spatial transcriptomic data  (TaKaRa)

Journal: Frontiers in Immunology

Article Title: Single-nucleus and spatial transcriptomics reveal intestinal cellular heterogeneity, differentiation, and cell communication mechanisms in SAP-induced intestinal injury

doi: 10.3389/fimmu.2026.1719902

Figure Lengend Snippet: Single-nucleus transcriptome and spatial transcriptomics landscape of the ileal tissue of SAP and CON group rats. (A) Schematic illustration of the workflow for this study. (B) Representative Hematoxylin and Eosin (H&E)–stained ileal sections from CON and SAP rats. (C) UMAP plot of single-nucleus transcriptome profiles of SAP and CON group samples. Colors indicate groups, clusters and cell types. (D) Heatmap plot of marker genes for cell annotation. (E) Bar plot showing cell-type proportions (mean ± SEM) in snRNA-seq data. (F) Spatial transcriptomics profiles of SAP and CON group samples. Colors indicate cell types. (G) Bar plot showing cell-type proportions (mean ± SEM) in spatial transcriptomics (Stereo-seq) data. Statistical significance: ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Article Snippet: The spatial transcriptomics data were obtained according to the protocol of STOmics Gene Expression Set-S1 on the website ( https://www.stomics.tech/ ), which is an improved version of initial procedures.

Techniques: Spatial Transcriptomics, Staining, Marker

Journal: bioRxiv

Article Title: Spatiotemporal Atlas of Heart Development Reveals Blood-Flow-Dependent Cellular, Structural, Metabolic, and Spatial Remodeling

doi: 10.64898/2025.12.09.693024

Figure Lengend Snippet: (A) Schematic overview of sample collection and surgical perturbation. Heart samples were collected at multiple developmental stages and processed for single-cell or spatial transcriptomic profiling. Hemodynamic perturbations were introduced by left atrial ligation (LAL) or right atrial ligation (RAL). (B) Workflow of scRNA-seq analysis. Cell type annotation was performed hierarchically, beginning with coarse classification followed by granular subtypes. RNA velocity analysis was carried out independently within each coarse cell type. (C) Overview of cell type and plasticity score mapping. Cell types were assigned to spatial spots using cell2location based on the dominant contributing cell type. Plasticity scores were computed for each spot as the product of the cell type proportion and its corresponding cell type plasticity score. (D) Schematic of manual region annotation with Napari for left ventricle (LV) and right ventricle (RV) analysis. (E) Schematic of subdomain identification for valve neighborhood analysis with recursive GraphST.

Article Snippet: Preprocessing of Spatial transcriptomic data (Seeker, Curio) was performed with the scanpy package .

Techniques: Ligation

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

Techniques: RNA Sequencing, Sequencing, RNAscope, Ligation

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

Techniques: Genome Wide