Visium

Manufactured by 10x Genomics

Visium is a spatial gene expression analysis solution developed by 10x Genomics. It enables the capture and analysis of spatial transcriptomic information from tissue samples. The Visium platform provides a comprehensive workflow for preserving the spatial context of gene expression data within a tissue sample.

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Lab products found in correlation

Scn400f slide scanner, by Leica camera (1 mentions) Novaseq600, by Illumina (1 mentions) Visium spatial gene expression reagent kit, by 10x Genomics (1 mentions) Novaseq, by Illumina (1 mentions) Nanozoomer scanner, by Hamamatsu Photonics (1 mentions)

5 protocols using visium

Spatial Transcriptomics of Cryopreserved Tissues

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Tissues were embedded in optimal cutting temperature medium and frozen in liquid nitrogen–chilled isopentane within 15 min of harvesting. Ten-micrometer cryosections were mounted onto the ST arrays (10X Genomics Visium) and stored at −80°C until use. Tissue sections were fixed in methanol at −20°C and then stained with hematoxylin and eosin. Bright-field images were taken on a Leica SCN400F slide scanner at 20× resolution. Slides were permeabilized with permeabilization enzyme for 5 min, as determined by the tissue optimization protocol. Polyadenylated RNAs captured on the underlying arrays were resuspended in 1.2 ml of 0.1 N HCl for 5 min and reverse transcribed at 53°C for 45 min, followed by second-strand synthesis at 65°C for 5 min. After library preparation, samples were sequenced on a Novaseq 600 (Illumina)

Konieczny P., Xing Y., Sidhu I., Subudhi I., Mansfield K.P., Hsieh B., Biancur D.E., Larsen S.B., Cammer M., Li D., Landén N.X., Loomis C., Heguy A., Tikhonova A.N., Tsirigos A, & Naik S. (2022). Interleukin-17 governs hypoxic adaptation of injured epithelium. Science (New York, N.Y.), 377(6602), eabg9302.

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Spatial Transcriptomics of Tumor Samples

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To prepare the ST slides, the samples preserved in OCT were sectioned, stained with hematoxylin and eosin, and examined by an experienced pathologist (R.A.). The areas for ST analysis were chosen based on tumor viability, presence of stroma with immune infiltration and cancer associated fibroblasts when possible. For each sample a 6 x 6mm region with those characteristics were selected for sectioning and mounting onto the ST slides.
The ST data was generated using the commercial platform Visium (10x Genomics). Briefly, from each surgical sample a 5μm section was placed in the designated area at the Visium slide and immediately stored at −80C until use. The sections were fixed in cold methanol for 30 minutes at −20C. The fixed samples were stained with hematoxylin and eosin (H&E) and imaged using the Nanozoomer scanner (Hamamatsu) at 40x magnification. Samples were permeabilized for 30 minutes at 37°C with the Permeabilization Enzyme provided with the Visium Spatial Gene Expression Reagent Kits (10x Genomics). Following permeabilization, reverse transcription; cDNA second strand synthesis, denaturation, and amplification; and library construction were performed according to manufacturer’s instructions. All libraries were sequenced with a depth of at least 50,000 reads per spot (minimum of ~250 millions per sample) at the NovaSeq (Illumina).

Zhang S., Yuan L., Danilova L., Mo G., Zhu Q., Deshpande A., Bell A.T., Elisseeff J., Popel A.S., Anders R.A., Jaffee E.M., Yarchoan M., Fertig E.J, & Kagohara L.T. (2023). Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence. bioRxiv.

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Spatial Transcriptomic Analysis of Breast Cancer

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The data and sample information were obtained from 10x Genomics [38 ]. The data consists of spatial transcriptomic measurements of two sections of a sample analyzed with 10x Genomics Visium. The sections come from tissue from a patient with grade 2 ER⁺, PR⁻, HER2⁺, annotated with ductal carcinoma in situ, lobular carcinoma in situ, and invasive carcinoma. The mean sequencing depths were reported to be 149,800 and 137,262 reads per spot for a total of 3813 and 4015 spots per section respectively. The median UMI counts per spot were reported as 17,531 and 16,501, and the median genes per spot as 5394 and 5100 respectively. The raw data was preprocessed and count matrices were generated with spaceranger-1.0.0. Individual count matrices were normalized with sctransform implemented in Seurat 3.1.2 [46 (link)]. For each spot, we estimated signaling pathway activities with PROGENy’s model matrix using the top 1000 genes of each transcriptional footprint. We retrieved from Omnipath [42 (link)] all proteins labeled as ligands and in each dataset, we filtered all ligands whose expression was captured in at least 5% of the spots.

Tanevski J., Flores R.O., Gabor A., Schapiro D, & Saez-Rodriguez J. (2022). Explainable multiview framework for dissecting spatial relationships from highly multiplexed data. Genome Biology, 23, 97.

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Integrative Splenocyte Deconvolution for Spatial Transcriptomics

Cited 4 times

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An integrated scRNA-seq dataset of splenocytes from multiple datasets was used as a reference dataset for SPOTlight deconvolution analysis of spatial transcriptomic data obtained using Visium by 10x Genomics. The following datasets were integrated and used as the reference:

GP33⁺ CD8 T cells, day 30 post-LCMV Cl13 infection^{3 (link)} (GSE129139)

GP33⁺ CD8 T cells, day 33 post-LCMV Cl13 infection^{59 (link)} (GSE201195)

CD44⁺ CD4 T cells, day 21 post-LCMV Cl13 infection (Control 1 and CD4 Depleted 1 from this paper: GSE200721)

Healthy splenocytes from Tabla Muris dataset^{60 (link)} (GSE109774)

B220⁻CD3⁻NK1.1⁻CD11b⁺ myeloid cells day 7 post-LCMV Cl13 chronic infection^{61 (link)} (GSE167204)

Topchyan P., Zander R., Kasmani M.Y., Nguyen C., Brown A., Lin S., Burns R, & Cui W. (2022). Spatial transcriptomics demonstrates the role of CD4 T cells in effector CD8 T cell differentiation during chronic viral infection. Cell reports, 41(9), 111736.

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Spatial Transcriptome Analysis using SpatialMix

Cited 1 time

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Lastly, we applied SpiceMix to a dataset acquired from the 10x Genomics Visium platform that profiled spatial transcriptome of the human DLPFC [31 (link)]. For analysis with SpiceMix, we removed genes which had non-zero expression in less than 10% of spots, which yielded an unbiased set of 3,194 genes. We did not apply this filtering when using SpaGCN or BayesSpace. We then normalized the expression of these genes by scaling the total counts to 10,000 per spot, adding one, and applying the log transform:

E_{i g}^{'} : = \log (1 + (10^{4} \frac{E_{i g}}{\sum_{g^{'}} E_{i g^{'}}}))

. To generate a graphical representation of the spots, we defined the neighborhood of a spot to be the set of directly adjacent spots in the hexagonal grid, since the spots in each FOV form a hexagonal grid. Therefore, except for spots on the edge of the grid, each spot has exactly 6 neighbors.
For further details of the methodology of our analysis, see Supplementary Note. This includes details on the selection of the four FOVs from the Br8100 sample to use for the ARI score comparison, the ARI score comparison between SpiceMix, SpaGCN, and BayesSpace on these FOVs, the subsequent analysis of SpiceMix metagenes on these FOVs, and the analysis of SpiceMix metagenes on sample Br5292.

Chidester B., Zhou T., Alam S, & Ma J. (2023). SPICEMIX enables integrative single-cell spatial modeling of cell identity. Nature genetics, 55(1), 78-88.

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