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breast cancer spatial transcriptomics st  (Spatial Transcriptomics Inc)

 
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    Spatial Transcriptomics Inc breast cancer spatial transcriptomics st
    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.
    Breast Cancer 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+transcriptomics+st/breast+data+her2+human+positive+spatial+transcriptomics+tumor/pmc12847018-99-0-2
    Average 86 stars, based on 1 article reviews
    breast cancer spatial transcriptomics st - by Bioz Stars, 2026-10
    86/100 stars

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    1) Product Images from "Spatial transcriptome and single-cell sequencing reveal the role of nucleotide metabolism in breast cancer progression and tumor microenvironment"

    Article Title: Spatial transcriptome and single-cell sequencing reveal the role of nucleotide metabolism in breast cancer progression and tumor microenvironment

    Journal: Frontiers in Oncology

    doi: 10.3389/fonc.2025.1703778

    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.
    Figure Legend 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.

    Techniques Used:

    Related Articles

    other:

    Article Title: MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning
    Article Snippet: For the adult human hippocampus dataset (spatial ATAC–RNA–seq), the mouse brain dataset (spatial transcriptomics + metabolomics), the breast cancer-patterned spatial ATAC + RNA dataset (Supplementary Fig. ), which have ground truth spatial domain annotations, we computed the following metrics to assess clustering agreement: Rand Index (RI), ARI, Adjusted Mutual Information, Normalized Mutual Information, Homogeneity, Completeness, V-measure, Fowlkes-Mallows Index.

    Article Title: Bridging cell morphological behaviors and molecular dynamics in multi-modal spatial omics with MorphLink
    Article Snippet: Publicly available data were acquired from the following websites or accession numbers: (1) human bladder tumor 10x Visium data (GEO repository: GSE246011 ); (2) zebrafish melanoma 10x Visium data (GEO repository: GSE159709 ); (3) human tonsil spatial CITE-seq data ( https://www.10xgenomics.com/datasets/gene-protein-expression-library-of-human-tonsil-cytassist-ffpe-2-standard ); (4) human HER2-positive breast tumor spatial transcriptomics data ( https://github.com/almaan/her2st ); (5) human breast tumor 10x Visium data ( https://www.10xgenomics.com/datasets/human-breast-cancer-visium-fresh-frozen-whole-transcriptome-1-standard ); (6) mouse brain 10x Visium data ( https://www.10xgenomics.com/datasets/fresh-frozen-visium-on-cytassist-mouse-brain-probe-based-whole-transcriptome-profiling-2-standard ); (7) mouse embryo 10x Visium data ( https://www.10xgenomics.com/datasets/visium-cytassist-mouse-embryo-11-mm-capture-area-ffpe-2-standard ); (8) human breast tumor H&E images from TCGA data ( https://portal.gdc.cancer.gov/ sample ID: DX1.01FB49CC, DX1.392580F3, DX1.0E26C46D).

    Functional Assay:

    Article Title: A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.
    Article Snippet: .. The reduced overlap may indicate weakening of their functional connection in aging process. ouse and human MERFISH brain cortex data e conducted a cross-species gene co-expression analysis on uman and mouse brain cortex data [ 36 ] generated by MERISH, an imaging-based spatial transcriptomics technology 78 ]. ..

    Generated:

    Article Title: A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.
    Article Snippet: .. The reduced overlap may indicate weakening of their functional connection in aging process. ouse and human MERFISH brain cortex data e conducted a cross-species gene co-expression analysis on uman and mouse brain cortex data [ 36 ] generated by MERISH, an imaging-based spatial transcriptomics technology 78 ]. ..

    Imaging:

    Article Title: A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.
    Article Snippet: .. The reduced overlap may indicate weakening of their functional connection in aging process. ouse and human MERFISH brain cortex data e conducted a cross-species gene co-expression analysis on uman and mouse brain cortex data [ 36 ] generated by MERISH, an imaging-based spatial transcriptomics technology 78 ]. ..

    Spatial Transcriptomics:

    Article Title: A spatially informed matrix normal model for gene co-expression analysis in spatial transcriptomics studies.
    Article Snippet: .. The reduced overlap may indicate weakening of their functional connection in aging process. ouse and human MERFISH brain cortex data e conducted a cross-species gene co-expression analysis on uman and mouse brain cortex data [ 36 ] generated by MERISH, an imaging-based spatial transcriptomics technology 78 ]. ..

    Article Title: Transfer learning of multicellular organization via single-cell and spatial transcriptomics
    Article Snippet: .. The original public data used in this paper can be accessed through the following links: (1) FISH data from the Berkeley Drosophila Transcription Network Project (BDTNP): https://shiny.mdc-berlin.de/DVEX/ ; (2) 10X Visium data of the human dorsolateral prefrontal cortex (DLPFC): http://spatial.libd.org/spatialLIBD/ ; (3) Adult mouse cortical cell datasets (GEO accession GSE71585): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585 ; (4) Mouse embryo data: https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/ ; (5) Seurat objects ST data (10X Genomics Visium) of mouse brain: https://satijalab.org/seurat/articles/spatial_vignette.html ; (6) 10X Visium data of mouse brain: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11114 ; (7) MERFISH data of mouse brain: https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas ; (8) MERFISH data of human MTG: https://doi.org/10.5061/dryad.x3ffbg7mw ; (9) SMART-seq data of human MTG: https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smart-seq ; (10) Single-cell RNA-seq and Spatial Transcriptomics data of developing human heart: https://data.mendeley.com/datasets/mbvhhf8m62/2 ; (11) Human artery data: https://doi.org/10.5281/zenodo.15132967 . ..

    Article Title: Single-cell and spatial detection of senescent cells using DeepScence
    Article Snippet: Spatial transcriptomics data from mouse muscle under notexin-induced injury , McKellar et al. , GEO: GSE161318. .. Spatial transcriptomics data from human brain with Alzheimer’s disease , Gong et al. , GEO: GSE269906. ..

    Article Title: scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization
    Article Snippet: .. Furthermore, we applied scGALA to a Xenium human breast cancer dataset (541 genes, accessible through SubcellularSpatialData R/Bioconductor package with dataset ID: EH8567) to demonstrate practical applicability on real spatial transcriptomics data with limited gene panels. ..

    Article Title: Spatial transcriptome and single-cell sequencing reveal the role of nucleotide metabolism in breast cancer progression and tumor microenvironment
    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/ ). ..

    Fluorescence In Situ Hybridization:

    Article Title: Transfer learning of multicellular organization via single-cell and spatial transcriptomics
    Article Snippet: .. The original public data used in this paper can be accessed through the following links: (1) FISH data from the Berkeley Drosophila Transcription Network Project (BDTNP): https://shiny.mdc-berlin.de/DVEX/ ; (2) 10X Visium data of the human dorsolateral prefrontal cortex (DLPFC): http://spatial.libd.org/spatialLIBD/ ; (3) Adult mouse cortical cell datasets (GEO accession GSE71585): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585 ; (4) Mouse embryo data: https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/ ; (5) Seurat objects ST data (10X Genomics Visium) of mouse brain: https://satijalab.org/seurat/articles/spatial_vignette.html ; (6) 10X Visium data of mouse brain: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11114 ; (7) MERFISH data of mouse brain: https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas ; (8) MERFISH data of human MTG: https://doi.org/10.5061/dryad.x3ffbg7mw ; (9) SMART-seq data of human MTG: https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smart-seq ; (10) Single-cell RNA-seq and Spatial Transcriptomics data of developing human heart: https://data.mendeley.com/datasets/mbvhhf8m62/2 ; (11) Human artery data: https://doi.org/10.5281/zenodo.15132967 . ..

    Single Cell:

    Article Title: Transfer learning of multicellular organization via single-cell and spatial transcriptomics
    Article Snippet: .. The original public data used in this paper can be accessed through the following links: (1) FISH data from the Berkeley Drosophila Transcription Network Project (BDTNP): https://shiny.mdc-berlin.de/DVEX/ ; (2) 10X Visium data of the human dorsolateral prefrontal cortex (DLPFC): http://spatial.libd.org/spatialLIBD/ ; (3) Adult mouse cortical cell datasets (GEO accession GSE71585): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585 ; (4) Mouse embryo data: https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/ ; (5) Seurat objects ST data (10X Genomics Visium) of mouse brain: https://satijalab.org/seurat/articles/spatial_vignette.html ; (6) 10X Visium data of mouse brain: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11114 ; (7) MERFISH data of mouse brain: https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas ; (8) MERFISH data of human MTG: https://doi.org/10.5061/dryad.x3ffbg7mw ; (9) SMART-seq data of human MTG: https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smart-seq ; (10) Single-cell RNA-seq and Spatial Transcriptomics data of developing human heart: https://data.mendeley.com/datasets/mbvhhf8m62/2 ; (11) Human artery data: https://doi.org/10.5281/zenodo.15132967 . ..

    RNA Sequencing:

    Article Title: Transfer learning of multicellular organization via single-cell and spatial transcriptomics
    Article Snippet: .. The original public data used in this paper can be accessed through the following links: (1) FISH data from the Berkeley Drosophila Transcription Network Project (BDTNP): https://shiny.mdc-berlin.de/DVEX/ ; (2) 10X Visium data of the human dorsolateral prefrontal cortex (DLPFC): http://spatial.libd.org/spatialLIBD/ ; (3) Adult mouse cortical cell datasets (GEO accession GSE71585): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71585 ; (4) Mouse embryo data: https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/ ; (5) Seurat objects ST data (10X Genomics Visium) of mouse brain: https://satijalab.org/seurat/articles/spatial_vignette.html ; (6) 10X Visium data of mouse brain: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-11114 ; (7) MERFISH data of mouse brain: https://portal.brain-map.org/atlases-and-data/bkp/abc-atlas ; (8) MERFISH data of human MTG: https://doi.org/10.5061/dryad.x3ffbg7mw ; (9) SMART-seq data of human MTG: https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smart-seq ; (10) Single-cell RNA-seq and Spatial Transcriptomics data of developing human heart: https://data.mendeley.com/datasets/mbvhhf8m62/2 ; (11) Human artery data: https://doi.org/10.5281/zenodo.15132967 . ..

    Sample Prep:

    Article Title: Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency
    Article Snippet: Pathological/Regenerative , Spatial multi-omic map of human MI (Myocardial Infarction) , snRNA-seq + snATAC-seq + Spatial Transcriptomics , Human infarcted heart , Yes (Post-MI timepoints) , Post-MI inflammatory environments induce batch-specific effects; regression-based correction (e.g., Harmony) improves comparability. Challenges include disease-induced heterogeneity complicating alignment, integrating chromatin/epigenetic with spatial data, temporal variability across MI stages, and sparsity in infarct zones. These issues impact remodelling maps, potentially biassing therapeutic target identification. , [ ] . .. , Human heart spatial transcriptomics (Disease) , Spatial Transcriptomics , Human heart (disease) , No (Disease states) , Differences in sample preparation (frozen vs. FFPE) require careful normalisation (e.g., using sctransform or DESeq2). Challenges involve tissue quality variations in diseased samples, artefact removal from pathology-induced noise, ensuring comparability across disease states, and handling low-resolution spots in heterogeneous lesions. These limitations affect disease progression modelling, risking inaccurate spatial gene expression profiles. , [ , ] . .. , Zebrafish heart regeneration atlas , scRNA-seq + Spatial Transcriptomics (Stereo-seq) , Zebrafish heart , Yes (8 timepoints of zebrafish heart regeneration stages) , Cross-species integration requires ortholog mapping + dimensionality reduction alignment. Challenges include species-specific gene expression differences, handling high-resolution Stereo-seq data volumes, temporal alignment across regeneration stages, and batch effects from injury timepoints. These issues influence comparative regenerative studies, potentially leading to translational gaps with human models , [ ] .

    Formalin-fixed Paraffin-Embedded:

    Article Title: Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency
    Article Snippet: Pathological/Regenerative , Spatial multi-omic map of human MI (Myocardial Infarction) , snRNA-seq + snATAC-seq + Spatial Transcriptomics , Human infarcted heart , Yes (Post-MI timepoints) , Post-MI inflammatory environments induce batch-specific effects; regression-based correction (e.g., Harmony) improves comparability. Challenges include disease-induced heterogeneity complicating alignment, integrating chromatin/epigenetic with spatial data, temporal variability across MI stages, and sparsity in infarct zones. These issues impact remodelling maps, potentially biassing therapeutic target identification. , [ ] . .. , Human heart spatial transcriptomics (Disease) , Spatial Transcriptomics , Human heart (disease) , No (Disease states) , Differences in sample preparation (frozen vs. FFPE) require careful normalisation (e.g., using sctransform or DESeq2). Challenges involve tissue quality variations in diseased samples, artefact removal from pathology-induced noise, ensuring comparability across disease states, and handling low-resolution spots in heterogeneous lesions. These limitations affect disease progression modelling, risking inaccurate spatial gene expression profiles. , [ , ] . .. , Zebrafish heart regeneration atlas , scRNA-seq + Spatial Transcriptomics (Stereo-seq) , Zebrafish heart , Yes (8 timepoints of zebrafish heart regeneration stages) , Cross-species integration requires ortholog mapping + dimensionality reduction alignment. Challenges include species-specific gene expression differences, handling high-resolution Stereo-seq data volumes, temporal alignment across regeneration stages, and batch effects from injury timepoints. These issues influence comparative regenerative studies, potentially leading to translational gaps with human models , [ ] .

    Biomarker Discovery:

    Article Title: Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency
    Article Snippet: Pathological/Regenerative , Spatial multi-omic map of human MI (Myocardial Infarction) , snRNA-seq + snATAC-seq + Spatial Transcriptomics , Human infarcted heart , Yes (Post-MI timepoints) , Post-MI inflammatory environments induce batch-specific effects; regression-based correction (e.g., Harmony) improves comparability. Challenges include disease-induced heterogeneity complicating alignment, integrating chromatin/epigenetic with spatial data, temporal variability across MI stages, and sparsity in infarct zones. These issues impact remodelling maps, potentially biassing therapeutic target identification. , [ ] . .. , Human heart spatial transcriptomics (Disease) , Spatial Transcriptomics , Human heart (disease) , No (Disease states) , Differences in sample preparation (frozen vs. FFPE) require careful normalisation (e.g., using sctransform or DESeq2). Challenges involve tissue quality variations in diseased samples, artefact removal from pathology-induced noise, ensuring comparability across disease states, and handling low-resolution spots in heterogeneous lesions. These limitations affect disease progression modelling, risking inaccurate spatial gene expression profiles. , [ , ] . .. , Zebrafish heart regeneration atlas , scRNA-seq + Spatial Transcriptomics (Stereo-seq) , Zebrafish heart , Yes (8 timepoints of zebrafish heart regeneration stages) , Cross-species integration requires ortholog mapping + dimensionality reduction alignment. Challenges include species-specific gene expression differences, handling high-resolution Stereo-seq data volumes, temporal alignment across regeneration stages, and batch effects from injury timepoints. These issues influence comparative regenerative studies, potentially leading to translational gaps with human models , [ ] .

    Gene Expression:

    Article Title: Integrating Spatial Omics and Deep Learning: Toward Predictive Models of Cardiomyocyte Differentiation Efficiency
    Article Snippet: Pathological/Regenerative , Spatial multi-omic map of human MI (Myocardial Infarction) , snRNA-seq + snATAC-seq + Spatial Transcriptomics , Human infarcted heart , Yes (Post-MI timepoints) , Post-MI inflammatory environments induce batch-specific effects; regression-based correction (e.g., Harmony) improves comparability. Challenges include disease-induced heterogeneity complicating alignment, integrating chromatin/epigenetic with spatial data, temporal variability across MI stages, and sparsity in infarct zones. These issues impact remodelling maps, potentially biassing therapeutic target identification. , [ ] . .. , Human heart spatial transcriptomics (Disease) , Spatial Transcriptomics , Human heart (disease) , No (Disease states) , Differences in sample preparation (frozen vs. FFPE) require careful normalisation (e.g., using sctransform or DESeq2). Challenges involve tissue quality variations in diseased samples, artefact removal from pathology-induced noise, ensuring comparability across disease states, and handling low-resolution spots in heterogeneous lesions. These limitations affect disease progression modelling, risking inaccurate spatial gene expression profiles. , [ , ] . .. , Zebrafish heart regeneration atlas , scRNA-seq + Spatial Transcriptomics (Stereo-seq) , Zebrafish heart , Yes (8 timepoints of zebrafish heart regeneration stages) , Cross-species integration requires ortholog mapping + dimensionality reduction alignment. Challenges include species-specific gene expression differences, handling high-resolution Stereo-seq data volumes, temporal alignment across regeneration stages, and batch effects from injury timepoints. These issues influence comparative regenerative studies, potentially leading to translational gaps with human models , [ ] .



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    Image Search Results


    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