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10X Genomics visium
Visium, 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/visium/pm42115298-41-4-5
Average 86 stars, based on 1 article reviews
visium - by Bioz Stars, 2026-09
86/100 stars

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Related Articles

Spatial Transcriptomics:

Article Title: GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.
Article Snippet: .. The typical evolution of spatial transcriptomics technologies is characterized by continuous improvement in spatial resolution, ranging from 10x Genomics Visium (55 μm spot resolution, AR TIC LE IN PR ES S capturing approximately 1-10 cells per spot) [5], to Slide-seq (~10 μm, near-cellular resolution) [6], and finally to Stereo-seq (0.22 μm, subcellular resolution) [7]. ..

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

Article Title: GLP1-E2 therapy delays autoimmune diabetes in late-stage prediabetic NOD mice and potentiates low-dose anti-CD3 therapy for enhanced disease protection.
Article Snippet: .. Analytically, we employed 10X Genomics Visium for spatial transcriptomics, which lacks true single-cell resolution compared with newer platforms such as Visium HD or Xenium. ..

Article Title: Analysis of unmapped RNA-seq data from cancer spatial transcriptome toward characterizing cancer microbiome.
Article Snippet: .. Recently, spatial transcriptomics, especially Visium (10X genomics, USA), has been garnering significant attention in cancer research. ..

Fluorescence:

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

In Situ Hybridization:

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

Labeling:

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

Gene Expression:

Article Title: Understanding nerve–tumor interactions: From basic biology to therapeutic innovation
Article Snippet: .. 2D spatial transcriptomics, such as Visium (10x Genomics) and MERFISH (Multiplexed Error-Robust Fluorescence In Situ Hybridization), enable the precise mapping of transcriptomes to their corresponding locations in a 2D plane., , Compared with high-plex protein labeling, 2D spatial transcriptomics offers high-resolution spatial gene expression data and facilitates the discovery of novel spatially regulated genes within the TME. ..

other:

Article Title: Integrative single-cell and spatial multi-Omics analyses identify NETs-driven colon cancer subtypes with distinct metabolic features and prognostic implications.
Article Snippet: ACC, Adrenocortical Carcinoma; ARRDC1, Arrestin Domain Containing 1; AUC, Area Under the Curve; AUCell, Area Under the Curve Cell-level scoring; BH, Benjamini–Hochberg; BLCA, Bladder Urothelial Carcinoma; AR TIC LE IN PR ES S BRCA, Breast Cancer; CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; CHOL, Cholangiocarcinoma; CI, Confidence Interval; CNV, Copy Number Variation; COAD, Colon Adenocarcinoma; CRC, Colorectal Cancer; CSS, Cancer-Specific Survival; CXCR4, C-X-C Chemokine Receptor Type 4; DEGs, Differentially Expressed Genes; DUSP5, Dual-Specificity Phosphatase 5; ELISA, Enzyme-Linked Immunosorbent Assay; eQTL, Expression Quantitative Trait Locus; ESCA, Esophageal Carcinoma; FDR, False Discovery Rate; GDSC, Genomics of Drug Sensitivity in Cancer; GEO, Gene Expression Omnibus; GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; GWAS, Genome-Wide Association Study; HNSC, Head and Neck Squamous Cell Carcinoma; HPA, Human Protein Atlas; HR, Hazard Ratio; IC50, Half-Maximal Inhibitory Concentration; ICIs, Immune Checkpoint Inhibitors; KEGG, Kyoto Encyclopedia of Genes and Genomes; KICH, Kidney Chromophobe; KIRC, Kidney Renal Clear Cell Carcinoma; KM, Kaplan–Meier; AR TIC LE IN PR ES S LASSO, Least Absolute Shrinkage and Selection Operator; LGG, Low-Grade Glioma; LUAD, Lung Adenocarcinoma; MAPK, Mitogen-Activated Protein Kinase; MD, Molecular Dynamics; MESO, Mesothelioma; MIA, Multimodal Intersection Analysis; MIF, Macrophage Migration Inhibitory Factor; NETs, Neutrophil Extracellular Traps; OS, Overall Survival; PCA, Principal Component Analysis; READ, Rectal Adenocarcinoma; RMSD, Root Mean Square Deviation; ROC, Receiver Operating Characteristic; ROS, Reactive Oxygen Species; SARC, Sarcoma; scRNA-seq, Single-Cell RNA Sequencing; SLC2A3, Solute Carrier Family 2 Member 3; SNV, Single Nucleotide Variant; ssGSEA, Single-Sample Gene Set Enrichment Analysis; STAD, Stomach Adenocarcinoma; TAMs, Tumor-Associated Macrophages; TANs, Tumor-Associated Neutrophils; TCGA, The Cancer Genome Atlas; THCA, Thyroid Carcinoma; THYM, Thymoma; TIDE, Tumor Immune Dysfunction and Exclusion; TME, Tumor Microenvironment; TPM, Transcripts Per Million; AR TIC LE IN PR ES S Tregs, Regulatory T Cells; TUBB2A, Tubulin Beta Class 2A; UCEC, Uterine Corpus Endometrial Carcinoma; UMI, Unique Molecular Identifier; UVM, Uveal Melanoma; Visium, 10x Genomics Visium

Single Cell:

Article Title: GLP1-E2 therapy delays autoimmune diabetes in late-stage prediabetic NOD mice and potentiates low-dose anti-CD3 therapy for enhanced disease protection.
Article Snippet: .. Analytically, we employed 10X Genomics Visium for spatial transcriptomics, which lacks true single-cell resolution compared with newer platforms such as Visium HD or Xenium. ..

Expressing:

Article Title: SpatialBench : Comparative cross-platform benchmarking of high-resolution spatial transcriptomics using matched mouse lymphoid tissue
Article Snippet: .. The original Visium (10x Genomics) became the most widely adopted sST platform ( ) , offering transcriptome-wide coverage while aggregating expression from multiple cells per 55 μm resolution that aggregates expression from multiple cells per spot ( , ) . ..



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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