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10x genomics visium workflow  (10X Genomics)

 
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    10X Genomics 10x genomics visium workflow
    10x Genomics Visium Workflow, 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/10x+genomics+visium+workflow/cytassist+platform+visium/pm42144098-66-8-8
    Average 86 stars, based on 1 article reviews
    10x genomics visium workflow - by Bioz Stars, 2026-11
    86/100 stars

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    Related Articles

    other:

    Article Title: Spatial multi-omics defines cancer-associated fibroblasts subtype gradients driving metabolic support and immune remodeling in pancreatic ductal adenocarcinoma.
    Article Snippet: Pancreatic ductal adenocarcinoma is characterized by a fibrotic and metabolically active tumor microenvironment where cancer-associated fibroblasts (CAFs) mediate metabolic crosstalk, extracellular matrix (ECM) remodeling, and immune regulation.. However, the metabolic and spatial heterogeneity of CAFs remains incompletely understood.. We integrated spatial transcriptomics and spatial metabolomics data from PDAC tissues and performed SpatialGlue-based multimodal clustering to define CAF subtypes.



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    A) Data collection protocol for <t>Visium</t> spatial transcriptomics slide. B) Training protocol for spatial omics-driven crossmodal contrastive model; two views are generated per modality, per patch; each view is passed through the corresponding branch of the crossmodal model; embeddings are transformed using a shared projection head; unimodal and crossmodal contrastive losses are applied to output embeddings.
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    A) Data collection protocol for Visium spatial transcriptomics slide. B) Training protocol for spatial omics-driven crossmodal contrastive model; two views are generated per modality, per patch; each view is passed through the corresponding branch of the crossmodal model; embeddings are transformed using a shared projection head; unimodal and crossmodal contrastive losses are applied to output embeddings.

    Journal: bioRxiv

    Article Title: Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

    doi: 10.1101/2023.07.30.551187

    Figure Lengend Snippet: A) Data collection protocol for Visium spatial transcriptomics slide. B) Training protocol for spatial omics-driven crossmodal contrastive model; two views are generated per modality, per patch; each view is passed through the corresponding branch of the crossmodal model; embeddings are transformed using a shared projection head; unimodal and crossmodal contrastive losses are applied to output embeddings.

    Article Snippet: This process was conducted with a 10x Genomics Visium CytAssist workflow using clinical-grade H&E staining on a Leica Bond instrument, followed by coverslipping and 40x WSI on the Aperio GT450 platform.

    Techniques: Generated, Transformation Assay