visium tissue section test slides Search Results


86
10X Genomics visium cassette with gasket
Visium Cassette With Gasket, 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
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10X Genomics visium ffpe spatial gene expression slides
Visium Ffpe 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
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86
10X Genomics visium datasets
Integration of spatially resolved <t> transcriptomic </t> data with other methods
Visium Datasets, 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
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86
10X Genomics slide based technique
Integration of spatially resolved <t> transcriptomic </t> data with other methods
Slide Based Technique, 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
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86
10X Genomics visium user guide
Integration of spatially resolved <t> transcriptomic </t> data with other methods
Visium User Guide, 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
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86
10X Genomics visium
Major SRT methods.
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+tissue+section+test+slides/pmc09891446-217-8-6?v=10X+Genomics
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86
Spatial Transcriptomics Inc rna capture based approaches
Major SRT methods.
Rna Capture Based Approaches, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Hamamatsu visium gene expression slides
Major SRT methods.
Visium Gene Expression Slides, supplied by Hamamatsu, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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86
10X Genomics visium tissue section test slide
Figure 3. Spatial transcriptomic profiling reveals distinct cell-origin-specific molecular and phenotypic presentation (A) H&E section generated as part of the <t>Visium</t> 10X spatial transcriptomic profiling performed on representative Gramd2:KRASG12D lung that contains multiple LUAD lesions. (B) High magnification views of distinct histologic regions within Gramd2:KRASG12D lung sections. (C) Spatial distribution of integrated clusters (ICs) across all six lung samples within the dataset. Colors indicate distinct ICs. Spatial <t>transcriptomic</t> <t>sequencing</t> was performed on three biological replicates from Sftpc:KRASG12D and Gramd2:KRASG12D mouse lungs. (D) Barplots of average number of array spots per section. Average (n = 3 samples) and error bars (percent standard deviation) are shown.
Visium Tissue Section Test Slide, 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+tissue+section+test+slides/pm37995179-343-15-21?v=10X+Genomics
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86
Spatial Transcriptomics Inc visium slide
a Experimental workflow for spatial multi-omics profiling of rodent lung, consisting of agarose inflation of the lung tissue to facilitate the preservation of tissue integrity, followed by snap-freezing and cryo-sectioning to create consecutive sections, used for <t>spatial</t> <t>transcriptomics</t> and spatial metabolomics, respectively. b MAGPIE computational framework for co-registering same or consecutive section spatial transcriptomics <t>(Visium)</t> and metabolomics (mass spectrometry imaging, MSI) data. The pipeline’s inputs and outputs are in standardised Space Ranger-style and tabular formats to ensure compatibility with other tools. Preprocessing the MSI data produces a data-generated image used for landmark selection and subsequent image co-registration. The output from the pipeline is the MSI data with updated coordinates aligned to the Visium data. c To create a 1:1 mapping between Visium spots and MSI pixels, MAGPIE (by default) expands Visium spot radii, such that there are no gaps between spots, and then aggregates MSI pixels that fall within these spot boundaries. This results in matching observations between modalities and a processed object which can then be read by analysis toolkits, including semla . d Overview of downstream analysis options once the modalities are aligned into a matching coordinate system. Examples include joint clustering, dimensionality reduction, linking transcriptomic and metabolic changes to histologic information, and gene-peak correlation or multi-omics covariation network analysis. Spatial b.c. spatial barcode, CCF common coordinate framework. Source data are provided as a Source Data file.
Visium Slide, supplied by Spatial Transcriptomics Inc, 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+tissue+section+test+slides/pmc12780049-265-9-12?v=Spatial+Transcriptomics+Inc
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86
10X Genomics 10xgenomics visium platform
a Experimental workflow for spatial multi-omics profiling of rodent lung, consisting of agarose inflation of the lung tissue to facilitate the preservation of tissue integrity, followed by snap-freezing and cryo-sectioning to create consecutive sections, used for <t>spatial</t> <t>transcriptomics</t> and spatial metabolomics, respectively. b MAGPIE computational framework for co-registering same or consecutive section spatial transcriptomics <t>(Visium)</t> and metabolomics (mass spectrometry imaging, MSI) data. The pipeline’s inputs and outputs are in standardised Space Ranger-style and tabular formats to ensure compatibility with other tools. Preprocessing the MSI data produces a data-generated image used for landmark selection and subsequent image co-registration. The output from the pipeline is the MSI data with updated coordinates aligned to the Visium data. c To create a 1:1 mapping between Visium spots and MSI pixels, MAGPIE (by default) expands Visium spot radii, such that there are no gaps between spots, and then aggregates MSI pixels that fall within these spot boundaries. This results in matching observations between modalities and a processed object which can then be read by analysis toolkits, including semla . d Overview of downstream analysis options once the modalities are aligned into a matching coordinate system. Examples include joint clustering, dimensionality reduction, linking transcriptomic and metabolic changes to histologic information, and gene-peak correlation or multi-omics covariation network analysis. Spatial b.c. spatial barcode, CCF common coordinate framework. Source data are provided as a Source Data file.
10xgenomics Visium Platform, 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+tissue+section+test+slides/pm34329587-98-6-6?v=10X+Genomics
Average 86 stars, based on 1 article reviews
10xgenomics visium platform - by Bioz Stars, 2026-08
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90
Hamamatsu c9600-12 nanozoomer scanner
a Experimental workflow for spatial multi-omics profiling of rodent lung, consisting of agarose inflation of the lung tissue to facilitate the preservation of tissue integrity, followed by snap-freezing and cryo-sectioning to create consecutive sections, used for <t>spatial</t> <t>transcriptomics</t> and spatial metabolomics, respectively. b MAGPIE computational framework for co-registering same or consecutive section spatial transcriptomics <t>(Visium)</t> and metabolomics (mass spectrometry imaging, MSI) data. The pipeline’s inputs and outputs are in standardised Space Ranger-style and tabular formats to ensure compatibility with other tools. Preprocessing the MSI data produces a data-generated image used for landmark selection and subsequent image co-registration. The output from the pipeline is the MSI data with updated coordinates aligned to the Visium data. c To create a 1:1 mapping between Visium spots and MSI pixels, MAGPIE (by default) expands Visium spot radii, such that there are no gaps between spots, and then aggregates MSI pixels that fall within these spot boundaries. This results in matching observations between modalities and a processed object which can then be read by analysis toolkits, including semla . d Overview of downstream analysis options once the modalities are aligned into a matching coordinate system. Examples include joint clustering, dimensionality reduction, linking transcriptomic and metabolic changes to histologic information, and gene-peak correlation or multi-omics covariation network analysis. Spatial b.c. spatial barcode, CCF common coordinate framework. Source data are provided as a Source Data file.
C9600 12 Nanozoomer Scanner, supplied by Hamamatsu, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/visium+tissue+section+test+slides/pm38626768-264-13-12?v=Hamamatsu
Average 90 stars, based on 1 article reviews
c9600-12 nanozoomer scanner - by Bioz Stars, 2026-08
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Image Search Results


Integration of spatially resolved  transcriptomic  data with other methods

Journal: BMB Reports

Article Title: Recent advances in spatially resolved transcriptomics: challenges and opportunities

doi: 10.5483/BMBRep.2022.55.3.014

Figure Lengend Snippet: Integration of spatially resolved transcriptomic data with other methods

Article Snippet: Robust cell type decomposition (RCTD) is based on statistical model maximum-likelihood estimation to approximate the proportions of spatially localized cellular subtypes in spatially resolved transcriptomic data such as Slide-seq or 10X Genomics Visium datasets ( ).

Techniques: Derivative Assay, In Situ Hybridization, Expressing, Microarray, Marker, Modification, Staining, In Silico, Hybridization, Sequencing, Imaging, Mass Spectrometry

Major SRT methods.

Journal: Blood Science

Article Title: Spatially resolved transcriptomics: advances and applications

doi: 10.1097/BS9.0000000000000141

Figure Lengend Snippet: Major SRT methods.

Article Snippet: Sequencing-based methods such as Slide-seq and 10X Genomics’ Visium have also been used to analyze the global transcriptome in both the mouse and human brain., The application of spatially resolved transcriptomic methods is highly likely to improve our understanding of neurological diseases.

Techniques: In Situ Hybridization, Amplification, Labeling, Imaging, Hybridization, Sequencing, Genome Wide, Tomography, High Throughput Screening Assay, Microscopy, Laser-Scanning Microscopy, Transgenic Assay, Staining, In Vivo, In Situ, Introduce

Schematics of the main sequencing-based methods. (A) In tomo-seq, specimens are cryosectioned, and sections are collected in individual microtubes for spatially resolved transcriptomic analysis. (B) In LCM, an infrared laser melts the thermolabile polymer on a tissue section on a glass slide in the vicinity of the laser pulse, resulting in the removal of polymer-cell composite from the tissue. A UV laser can cut away cells of interest or ablate unwanted tissue, leaving cells of interest intact on the slide. (C) In NICHE-seq, tissue expressing a PA-GFP can be activated by 2-photon irradiation, allowing precise in situ labeling. Activated cells are sorted to perform MARS-seq. (D) In 10X Genomics’ Visium, tissue sections are placed on a barcoded glass slide containing 4 capture areas, each with around 5000 spatial spots. After HE staining and imaging, tissue permeabilization releases the mRNA and it is captured by spatial probes. LCM = laser-capture microdissection, PA-GFP = photoactivatable green fluorescent protein, MARS-seq =massively parallel scRNA-seq, UV=ultraviolet, HE staining = hematoxylin-eosin staining.

Journal: Blood Science

Article Title: Spatially resolved transcriptomics: advances and applications

doi: 10.1097/BS9.0000000000000141

Figure Lengend Snippet: Schematics of the main sequencing-based methods. (A) In tomo-seq, specimens are cryosectioned, and sections are collected in individual microtubes for spatially resolved transcriptomic analysis. (B) In LCM, an infrared laser melts the thermolabile polymer on a tissue section on a glass slide in the vicinity of the laser pulse, resulting in the removal of polymer-cell composite from the tissue. A UV laser can cut away cells of interest or ablate unwanted tissue, leaving cells of interest intact on the slide. (C) In NICHE-seq, tissue expressing a PA-GFP can be activated by 2-photon irradiation, allowing precise in situ labeling. Activated cells are sorted to perform MARS-seq. (D) In 10X Genomics’ Visium, tissue sections are placed on a barcoded glass slide containing 4 capture areas, each with around 5000 spatial spots. After HE staining and imaging, tissue permeabilization releases the mRNA and it is captured by spatial probes. LCM = laser-capture microdissection, PA-GFP = photoactivatable green fluorescent protein, MARS-seq =massively parallel scRNA-seq, UV=ultraviolet, HE staining = hematoxylin-eosin staining.

Article Snippet: Sequencing-based methods such as Slide-seq and 10X Genomics’ Visium have also been used to analyze the global transcriptome in both the mouse and human brain., The application of spatially resolved transcriptomic methods is highly likely to improve our understanding of neurological diseases.

Techniques: Sequencing, Polymer, Expressing, Irradiation, In Situ, Labeling, Staining, Imaging, Laser Capture Microdissection

Figure 3. Spatial transcriptomic profiling reveals distinct cell-origin-specific molecular and phenotypic presentation (A) H&E section generated as part of the Visium 10X spatial transcriptomic profiling performed on representative Gramd2:KRASG12D lung that contains multiple LUAD lesions. (B) High magnification views of distinct histologic regions within Gramd2:KRASG12D lung sections. (C) Spatial distribution of integrated clusters (ICs) across all six lung samples within the dataset. Colors indicate distinct ICs. Spatial transcriptomic sequencing was performed on three biological replicates from Sftpc:KRASG12D and Gramd2:KRASG12D mouse lungs. (D) Barplots of average number of array spots per section. Average (n = 3 samples) and error bars (percent standard deviation) are shown.

Journal: Cell reports

Article Title: Alveolar type I cells can give rise to KRAS-induced lung adenocarcinoma.

doi: 10.1016/j.celrep.2023.113286

Figure Lengend Snippet: Figure 3. Spatial transcriptomic profiling reveals distinct cell-origin-specific molecular and phenotypic presentation (A) H&E section generated as part of the Visium 10X spatial transcriptomic profiling performed on representative Gramd2:KRASG12D lung that contains multiple LUAD lesions. (B) High magnification views of distinct histologic regions within Gramd2:KRASG12D lung sections. (C) Spatial distribution of integrated clusters (ICs) across all six lung samples within the dataset. Colors indicate distinct ICs. Spatial transcriptomic sequencing was performed on three biological replicates from Sftpc:KRASG12D and Gramd2:KRASG12D mouse lungs. (D) Barplots of average number of array spots per section. Average (n = 3 samples) and error bars (percent standard deviation) are shown.

Article Snippet: Regions of Interest (ROIs) then underwent sample preparation including test slide sample sequencing using the Visium Tissue Section Test Slide (PN-2000460, 10X Genomics, Dublin, CA, USA).

Techniques: Generated, Sequencing, Standard Deviation

a Experimental workflow for spatial multi-omics profiling of rodent lung, consisting of agarose inflation of the lung tissue to facilitate the preservation of tissue integrity, followed by snap-freezing and cryo-sectioning to create consecutive sections, used for spatial transcriptomics and spatial metabolomics, respectively. b MAGPIE computational framework for co-registering same or consecutive section spatial transcriptomics (Visium) and metabolomics (mass spectrometry imaging, MSI) data. The pipeline’s inputs and outputs are in standardised Space Ranger-style and tabular formats to ensure compatibility with other tools. Preprocessing the MSI data produces a data-generated image used for landmark selection and subsequent image co-registration. The output from the pipeline is the MSI data with updated coordinates aligned to the Visium data. c To create a 1:1 mapping between Visium spots and MSI pixels, MAGPIE (by default) expands Visium spot radii, such that there are no gaps between spots, and then aggregates MSI pixels that fall within these spot boundaries. This results in matching observations between modalities and a processed object which can then be read by analysis toolkits, including semla . d Overview of downstream analysis options once the modalities are aligned into a matching coordinate system. Examples include joint clustering, dimensionality reduction, linking transcriptomic and metabolic changes to histologic information, and gene-peak correlation or multi-omics covariation network analysis. Spatial b.c. spatial barcode, CCF common coordinate framework. Source data are provided as a Source Data file.

Journal: Nature Communications

Article Title: Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

doi: 10.1038/s41467-025-68003-w

Figure Lengend Snippet: a Experimental workflow for spatial multi-omics profiling of rodent lung, consisting of agarose inflation of the lung tissue to facilitate the preservation of tissue integrity, followed by snap-freezing and cryo-sectioning to create consecutive sections, used for spatial transcriptomics and spatial metabolomics, respectively. b MAGPIE computational framework for co-registering same or consecutive section spatial transcriptomics (Visium) and metabolomics (mass spectrometry imaging, MSI) data. The pipeline’s inputs and outputs are in standardised Space Ranger-style and tabular formats to ensure compatibility with other tools. Preprocessing the MSI data produces a data-generated image used for landmark selection and subsequent image co-registration. The output from the pipeline is the MSI data with updated coordinates aligned to the Visium data. c To create a 1:1 mapping between Visium spots and MSI pixels, MAGPIE (by default) expands Visium spot radii, such that there are no gaps between spots, and then aggregates MSI pixels that fall within these spot boundaries. This results in matching observations between modalities and a processed object which can then be read by analysis toolkits, including semla . d Overview of downstream analysis options once the modalities are aligned into a matching coordinate system. Examples include joint clustering, dimensionality reduction, linking transcriptomic and metabolic changes to histologic information, and gene-peak correlation or multi-omics covariation network analysis. Spatial b.c. spatial barcode, CCF common coordinate framework. Source data are provided as a Source Data file.

Article Snippet: Consecutive sections from each lung were thaw-mounted on a Visium slide for spatial transcriptomics and a Superfrost slide (Fisher Scientific, Loughborough, UK) for desorption electrospray ionisation (DESI) MSI.

Techniques: Biomarker Discovery, Preserving, Mass Spectrometry, Imaging, Generated, Selection