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visium v2 human transcriptome gene expression kit  (10X Genomics)

 
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    Structured Review

    10X Genomics visium v2 human transcriptome gene expression kit
    Visium V2 Human Transcriptome Gene Expression Kit, 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+transcriptomics+platform/expression+gene+slides+spatial+visium/pm42168176-401-7-14
    Average 86 stars, based on 1 article reviews
    visium v2 human transcriptome gene expression kit - by Bioz Stars, 2026-10
    86/100 stars

    Images

    Related Articles

    Gene Expression:

    Article Title: Mapping neuro-vascular unit communications reveals distinct angiogenic programs across developing mouse brain regions.
    Article Snippet: .. Reactions were carried out with the Visium Spatial Gene Expression (GEX) and Tissue Optimization (TO) Slide & Reagent Kits according to the manufacturer’s protocol with recommended reagents (10X Genomics, 1000193 and 1000187). ..

    Article Title: A glycan-based adjuvant expands the breadth and duration of protection of mRNA-based vaccines.
    Article Snippet: DLS (ZetaPlus Zeta Potential Analyzer; Brookhaven Instruments) was performed to determine the stability of each active pharmaceutical ingredient in the FDC solution. .. 5′ gene expression (GEX) libraries, single-cell V(D)J libraries (10x Genomics, 1000255) and cell surface protein libraries were prepared according to the manufacturer’s protocols. .. Library quality was evaluated using an Agilent 2200 TapeStation, and final libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina platforms.

    Article Title: WNT signaling in human pluripotent stem cells promotes HDAC2-dependent epigenetic programs and development of retinoic acid-responsive mesoderm.
    Article Snippet: 10,000 nuclei per sample were processed and isolated using the Nuclei EZ Prep kit (Sigma-Aldrich) according to the manufacturer’s protocol. .. Libraries for both ATAC-seq and RNA-seq were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle (10x Genomics, PN-1000283). .. Cells were first stained for aldehyde dehydrogenase expression using the ALDEFLUOR Kit (StemCell Technologies, Cat# 01700) according to the manufacturer’s protocol.

    Single Cell:

    Article Title: A glycan-based adjuvant expands the breadth and duration of protection of mRNA-based vaccines.
    Article Snippet: DLS (ZetaPlus Zeta Potential Analyzer; Brookhaven Instruments) was performed to determine the stability of each active pharmaceutical ingredient in the FDC solution. .. 5′ gene expression (GEX) libraries, single-cell V(D)J libraries (10x Genomics, 1000255) and cell surface protein libraries were prepared according to the manufacturer’s protocols. .. Library quality was evaluated using an Agilent 2200 TapeStation, and final libraries were quantified by qPCR using the KAPA Library Quantification Kit for Illumina platforms.

    Article Title: Multilayer network approaches to omics data integration in digital twins for cancer research
    Article Snippet: .. This process requires the non-trivial development of new methods that capture salient information from either estimation of cell type proportions from bulk RNA-seq , or from single-cell spatial (Visium/Xenium) ( ) and non-spatial datasets (10x Genomics scRNA-seq/scATAC-seq) ( ). ..

    Article Title: WNT signaling in human pluripotent stem cells promotes HDAC2-dependent epigenetic programs and development of retinoic acid-responsive mesoderm.
    Article Snippet: 10,000 nuclei per sample were processed and isolated using the Nuclei EZ Prep kit (Sigma-Aldrich) according to the manufacturer’s protocol. .. Libraries for both ATAC-seq and RNA-seq were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle (10x Genomics, PN-1000283). .. Cells were first stained for aldehyde dehydrogenase expression using the ALDEFLUOR Kit (StemCell Technologies, Cat# 01700) according to the manufacturer’s protocol.

    RNA Sequencing:

    Article Title: Multilayer network approaches to omics data integration in digital twins for cancer research
    Article Snippet: .. This process requires the non-trivial development of new methods that capture salient information from either estimation of cell type proportions from bulk RNA-seq , or from single-cell spatial (Visium/Xenium) ( ) and non-spatial datasets (10x Genomics scRNA-seq/scATAC-seq) ( ). ..

    Article Title: WNT signaling in human pluripotent stem cells promotes HDAC2-dependent epigenetic programs and development of retinoic acid-responsive mesoderm.
    Article Snippet: 10,000 nuclei per sample were processed and isolated using the Nuclei EZ Prep kit (Sigma-Aldrich) according to the manufacturer’s protocol. .. Libraries for both ATAC-seq and RNA-seq were prepared using the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle (10x Genomics, PN-1000283). .. Cells were first stained for aldehyde dehydrogenase expression using the ALDEFLUOR Kit (StemCell Technologies, Cat# 01700) according to the manufacturer’s protocol.

    other:

    Article Title: Melanoma cell states shape spatial tumor-immune ecosystems to dictate the efficacy of anti-PD1 immunotherapy.
    Article Snippet: Spatial transcriptomic data were generated by the CRCL (Cancer Research Center Of Lyon) Cancer Genomics Platform using Visium CytAssist Spatial gene expression for FFPE, Human Transcriptome, kit (10X Genomics), according to the manufacturer’s guidelines.



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    (A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial <t>transcriptomics</t> to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.
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    Image Search Results


    Integration of Single-cell transcriptomics datasets. a Diagram depicting the single-cell transcriptomics dataset utilized. b Highlighted transcriptional states selected from each single-cell transcriptomics dataset, demarcated with dotted lines. c UMAP plot showing 14 distinct integrated clusters labeled 0–13, comprising a total of 222,822 cells. d Quantification of individual cell state contributions to the integrated transcriptional state. e – f Gene expression analysis within each transcriptional state, referencing studies by Yun Chen et al. and Sun Victor et al. . Cluster numbers and gene names are highlighted with the same color code to indicate enrichment. Note: Xenografted-mic term used for Xenografted-microglia

    Journal: Alzheimer's Research & Therapy

    Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

    doi: 10.1186/s13195-025-01944-y

    Figure Lengend Snippet: Integration of Single-cell transcriptomics datasets. a Diagram depicting the single-cell transcriptomics dataset utilized. b Highlighted transcriptional states selected from each single-cell transcriptomics dataset, demarcated with dotted lines. c UMAP plot showing 14 distinct integrated clusters labeled 0–13, comprising a total of 222,822 cells. d Quantification of individual cell state contributions to the integrated transcriptional state. e – f Gene expression analysis within each transcriptional state, referencing studies by Yun Chen et al. and Sun Victor et al. . Cluster numbers and gene names are highlighted with the same color code to indicate enrichment. Note: Xenografted-mic term used for Xenografted-microglia

    Article Snippet: Further, the Visium SD spatial transcriptomics platform is limited by its resolution, as each capture spot (< 55 μm) often contains transcripts from multiple cells, which is improved from first report (capture > 10,000 transcripts per spot) [ ].

    Techniques: Single-cell Transcriptomics, Labeling, Gene Expression

    Microglial transcriptional shift in response to AD pathology. a Spatial transcriptomics (SRT) of the Middle Temporal Gyrus (MTG) in Alzheimer's disease (AD), with each section being 10 µm thick. b Visium spots highlighting the top 25% highest probability for Homeostatic, DAM, MHCII, Neuronal Surveillance and Inflammatory-I states. c Heatmap illustrating the fraction of predicted transcriptional states within each cortical layer. d Overview of spatial transcriptomics Aβ localization. Aβ-proximal spots refer to those directly overlapping Aβ plaques, while all others are considered Aβ-distal. e Upper: Quantification of transcriptional states around proximal and distal Aβ spots for Combined II-VI, External II-III, and Internal IV-VI cortical layers. f - j SRT sample from AD frontal cortex from van Olst et al. . f Spatially resolved clusters based on gene expression from van Olst et al. AD sample. g Cortical layers identified based on main layer markers reported in van Olst et al., shown in panel h . The grey matter layers were identified as External (Layers I-III) and Internal (Layers IV-VI). Meninges and white mater were not considered in the analysis. i The Homeostatic and DAM enriched spots identified across the grey matter. j Proportion of Homeostatic and DAM enriched spots in each Internal and External layers. Chi-square significance tests were used to calculate p-values (refer Fig. S9 for other transcriptional states). Note: Xenografted-mic term used for Xenografted-microglia

    Journal: Alzheimer's Research & Therapy

    Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

    doi: 10.1186/s13195-025-01944-y

    Figure Lengend Snippet: Microglial transcriptional shift in response to AD pathology. a Spatial transcriptomics (SRT) of the Middle Temporal Gyrus (MTG) in Alzheimer's disease (AD), with each section being 10 µm thick. b Visium spots highlighting the top 25% highest probability for Homeostatic, DAM, MHCII, Neuronal Surveillance and Inflammatory-I states. c Heatmap illustrating the fraction of predicted transcriptional states within each cortical layer. d Overview of spatial transcriptomics Aβ localization. Aβ-proximal spots refer to those directly overlapping Aβ plaques, while all others are considered Aβ-distal. e Upper: Quantification of transcriptional states around proximal and distal Aβ spots for Combined II-VI, External II-III, and Internal IV-VI cortical layers. f - j SRT sample from AD frontal cortex from van Olst et al. . f Spatially resolved clusters based on gene expression from van Olst et al. AD sample. g Cortical layers identified based on main layer markers reported in van Olst et al., shown in panel h . The grey matter layers were identified as External (Layers I-III) and Internal (Layers IV-VI). Meninges and white mater were not considered in the analysis. i The Homeostatic and DAM enriched spots identified across the grey matter. j Proportion of Homeostatic and DAM enriched spots in each Internal and External layers. Chi-square significance tests were used to calculate p-values (refer Fig. S9 for other transcriptional states). Note: Xenografted-mic term used for Xenografted-microglia

    Article Snippet: Further, the Visium SD spatial transcriptomics platform is limited by its resolution, as each capture spot (< 55 μm) often contains transcripts from multiple cells, which is improved from first report (capture > 10,000 transcripts per spot) [ ].

    Techniques: Gene Expression

    Spatial distribution of microglial activation across cortical layers in Alzheimer’s disease (AD) brain. a Immunofluorescence (IF) staining of P2RY12 and Aβ on adjacent Sects. (10 µm interval) from the Middle Temporal Gyrus of an AD donor, aligned to 10X Genomics Visium spatial transcriptomics spots (color-coded) across cortical layers II–VI (Chen et al., ANC, 2022) . High-magnification images show nuclei (DAPI, gray), homeostatic microglia (P2RY12, magenta), and Aβ plaques (blue) in external layers II–III (top) and internal layers IV–VI (bottom). b Quantification of IF-stained P2RY12⁺ cells and Aβ⁺ plaques across cortical layers II–III and IV–VI in AD samples. Bar plots display normalized counts for: Upper Left—P2RY12⁺ cells; Upper Right—Aβ⁺ plaques; Lower Left—P2RY12⁺/Aβ⁺ overlap; Lower Right—P2RY12⁺/Aβ⁻ plaques. Counts were normalized to the total number within layers II–VI. c IF co-staining of Aβ (red) and phosphorylated tau (pTAU, green) in frontal cortex sections with AD pathology (Section A). Nuclei stained with DAPI (blue). Adjacent section (Section B) stained for CD68 (red), a marker of activated microglia. d Quantification of CD68⁺ cells across cortical layers in AD frontal cortex. Graph shows distribution of CD68⁺ and CD68⁻ cells in external versus internal layers

    Journal: Alzheimer's Research & Therapy

    Article Title: Exploring cellular heterogeneity: single-cell and spatial transcriptomics of Alzheimer's disease brains and iPSC-derived microglia

    doi: 10.1186/s13195-025-01944-y

    Figure Lengend Snippet: Spatial distribution of microglial activation across cortical layers in Alzheimer’s disease (AD) brain. a Immunofluorescence (IF) staining of P2RY12 and Aβ on adjacent Sects. (10 µm interval) from the Middle Temporal Gyrus of an AD donor, aligned to 10X Genomics Visium spatial transcriptomics spots (color-coded) across cortical layers II–VI (Chen et al., ANC, 2022) . High-magnification images show nuclei (DAPI, gray), homeostatic microglia (P2RY12, magenta), and Aβ plaques (blue) in external layers II–III (top) and internal layers IV–VI (bottom). b Quantification of IF-stained P2RY12⁺ cells and Aβ⁺ plaques across cortical layers II–III and IV–VI in AD samples. Bar plots display normalized counts for: Upper Left—P2RY12⁺ cells; Upper Right—Aβ⁺ plaques; Lower Left—P2RY12⁺/Aβ⁺ overlap; Lower Right—P2RY12⁺/Aβ⁻ plaques. Counts were normalized to the total number within layers II–VI. c IF co-staining of Aβ (red) and phosphorylated tau (pTAU, green) in frontal cortex sections with AD pathology (Section A). Nuclei stained with DAPI (blue). Adjacent section (Section B) stained for CD68 (red), a marker of activated microglia. d Quantification of CD68⁺ cells across cortical layers in AD frontal cortex. Graph shows distribution of CD68⁺ and CD68⁻ cells in external versus internal layers

    Article Snippet: Further, the Visium SD spatial transcriptomics platform is limited by its resolution, as each capture spot (< 55 μm) often contains transcripts from multiple cells, which is improved from first report (capture > 10,000 transcripts per spot) [ ].

    Techniques: Activation Assay, Immunofluorescence, Staining, Marker

    The timeline of technological developments in exploring musculoskeletal diseases spans multiple biological levels, including transcriptomics, epigenomics, proteomics, and metabolomics

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: The timeline of technological developments in exploring musculoskeletal diseases spans multiple biological levels, including transcriptomics, epigenomics, proteomics, and metabolomics

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques:

    Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in cartilage

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in cartilage

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques:

    Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in synovium

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of advanced epigenomics, transcriptomics, proteomics, and metabolomics in synovium

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques:

    Application of advanced transcriptomics and metabolomics in bone cells and bony callus

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of advanced transcriptomics and metabolomics in bone cells and bony callus

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques:

    Application of advanced transcriptomics, proteomics, and metabolomics in intervertebral disc

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of advanced transcriptomics, proteomics, and metabolomics in intervertebral disc

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques:

    Application of single-cell RNA-seq and spatial transcriptomics in Tendon

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of single-cell RNA-seq and spatial transcriptomics in Tendon

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques: RNA Sequencing

    Application of single-cell RNA-seq, single-nucleus RNA-seq, spatial transcriptomics, and metabolomics in muscle

    Journal: Bone Research

    Article Title: Current cutting-edge omics techniques on musculoskeletal tissues and diseases

    doi: 10.1038/s41413-025-00442-z

    Figure Lengend Snippet: Application of single-cell RNA-seq, single-nucleus RNA-seq, spatial transcriptomics, and metabolomics in muscle

    Article Snippet: Using the Visium CytAssist spatial transcriptomics platform, researchers successfully mapped genes associated with hard callus (e.g., Dmp1 and Sost ) and soft callus (e.g., Acan and Col2a1 ) while preserving the spatial integrity of the tissue.

    Techniques: RNA Sequencing

    (A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial transcriptomics to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.

    Journal: bioRxiv

    Article Title: PLXNB1 and other signaling drives a pathologic astrocyte state contributing to cognitive decline in Alzheimer’s Disease

    doi: 10.1101/2025.02.24.639868

    Figure Lengend Snippet: (A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial transcriptomics to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.

    Article Snippet: Fresh-frozen dorsolateral prefrontal cortex (DLPFC) samples from ROSMAP participants were processed using the Visium Spatial Transcriptomics (ST) platform, coupled with immunofluorescence.

    Techniques: Expressing, Biomarker Discovery, Immunohistochemistry, Marker, Derivative Assay, Activity Assay, Quantitative Proteomics