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6,040 results for “Single-cell”
Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data
<p>Processed Datasets for:</p> <p>EGA Study ID: EGAS00001006141</p> <p>EGA Dataset ID: EGAD00001008662</p> <p> </p> <p>Find processed files and arrow files</p> <p> </p> <p>Abstract:</p> <p>Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8<sup>+</sup> TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.</p>
Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"
<p>This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)</p>
Source data for paper "Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data"
<p>Sample-paired scRNA-seq and scATAC-seq data collected from human peripheral blood mononuclear cells with <em>Staphylococcus aureus</em><em> </em>infection. ScATAC-seq data collected from human peripheral blood mononuclear cells with <em>COVID-19</em> infection. </p>
Gene expression dataset of the Spatially Resolved Single-cell Translatomics at Molecular Resolution
<p>Here are the gene expression datasets of RIBOmap included in "<strong>Spatially Resolved Single-cell Translatomics at Molecular Resolution</strong>" from Zeng et al. Please refer to the README file for more detailed information. </p> <p> </p> <p><strong>Abstract</strong></p> <p>The precise control of mRNA translation is a crucial step in post-transcriptional gene regulation of cellular physiology. However, it remains a major challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of RIBOmap, a three-dimensional (3D) in situ profiling method to detect mRNA translation of thousands of genes simultaneously in intact cells and tissues. By applying RIBOmap to 981 genes in HeLa cells, we revealed a remarkable dependency of translation on cell-cycle stages and subcellular localization. Furthermore, we profiled single-cell translatomes of 5,413 genes in adult mouse brain tissues yielding a spatial cell atlas of 119,173 cells. The pairwise spatial mapping of single-cell translatome and transcriptome in two adjacent mouse brain slices revealed cell-type and brain-region-dependent translational regulation and suggested a translation remodeling during oligodendrocyte lineage maturation. The spatial translatome profiling detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks. Together, RIBOmap presents the first spatially resolved single-cell translatomics technology, accelerating our understanding of protein synthesis in the context of subcellular architecture, cell types, and tissue anatomy.</p>
Supporting data for "Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche"
<p>This data repository contains several datasets supplementing the paper “Dissecting the cellular architecture of neuroblastoma bone marrow metastasis using single-cell transcriptomics and epigenomics unravels the role of monocytes at the metastatic niche” by Fetahu, Esser-Skala, Dnyansagar et al. (2023).</p> <ul> <li>HOMER_Results.zip: detailed results of the HOMER analysis</li> <li>nblast_scopen_gene_activity_normalized_motifs_added.rds: Seurat object with scATAC-seq data</li> <li>snp_array.tgz: SNP array data</li> <li>R_data_generated.tgz: Files generated by the scRNA-seq analysis scripts in the GitHub repository associated with the publication.</li> </ul>
Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
<p>In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium <em>vs.</em> microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.</p> <p>Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.</p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS
<p>Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS".</p> <p>It includes the output of SCINPAS, available here: <a href="https://github.com/zavolanlab/SCINPAS">https://github.com/zavolanlab/SCINPAS</a>, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.</p>
Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
<p>Despite their importance in tissue maintenance and repair, fibroblast diversity and plasticity remain poorly understood. Using single-cell RNA sequencing, we uncover distinct sclerotome-derived fibroblast populations in zebrafish, including progenitor-like perivascular/interstitial fibroblasts, and specialized fibroblasts such as tenocytes. To determine fibroblast plasticity <em>in vivo</em>, we develop a laser-induced tendon ablation and regeneration model. Lineage tracing reveals that laser-ablated tenocytes are quickly regenerated by preexisting fibroblasts. By combining single-cell clonal analysis and live imaging, we demonstrate that perivascular/interstitial fibroblasts actively migrate to the injury site, where they proliferate and give rise to new tenocytes. By contrast, perivascular fibroblast-derived pericytes or specialized fibroblasts, including tenocytes, exhibit no regenerative plasticity. Interestingly, active Hedgehog (Hh) signaling is required for the proliferation of activated fibroblasts to ensure efficient tenocyte regeneration. Together, our work highlights the functional diversity of fibroblasts and establishes perivascular/interstitial fibroblasts as tenocyte progenitors that promote tendon regeneration in a Hh signaling-dependent manner.</p>
scProAtlas: an atlas of multiplexed single-cell spatial proteomics imaging in human tissues
<p>All analysis results for the spatial proteomics imaging techniques in the scProAtlas database are stored in compressed files named accordingly. Within each compressed file, the folders are organized in a fixed storage structure in the following order: Analysis module > Imaging Technique > Dataset > Tissue > ROI.</p> <p>Each folder contains the corresponding metadata (including original sample information, cell type annotations, and neighborhood annotations) stored in a file named <code>cells.tsv</code>. Additionally, the module used to identify spatial pattern genes includes an <code>anndata</code> format file, named <code>adata_moran.h5ad</code>, which stores the integrated results of scRNA-seq and spatial proteomics.</p> <p>scProAtlas_analysis_code.tar.gz contains example codes for all analysis modules in scProAtlas. Here, we provide the example using <strong>SCP_CODEX1 - Large intestine. </strong>The codes include all the scripts used for the entire workflow, from image segmentation to scRNA-spatial proteomics integration, and spatial analysis.</p> <p>We have also uploaded the raw protein channel matrices with AnnData format in <strong>version 3 and 4.</strong></p>
Vizgen MERFISH files for Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs
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Single-cell analysis reveals M. tuberculosis ESX-1-mediated accumulation of anti-inflammatory macrophages in infected mouse lungs
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Data for: Tools and methods for high-throughput single-cell imaging with the mother machine
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Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
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Supplementary data from: Inherent single-cell heterogeneity of the transcriptional response to hypoxia in cancer cells
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Modeling single-cell heterogeneity in signaling dynamics of macrophages reveals principles of information transmission
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Single-cell RNA sequencing of human salivary gland derived mesenchymal stromal cells under cytokine treatment conditions
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Single-cell profiling reveals immune-based mechanisms underlying tumor radiosensitization by a novel Mn porphyrin clinical candidate, MnTnBuOE-2-PyP5+ (BMX-001)
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Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
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Data from: Single-cell peripheral immunoprofiling of Lewy body and Parkinson’s disease in a multi-site cohort
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.