Title: Ontogeny of the spinal cord dorsal horn.
Article Snippet: INTRODUCTION: In every part of our bodies, cell types and anatomy are organized to carry out specialized functions, and the dorsal horn of the mammalian spinal cord serves as a prime example.. It is characterized by a vast array of cell types arranged in a layered microcircuit architecture, with each layer receiving a specific set of sensory and descending neural inputs and contributing in a distinctive manner to animal behavior.. In this way, the dorsal horn represents a common motif of neural organization found throughout the animal kingdom, from the vertebrate midbrain to the fly’s optic lobe.

Article Title: Ontogeny of the spinal cord dorsal horn
Article Snippet: .. For EdU cell count coordinates (and later also for spatial transcriptomics cell coordinates), cell coordinates were normalized so that statistics and graphics could be produced systematically on cell coordinates across sections and experimental groups. ..

Spatial Transcriptomics:

Article Title: Ontogeny of the spinal cord dorsal horn.
Article Snippet: INTRODUCTION: In every part of our bodies, cell types and anatomy are organized to carry out specialized functions, and the dorsal horn of the mammalian spinal cord serves as a prime example.. It is characterized by a vast array of cell types arranged in a layered microcircuit architecture, with each layer receiving a specific set of sensory and descending neural inputs and contributing in a distinctive manner to animal behavior.. In this way, the dorsal horn represents a common motif of neural organization found throughout the animal kingdom, from the vertebrate midbrain to the fly’s optic lobe.

Article Title: Immunometabolic reprogramming of macrophages: Emerging roles in skeletal muscle regeneration and therapeutic perspectives.
Article Snippet: .. Recent advances in single-cell and spatial transcriptomics technologies have revealed the remarkable heterogeneity of macrophage subpopulations within skeletal muscle. ..

Article Title: Ontogeny of the spinal cord dorsal horn
Article Snippet: .. For EdU cell count coordinates (and later also for spatial transcriptomics cell coordinates), cell coordinates were normalized so that statistics and graphics could be produced systematically on cell coordinates across sections and experimental groups. ..

Produced:

Article Title: Ontogeny of the spinal cord dorsal horn.
Article Snippet: INTRODUCTION: In every part of our bodies, cell types and anatomy are organized to carry out specialized functions, and the dorsal horn of the mammalian spinal cord serves as a prime example.. It is characterized by a vast array of cell types arranged in a layered microcircuit architecture, with each layer receiving a specific set of sensory and descending neural inputs and contributing in a distinctive manner to animal behavior.. In this way, the dorsal horn represents a common motif of neural organization found throughout the animal kingdom, from the vertebrate midbrain to the fly’s optic lobe.

Article Title: Ontogeny of the spinal cord dorsal horn
Article Snippet: .. For EdU cell count coordinates (and later also for spatial transcriptomics cell coordinates), cell coordinates were normalized so that statistics and graphics could be produced systematically on cell coordinates across sections and experimental groups. ..



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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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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 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