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visium spatial gene expression slides  (10X Genomics)

 
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    10X Genomics visium spatial gene expression slides
    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled <t>using</t> <t>10x</t> <t>Visium</t> spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
    Visium Spatial Gene Expression Slides, supplied by 10X Genomics, 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/visium+spatial+gene+expression+slides/bio_rxiv__64898__2026__06__02__729569-152-10-15?v=10X+Genomics
    Average 86 stars, based on 1 article reviews
    visium spatial gene expression slides - by Bioz Stars, 2026-08
    86/100 stars

    Images

    1) Product Images from "Spatial Transcriptomics Reveals a Conserved Border Niche and Etiology-Associated Immune Rewiring in Hepatocellular Carcinoma"

    Article Title: Spatial Transcriptomics Reveals a Conserved Border Niche and Etiology-Associated Immune Rewiring in Hepatocellular Carcinoma

    Journal: bioRxiv

    doi: 10.64898/2026.06.02.729569

    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled using 10x Visium spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
    Figure Legend Snippet: This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled using 10x Visium spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.

    Techniques Used: Spatial Transcriptomics, Construct, Staining, Derivative Assay



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    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled <t>using</t> <t>10x</t> <t>Visium</t> spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
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    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled <t>using</t> <t>10x</t> <t>Visium</t> spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
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    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled <t>using</t> <t>10x</t> <t>Visium</t> spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
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    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled <t>using</t> <t>10x</t> <t>Visium</t> spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.
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    Image Search Results


    This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled using 10x Visium spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.

    Journal: bioRxiv

    Article Title: Spatial Transcriptomics Reveals a Conserved Border Niche and Etiology-Associated Immune Rewiring in Hepatocellular Carcinoma

    doi: 10.64898/2026.06.02.729569

    Figure Lengend Snippet: This figure summarizes the machine learning-guided workflow used to define tumor, boundary, and stromal domains in hepatocellular carcinoma specimens. ( a) Study workflow. Eleven treatment-naive HCC resection specimens, including seven HBV-related and four non-B non-C cases, were profiled using 10x Visium spatial transcriptomics. CancerFinder was used to estimate spot-level malignancy probability, SpaceFlow was used for spatially regularized clustering, and the resulting outputs were integrated with histologic review to define three spatial domains: Tumor, Boundary, and Stroma. A signed distance-to-border axis was constructed for continuous spatial gradient analyses. ( b) Hematoxylin and eosin-stained sections from representative HBV-related and NBNC specimens. ( c) CancerFinder-derived cancer/normal classification maps for the same specimens. Blue indicates normal or low-malignancy-probability regions, and orange indicates cancer or high-malignancy-probability regions. ( d) Integrated three-domain spatial annotation maps. Dark red indicates Boundary, cyan indicates Stroma, and dark blue indicates Tumor. ( e) Boxplots of estimated malignant cell proportion across ordered spatial subdomains in the representative specimens. The red line indicates mean distance from the tumor for each subdomain, supporting concordance between the inferred malignancy gradient and the spatial domain hierarchy.

    Article Snippet: Specimens were OCT-embedded, snap-frozen, and cryosectioned at 10 um onto Visium Spatial Gene Expression slides (10x Genomics).

    Techniques: Spatial Transcriptomics, Construct, Staining, Derivative Assay