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95 results for “Visium”
Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC (Spatial Transcriptomic 10X Visium portion)
<p>Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be “immune cold” and hence immunotherapy resistant tumors, and EA TNBC as ‘inflamed’ tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.</p> <p> </p> <p>This dataset contains the 10X Visium Spatial Transcriptomic data of TNBC patients. There are two cohorts.</p> <p> </p> <p><strong>Baylor Scott and White (BSW) cohort</strong>: <strong>10x.visium.tar.gz</strong>, containing 10 patients with TNBC from Baylor Scott and White affiliated Hospital. </p> <p>Each sample is made of Space Ranger processed spot-separated gene expression data (processed to HDF5 AnnData file). There are also H&E images, and spot coordinate files available. </p> <p> </p> <p>For <strong>Georgia validation cohort</strong>, 400 genes used for validation of ESG signatures (associated with BA-Community 1 and WA-Community-1) were obtained and provided by Ritu Aneja's lab. These 400 genes' spot-based expression data across Black and White TNBC patients are provided. See file <strong>georgia.validation.visium.tar.gz</strong>. Expression was normalized by total counts per spot, followed by log-normalization by Giotto.</p> <p> </p> <p>As well in our paper, we integrated a published racial TNBC cohort for deriving some of initial results in the paper. This refers to the Bassiouni et al (Cancer Research) paper in Carpten's group. <strong>GSM_giotto_processed.tar.gz</strong> refers to this dataset, which we deposit here. The data were normalized by Giotto using standard procedure.</p>
Supplementary data for ENACT: End-to-End Analysis and Cell Type Annotation for Visium High Definition (HD) Slides
<p>This project contains the datasets used to evaluate and reproduce the results of ENACT (End-to-End Analysis and Cell Type<br>Annotation for Visium HD Slides). The dataset consists of:</p> <ul> <li>a sample Visium HD sample of human colorectal cancer, courtesy of 10X Genomics (<a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">Visium HD Spatial Gene Expression Library, Human Colorectal Cancer (FFPE) - 10x Genomics</a>). All credit goes to 10X Genomics.</li> <li>configuration files to be used to reproduce the results provided in the ENACT publication, </li> <li>evaluation plots used in the ENACT publication, and</li> <li>results obtained after running ENACT on the human colorectal cancer sample using the four bin-to-cell assignment methods (naive, weighted_by_area, weighted_by_transcript, and weighted_by_cluster) and the three cell annotation methods (Sargent, CellAssign, CellTypist)</li> </ul> <p>Additionally, results from running ENACT on the following three public VisiumHD samples are provided to showcase ENACT’s tissue-agnostic nature:</p> <div> <div> <div> <ul> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-lung-cancer-post-xenium-expt">Human Lung FFPE</a> sample from a subject with Adenocarcinoma (age and gender unspecified),</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-tonsil-fresh-frozen">Human Tonsil Fresh Frozen</a> sample from a 21 year old male subject with Reactive Follicular Hyperplasia,</p> </li> <li> <p><a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-human-breast-cancer-fresh-frozen">Human Breast Fresh Frozen</a> sample from a 58 year old female subject with Ductal Carcinoma in Situ (DCIS).</p> </li> </ul> </div> </div> </div> <p> </p>
Visium Spatially Resolved Transcriptomics of Glioblastoma Samples
<p>This repository contains samples (Visium Spatially resolved Transcriptomics) of the project entitled: <strong>Epigenetic neural glioblastoma integrates into neuron-to-glioma-networks and predicts therapeutic vulnerability</strong></p>
Mouse Brain Visium Demo Dataset for Cellxgene VIP
<p>Visium data generated from 3 control mouse full half brains</p> <p>Animal IDs 13, 14 and 53</p> <p>10X standard mm10 (2020-A) reference and spaceranger 1.1.0 was used<br> </p>
Mouse Brain snRNASeq and Visium Demo Dataset for CelEry
<p>## snRNASeq data generated from control mouse brain 3 brain regions.</p> <p>Animal IDs 7</p> <p>Brain region codes:<br> 7W-1: WhiteMatter<br> 7H-1: Hippo<br> 7G-1: GreyMatter</p> <p>10X standard mm10 (3.0.0) reference was used, on cellranger 5.0.0 with --include-introns on.</p> <p><br> ## Visium data generated from the same animal control mouse full half brains</p> <p>Animal IDs 14 (075B slice)</p> <p>10X standard mm10 (2020-A) reference and spaceranger 1.1.0 was used<br> </p>
Visium HD Human Colorectal Cancer (FFPE) data release pathologist annotation
<p>10X Genomics released a spatial <a href="https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc">transcriptomic dataset of human colorectal cancer collected on the Visium HD platform. </a></p> <p>The dataset was divided into different spatial domains based on the accompanying HE stain and the expression of characteristic marker genes.</p> <p>The pathologist's annotation was added with the help of Napari and the Spatialdata python package.</p> <p>Every .csv file contains the Visium HD bin barcode and annotation for the respective level of binning.</p> <p>In addition the HE image was segmented and bins were assigned to Nuclei for a pseudo single cell resolution, as described <a href="https://www.10xgenomics.com/analysis-guides/segmentation-visium-hd">here.</a></p> <p>This work was carried out for the <a href="https://github.com/SpatialHackathon/SpaceHack2023">SpaceHack2023 project </a>and the data shared here is licensed CC0. </p> <p> </p>
Day2_Session2 - mini Visium HD
<p>Visium HD colorectal cancer dataset that was further subsetted based on the tissue positions parquest file. The first 1000 spatial array rows and columns were selected and the associated expression data was subsetted. In addition we selected 5,000 genes which were enriched for highly variable genes.</p>
Example dataset for HoloNet: preprocessed public Visium data
<p>The preprocessed public spatial datasets, with cell-type annotation, can be used as the input of our 'HoloNet' Python package.</p>
FredHutch/Galeano-Nino-Bullman-Intratumoral-Microbiota_2022: 10X Visium Scans
<p>10X Visium scans associated with manuscript submission</p>
Visium datasets of mouse liver in different time points after APAP injection
<p>The datasets were used to analyse the spatial trascriptomics for the manuscript "The spatiotemporal program of zonal liver regeneration following acute injury" (DOI https://doi.org/10.1016/j.stem.2022.04.008). Mice were injected a single dose of 300 mg/kg APAP, and livers were harvested 24, 48 and 72 hours after injection. livers were sectioned for Visium spatial transcirpomics (N = 2 mice per time point). see article's methods section for more detail. </p> <p>other datasets used in the study are deposited in https://zenodo.org/records/6035873</p> <h1></h1>
10X Genomics Human Visium Spatial Transcriptomics Demo Dataset for Cellxgene VIP
<p>4 Visium Spatial Transcriptomics datasets downloaded 10X Genomics data site ,and organized in the way to be used for Cellxgene VIP input.</p> <p>10X_demo_data_Breast_Cancer_Block_A_Section_1<br> 10X_demo_data_Breast_Cancer_Block_A_Section_2<br> 10X_demo_data_Human_Heart<br> 10X_demo_data_Human_Lymph_Node<br> </p>
Spotlight on 10x Visium: a multi-sample protocol comparison of spatial technologies
Open the record for dataset details and reuse information.
Spatial localization with Spatial Transcriptomics for an atlas of healthy and injured cell states and niches in the human kidney [Visium ST]
GEO Series GSE183456. Homo sapiens. 23 samples. Type: Expression profiling by high throughput sequencing.
Interleukin 7 therapy changes tumor immune environment to pro-inflammatory through myeloid and T cell interactions [Visium]
GEO Series GSE205306. Mus. 2 samples. Type: Expression profiling by high throughput sequencing.
Visium Spatial Transcriptomics expression profiles from IPMN samples of the pancreas
GEO Series GSE233293. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
Precision targeting of beta-catenin induces tumor reprogramming and immunity in hepatocellular cancers [Spatial Transcriptomics by 10X Visium CTNNB1/NFE2L2]
GEO Series GSE290445. Mus musculus. 5 samples. Type: Other.
Pharmacological targeting Netrin-1 inhibits EMT in cancer [Visium]
GEO Series GSE234266. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing; Other.
Visium spatial gene expression analyses of ovarian clear cell carcinoma (OCCC) [visium OCCC]
GEO Series GSE224335. Homo sapiens. 2 samples. Type: Other.
A pairwise cytokine code explains the organism-wide response to sepsis [spatial transcriptomics by Visium]
GEO Series GSE224145. Mus musculus. 4 samples. Type: Other.
Visium Analysis of head and neck squamous carcinoma (HNSCC) spatial transcriptome evolution
GEO Series GSE181300. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
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