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975 results for “Spatial transcriptomics”
Spatial Transcriptomics in Breast Cancer Reveals Tumour Microenvironment-Driven Drug Responses and Clonal Therapeutic Heterogeneity
<p>We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell [6] (<a href="https://github.com/cnio-bu/beyondcell" target="_blank" rel="noopener">cnio-bu/beyondcell</a>), a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions.</p> <p>Here, you can find:</p> <p>In signatures folder:</p> <ul> <li><strong>SSc breast:</strong> Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.</li> <li><strong>Functional signatures:</strong> Collection of gene signatures used to compute enrichment in different biological pathways.</li> </ul> <p>In visium folder:</p> <ul> <li><strong>Visium objects:</strong> Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.</li> </ul> <p>In single-cell folder:</p> <ul> <li><strong>Single-cell objects:</strong> Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.</li> </ul> <p>In beyondcell folder:</p> <ul> <li><strong>Beyondcell </strong><strong>sensitivity </strong><strong>objects</strong> with prediction scores for all drug response signatures in SSc breast.</li> <li><strong>Beyondcell functional objects </strong>with enrichment scores for all functional signatures.</li> </ul>
Spatially resolved transcriptomics of benign and malignant peripheral nerve sheath tumors
<p><em><span>Background: </span></em></p> <p><span>Peripheral nerve sheath tumors (PNSTs) encompass entities with different cellular differentiation and degrees of malignancy. Spatial heterogeneity complicates diagnosis and grading of PNSTs in some cases. In malignant PNST (MPNST) for example, single cell sequencing data has shown dissimilar differentiation states of tumor cells. Here, we aimed at determining the spatial and biological heterogeneity of PNSTs.</span></p> <p><em><span>Methods: </span></em></p> <p><span>We performed spatial transcriptomics on formalin-fixed paraffin-embedded diseased peripheral nerve tissue. We used spatial clustering and weighted correlation network analysis to construct niche-similarity networks and gene expression modules. We determined differential expression in primary pathologies, analysed pathways to investigate the biological significance of identified meta-signatures, integrated the transcriptional data with histological features and existing single cell data, and validated expression data by immunohistochemistry. </span></p> <p><em><span>Results: </span></em></p> <p><span>We identified distinct transcriptional signatures differentiating PNSTs. We observed spatial transcriptional heterogeneity within hybrid PNSTs (HPNSTs) and immune cells preferentially infiltrating the neurofibroma component. S100b and Vimentin were validated as markers for schwannomas and schwannoma components of HPNSTs, while APOD highlights neurofibroma components of HPNSTs. Furthermore, we mapped cells with different differentiation states, including Schwann cell precursors, neural crest-like cells and those with mesenchymal transition in MPNST in space. </span></p> <p><em><span>Conclusions:</span></em></p> <p><span>This pilot study shows that spatial transcriptomics can be applied to PNSTs to gain insight into their biology. It helps establishing new markers, provides spatial information about cellular composition and distribution of cellular differentiation states. Hence, it is a powerful tool for integrating morphological and high-dimensional molecular data with the potential to facilitate PNSTs classification in the future. </span></p>
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
Processed data for "SpotClean adjusts for spot swapping in spatial transcriptomics data"
<p>This repo contains processed data to reproduce results in the paper "SpotClean adjusts for spot swapping in spatial transcriptomics data".</p>
Pre-fitted Bayesian models for "Gene panel selection for targeted spatial transcriptomics"
<p>simulation_parameters_DARTFISH_slim.rds: Bayesian model fitted on the Zhang dataset.</p> <p>simulation_parameters_MERFISH_slim.rds: Bayesian model fitted on the Moffit dataset.</p> <p>simulation_parameters_osmFISH_slim.rds: Bayesian model fitted on the Codeluppi dataset.</p> <p> </p>
Spatial domains identification in spatial transcriptomics by domain knowledge-aware and subspace-enhanced graph contrastive learning
<p>We propose a graph contrastive learning framework, GRAS4T, which combines contrastive learning and subspace module to accurately distinguish different spatial domains by capturing tissue microenvironment through self-expressiveness of spots within the same domain. To uncover the pertinent features for spatial domain identification, GRAS4T employs a graph augmentation based on histological images prior, preserving information crucial for the clustering task. Experimental results on 8 ST datasets from 5 different platforms show that GRAS4T outperforms five state-of-the-art competing methods in spatial domain identification. Significantly, GRAS4T excels at separating distinct tissue structures and unveiling more detailed spatial domains. GRAS4T combines the advantages of subspace analysis and graph representation learning with extensibility, making it an ideal framework for ST domain identification.</p>
Technology comparison (image-based spatial transcriptomics)- annotated datasets
<p>This repository contains all the AnnData datasets, regionally annotated, used in the comparison of image-based spatial transcriptomics technologies (Marco Salas et al. 2024)</p>
Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering
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Pancreatic Ductal Adenocarcinoma Spatial Transcriptomics
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Masked Conditional Diffusion Model with GNN for Spatial Transcriptomics Data Imputation
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Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML
<p>Images utilized in the paper <em>Single Cell Spatial Transcriptomics Reveals Immunotherapy-Driven Bone Marrow Niche Remodeling in AML </em>- by Gui, Bingham et al.<em><br></em></p>
Example dataset and expected outcomes of Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues
<p>Here are the example datasets and expected outcomes included in "<strong>Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues</strong>" from Ren et al. Please refer to the README.txt file for more detailed information. Corresponding computational tools are available at <a href="https://github.com/wanglab-broad/starfinder">https://github.com/wanglab-broad/starfinder.</a></p>
Experiment results of the article of "Enhancing spatial domain detection in spatial transcriptomics with EnSDD"
<p>The experiment results for the reproducibility of the article of "Enhancing spatial domain detection in spatial transcriptomics with EnSDD"</p>
Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing
<p>We leveraged single-cell RNA sequencing to understand the early molecular events that define the hepatocyte response to acetaminophen exposure at a subpopulation level. We spatially assigned hepatocytes along the portol-central vein axis by using established landmark genes. By spatially assigning the hepatocytes were were able to account for innate differences in gene expression that existed along this gradient. The excel files herein provide the full list of differentially expressed genes between key subpopulations of interest. Additionally, we classified genes as either pericentral zonated, periportal zonated, or non-zonated, the full list of genes and their spatial assignments are included in the appropriate excel file. </p>
Spatially resolved transcriptomics reveals innervation-responsive functional clusters in skeletal muscle
<p>Spatial Transcriptomics Data of murine skeletal muscle undergoing reversible nerve injury. Accompanying the manuscript, D'Ercole et al. <strong>"Spatially resolved transcriptomics reveals innervation-responsive functional clusters in skeletal muscle".</strong></p> <p> </p> <p><strong>Release v1: </strong>This release Includes all the code used to generate the figures and the processed and integrated original dataset in rds format.</p> <p> </p>
Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation
<p>Raw microscopy images underlying the publication "Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation"</p>
Single-cell and spatial transcriptomics of cardiac neural crest reveal a dual role of vinculin in Tgf beta signaling and cell-extracellular matrix interaction during cardiac outflow tract development
<p>single-cell RNA-seq data analysis:</p> <p>all.cncc.combined.EMBO.mapped.Rdata: public CNCC single-cell RNA-seq data integration</p> <p>E13.5_CNCC_merged_updated.Rdata: single-cell RNA-seq data of E13.5 CNCC generated in Elly lab</p> <p>all.seurat.GFP.Rdata: scRNA-seq data with GFP detected</p> <p>visium.merge_AB.control.Rdata: R processed ST data for slice A and B</p> <p>visium.merge_CD.mutant.Rdata: R processed ST data for slice A and B</p> <p> </p>
Immune Signature of Chronic Hand Eczema Unveiled by Spatial Transcriptomics and Single-Cell Proteomics
ClinicalTrials.gov study NCT06884163. IPD Sharing: NO. Countries: 1. Publications: 6.
Spatially Transcriptomics Reveals Molecular Signatures in CRPS
ClinicalTrials.gov study NCT05986461. IPD Sharing: NO. Countries: 1. Publications: 7.
Spatial reconstruction of the early hepatic transcriptomic landscape after an acetaminophen overdose using single-cell RNA sequencing
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.