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spatial transcriptomic data  (Complete Genomics Inc)


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    Complete Genomics Inc spatial transcriptomic data
    Spatial Transcriptomic Data, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 99/100, based on 485 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stereo+seq+data/Stereo-seq+Transcriptomics+Set+for+FFPE/pm41449269-436-2-7
    Average 99 stars, based on 485 article reviews
    spatial transcriptomic data - by Bioz Stars, 2026-11
    99/100 stars

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    Related Articles

    Spatial Transcriptomics:

    Article Title: A pan cancer multi omics analysis of OLR1 reveals its association with tumor immunity metabolic pathways and clinical prognosis
    Article Snippet: .. Spatial transcriptomics datasets were obtained from the STOmics database (https://db.cngb.org/stomics/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/), including STDS0000027, STDS0000033, STDS0000046, GSM5924031, and GSM6505133. ..

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    Analysis:

    Article Title: Diverse infections transcriptionally reprogram the intestinal epithelium and epithelial-immune cell interactions.
    Article Snippet: .. Indeed, using Stereo-seq (STOmics) spatial transcriptomic analysis, which profiles host and microbial RNA, we found that Yersinia transcripts were localized to the basal region of the PG and did not extend to the enterocytes overlaying the PG (Supplementary Fig. 10g). ..

    Sequencing:

    Article Title: Multi-omics integration and machine learning identify an ACKR1 + endothelial cell-based lactate-immune signature associated with carotid plaque vulnerability and progression.
    Article Snippet: .. The internal singlecell and spatial transcriptomes were deposited with our previous study (Lai et al., Nat Cardiovasc Res, 2025) in the Genome Sequence Archive for Human at the National Genomics Data Center under accession HRA006030 (https://ngdc.cncb.ac.cn/gsa-human), with backups in the China National GeneBank: spatial transcriptomes in the STOmics Database (STT0000052, https://db.cngb.org/stomics/) and single-cell data in the China National Sequence Archive (CNP0004894, https:// db.cngb.org/cnsa/). ..

    Single Cell:

    Article Title: Multi-omics integration and machine learning identify an ACKR1 + endothelial cell-based lactate-immune signature associated with carotid plaque vulnerability and progression.
    Article Snippet: .. The internal singlecell and spatial transcriptomes were deposited with our previous study (Lai et al., Nat Cardiovasc Res, 2025) in the Genome Sequence Archive for Human at the National Genomics Data Center under accession HRA006030 (https://ngdc.cncb.ac.cn/gsa-human), with backups in the China National GeneBank: spatial transcriptomes in the STOmics Database (STT0000052, https://db.cngb.org/stomics/) and single-cell data in the China National Sequence Archive (CNP0004894, https:// db.cngb.org/cnsa/). ..

    Activation Assay:

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    Tissue:

    Article Title: Spi1-Soat1 Transcriptional axis drives microglial lipid droplet accumulation and secondary brain injury after intracerebral hemorrhage
    Article Snippet: .. Cholesterol metabolic pathway activation and microglia-specific upregulation of Soat1 are tightly associated with lipid droplet accumulation after ICH To define the core metabolic pathways and key regulators driving microglial LD accumulation after ICH, we analyzed the day 7 perihematomal tissue subset of a spatial transcriptomics dataset (STOmics DB, ID:STT0000047), with a focus on lipid metabolic cascades underlying LD biogenesis (Fig. 3A). ..

    H&E Stain:

    Article Title: Spatial transcriptomic profiling of porcine tissue microarray detecting subclinical circovirus infection
    Article Snippet: .. The first section was subjected to H&E staining for histological evaluation, while the second was mounted directly onto a Stereo-seq chip N Slide V1.0 (STOmics, Cat. No. 210CN114). .. Subsequent processing was performed according to the Stereo-seq Transcriptomics Set for FFPE User Manual (Document No. STUM-TT004) using the Stereo-seq Transcriptomics N Kit V1.0 (Cat. No. 211KN114) and Stereo-seq 16 Barcode Library Preparation Kit V1.0 (Cat. No. 111KL160).



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    a , UMAP of GCs, colored by seven GC subtypes (Progenitor, Preantral 1, Preantral 2, Mitotic 1, Mitotic 2, Antral Mural and Atretic). b , Feature plots of representative subtype markers on the UMAP. c , Monocle3 pseudotime trajectory inferred for GCs, with the principal graph overlaid and direction indicated from progenitor toward antral mural cells. d , Heatmap of representative genes showing coordinated expression changes along the progenitor-to-mural trajectory (expression shown as z-scores). e , Heatmap of Hallmark ssGSEA scores across granulosa subtypes (z-scored per gene set). f , H&E image of a Stereo-seq <t>FF</t> <t>V1.3</t> mouse ovary section (6-8 weeks old), with representative regions (α–θ) indicated. Scale bar, 100 μm. g , Cell2location-based spatial mapping of GC subtypes at cell-bin resolution. h , Zoom-in views of representative regions (α, β, γ, and θ) showing H&E morphology and spatial expression of selected marker genes. Scale bar, 50 μm.
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    Single-nucleus transcriptome and spatial <t>transcriptomics</t> 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.
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    Single-nucleus transcriptome and spatial <t>transcriptomics</t> 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.
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    Single-nucleus transcriptome and spatial <t>transcriptomics</t> 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.
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    Image Search Results


    a , UMAP of GCs, colored by seven GC subtypes (Progenitor, Preantral 1, Preantral 2, Mitotic 1, Mitotic 2, Antral Mural and Atretic). b , Feature plots of representative subtype markers on the UMAP. c , Monocle3 pseudotime trajectory inferred for GCs, with the principal graph overlaid and direction indicated from progenitor toward antral mural cells. d , Heatmap of representative genes showing coordinated expression changes along the progenitor-to-mural trajectory (expression shown as z-scores). e , Heatmap of Hallmark ssGSEA scores across granulosa subtypes (z-scored per gene set). f , H&E image of a Stereo-seq FF V1.3 mouse ovary section (6-8 weeks old), with representative regions (α–θ) indicated. Scale bar, 100 μm. g , Cell2location-based spatial mapping of GC subtypes at cell-bin resolution. h , Zoom-in views of representative regions (α, β, γ, and θ) showing H&E morphology and spatial expression of selected marker genes. Scale bar, 50 μm.

    Journal: bioRxiv

    Article Title: Single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation

    doi: 10.64898/2026.03.11.710939

    Figure Lengend Snippet: a , UMAP of GCs, colored by seven GC subtypes (Progenitor, Preantral 1, Preantral 2, Mitotic 1, Mitotic 2, Antral Mural and Atretic). b , Feature plots of representative subtype markers on the UMAP. c , Monocle3 pseudotime trajectory inferred for GCs, with the principal graph overlaid and direction indicated from progenitor toward antral mural cells. d , Heatmap of representative genes showing coordinated expression changes along the progenitor-to-mural trajectory (expression shown as z-scores). e , Heatmap of Hallmark ssGSEA scores across granulosa subtypes (z-scored per gene set). f , H&E image of a Stereo-seq FF V1.3 mouse ovary section (6-8 weeks old), with representative regions (α–θ) indicated. Scale bar, 100 μm. g , Cell2location-based spatial mapping of GC subtypes at cell-bin resolution. h , Zoom-in views of representative regions (α, β, γ, and θ) showing H&E morphology and spatial expression of selected marker genes. Scale bar, 50 μm.

    Article Snippet: Publicly available mouse ovary Stereo-seq Transcriptomics FF v1.3 demo data were obtained from the STOmics website ( https://www.stomics.tech/col1347 ).

    Techniques: Expressing, Marker

    Spatial maps showing cell2location-predicted localization of each annotated cell subtype on the Stereo-seq FF V1.3 ovary section (6–8 weeks old), displayed separately by subtype. Scale bar, 100 μm

    Journal: bioRxiv

    Article Title: Single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation

    doi: 10.64898/2026.03.11.710939

    Figure Lengend Snippet: Spatial maps showing cell2location-predicted localization of each annotated cell subtype on the Stereo-seq FF V1.3 ovary section (6–8 weeks old), displayed separately by subtype. Scale bar, 100 μm

    Article Snippet: Publicly available mouse ovary Stereo-seq Transcriptomics FF v1.3 demo data were obtained from the STOmics website ( https://www.stomics.tech/col1347 ).

    Techniques:

    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.

    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