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high-resolution transcriptomic and spatial cell-type atlas  (Allen Institute for Brain Science)

 
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    Allen Institute for Brain Science high-resolution transcriptomic and spatial cell-type atlas
    Main results of the quantitative spatial <t> transcriptomic </t> analysis
    High Resolution Transcriptomic And Spatial Cell Type Atlas, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/resolution+spatial+transcriptomics+st/high+resolution+transcriptomic+and+spatial+cell+type+atlas/pmc11659360-114-17-30
    Average 90 stars, based on 1 article reviews
    high-resolution transcriptomic and spatial cell-type atlas - by Bioz Stars, 2026-11
    90/100 stars

    Images

    1) Product Images from "Growth hormone secretagogue receptor and cannabinoid receptor type 1 intersection in the mouse brain"

    Article Title: Growth hormone secretagogue receptor and cannabinoid receptor type 1 intersection in the mouse brain

    Journal: Brain Structure & Function

    doi: 10.1007/s00429-024-02876-3

    Main results of the quantitative spatial transcriptomic analysis
    Figure Legend Snippet: Main results of the quantitative spatial transcriptomic analysis

    Techniques Used:

    Related Articles

    Generated:

    Article Title: Growth hormone secretagogue receptor and cannabinoid receptor type 1 intersection in the mouse brain.
    Article Snippet: The volumetric renderings shown in Fig. 2 were generated using the Fiji plugin 3Dscript (Schmid et al. 2019). .. To analyze the abundance, co-distribution and phenotype of Ghsr- and Cnr1-expressing cells, we used a high-resolution transcriptomic and spatial cell-type atlas from the mouse brain generated at the Allen Institute for Brain Science (Yao et al. 2023). ..

    Article Title: Growth hormone secretagogue receptor and cannabinoid receptor type 1 intersection in the mouse brain
    Article Snippet: .. To analyze the abundance, co-distribution and phenotype of Ghsr - and Cnr1 -expressing cells, we used a high-resolution transcriptomic and spatial cell-type atlas from the mouse brain generated at the Allen Institute for Brain Science (Yao et al. ). ..

    Expressing:

    Article Title: Growth hormone secretagogue receptor and cannabinoid receptor type 1 intersection in the mouse brain
    Article Snippet: .. To analyze the abundance, co-distribution and phenotype of Ghsr - and Cnr1 -expressing cells, we used a high-resolution transcriptomic and spatial cell-type atlas from the mouse brain generated at the Allen Institute for Brain Science (Yao et al. ). ..

    other:

    Article Title: Selective Colocalization of GHSR and GLP-1R in a Subset of Hypothalamic Neurons and Their Functional Interaction.
    Article Snippet: The GH secretagogue receptor (GHSR) and the glucagon-like peptide-1 receptor (GLP-1R) are G protein-coupled receptors with critical, yet opposite, roles in regulating energy balance.. Interestingly, these receptors are expressed in overlapping brain regions.. However, the extent to which they target the same neurons and engage in molecular crosstalk remains unclear.



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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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    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

    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