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11,105 results for “single cells”
Improving the efficiency of single cell genome sequencing based on overlapping pooling strategy
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Data from: Single cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to Influenza A infection
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Data from: Three-dimensional single-cell transcriptome imaging of thick tissues
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Single-cell burst size estimates
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Data from: Inherent single-cell heterogeneity of the transcriptional response to hypoxia in cancer cells
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Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice
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Single cell RNA sequencing of human tissue along the stomach-intestinal tract
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Data from: Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer
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ZipSeq : barcoding for real-time mapping of single cell transcriptomes
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Data from: Subsets of tissue CD4 T cells display different susceptibilities to HIV infection and death: Analysis by CyTOF and single cell RNA-seq
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SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections
<p>The submitted dataset, correspond to the RAW (*.czi format) and analysis files of the manuscript "SCRINSHOT, a spatial method for single-cell resolution mapping of cell states in tissue sections", which has been processed for revision (2020-01-31) by PLOS Biology.</p> <p>The files are in *.zip format and the naming follows thenumbering of the manuscript figures, which will be found in bioRxiv.org (<strong>ID#: BIORXIV/2020/938571</strong>). Each *.zip files contains a document with the description of all the provided files.</p>
Dataset related to article "Single-Cell Sequencing of Mouse Heart Immune Infiltrate in Pressure Overload-Driven Heart Failure Reveals Extent of Immune Activation."
<p>BACKGROUND:</p> <p>Inflammation is a key component of cardiac disease, with macrophages and T lymphocytes mediating essential roles in the progression to heart failure. Nonetheless, little insight exists on other immune subsets involved in the cardiotoxic response.</p> <p>METHODS:</p> <p>Here, we used single-cell RNA sequencing to map the cardiac immune composition in the standard murine nonischemic, pressure-overload heart failure model. By focusing our analysis on CD45<sup>+</sup> cells, we obtained a higher resolution identification of the immune cell subsets in the heart, at early and late stages of disease and in controls. We then integrated our findings using multiparameter flow cytometry, immunohistochemistry, and tissue clarification immunofluorescence in mouse and human.</p> <p>RESULTS:</p> <p>We found that most major immune cell subpopulations, including macrophages, B cells, T cells and regulatory T cells, dendritic cells, Natural Killer cells, neutrophils, and mast cells are present in both healthy and diseased hearts. Most cell subsets are found within the myocardium, whereas mast cells are found also in the epicardium. Upon induction of pressure overload, immune activation occurs across the entire range of immune cell types. Activation led to upregulation of key subset-specific molecules, such as oncostatin M in proinflammatory macrophages and PD-1 in regulatory T cells, that may help explain clinical findings such as the refractivity of patients with heart failure to anti-tumor necrosis factor therapy and cardiac toxicity during anti-PD-1 cancer immunotherapy, respectively.</p> <p>CONCLUSIONS:</p> <p>Despite the absence of infectious agents or an autoimmune trigger, induction of disease leads to immune activation that involves far more cell types than previously thought, including neutrophils, B cells, Natural Killer cells, and mast cells. This opens up the field of cardioimmunology to further investigation by using toolkits that have already been developed to study the aforementioned immune subsets. The subset-specific molecules that mediate their activation may thus become useful targets for the diagnostics or therapy of heart failure.</p> <p> </p> <p>This dataset is created in .ets form, we attach a pdf with the information about.</p>
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
<p>This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE.</p> <p>New in version 3:<br> 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets<br> 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data</p>
RAW data: Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines - single cell RNAseq - Fastq format and UMI counts
<p>This data set of the single cell sequencing experiment of the urothelial cell line HBLAK belongs to the publication: "Knockdown of UTX/KDM6A Enriches Precursor Cell Populations in Urothelial Cell Cultures and Cell Lines" Cancers 2020, 12(4), 1023; https://doi.org/10.3390/cancers12041023.</p> <p> </p>
Single-cell data on lac operon induction by lactose in E. coli
<p>Raw data from experiments presented in https://doi.org/10.1101/2020.01.04.894766. </p> <p>Escherichia coli cells are exposed alternatively to glucose and lactose as carbon source, while their growth and lac operon expression are monitored using phase contrast and epifluorescence microscopy.</p> <p>Data from 36 independent experiments are provided: details about conditions are given in Julou_2020_lacInduction_assays.xlsx, and the list of data files in Julou_2020_lacInduction_GL_Preproc_fileList.txt</p>
How to perform PCA on single-cell RNA-Seq data in three simple steps
<p>Video on YouTube: <a href="https://www.youtube.com/watch?v=IOe0X-q7FtE">https://www.youtube.com/watch?v=IOe0X-q7FtE</a></p> <p>My Twitter: <a href="https://twitter.com/flo_compbio">https://twitter.com/flo_compbio</a></p> <p>Savannah Bertrand’s fundraiser: <a href="https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1">https://www.gofundme.com/f/help-a-black-lesbian-academic-get-out?utm_source=twitter&utm_medium=social&utm_campaign=p_cf+share-flow-1</a></p> <p>-------------------<br> References:</p> <p>Batson, Joshua, Loïc Royer, and James Webber. “Molecular Cross-Validation for Single-Cell RNA-Seq.” BioRxiv, September 30, 2019, 786269. <a href="https://doi.org/10.1101/786269">https://doi.org/10.1101/786269</a>.</p> <p>Grün, Dominic, Lennart Kester, and Alexander van Oudenaarden. “Validation of Noise Models for Single-Cell Transcriptomics.” Nature Methods 11, no. 6 (June 2014): 637–40.<a href="https://doi.org/10.1038/nmeth.2930"> https://doi.org/10.1038/nmeth.2930</a>.</p> <p>Hafemeister, Christoph, and Rahul Satija. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20, no. 1 (23 2019): 296. <a href="https://doi.org/10.1186/s13059-019-1874-1">https://doi.org/10.1186/s13059-019-1874-1</a>.</p> <p>Hsu, Lauren L., and Aedin C. Culhane. “Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data.” Frontiers in Oncology 10 (2020). <a href="https://doi.org/10.3389/fonc.2020.00973">https://doi.org/10.3389/fonc.2020.00973</a>.</p> <p>Sun, Shiquan, Jiaqiang Zhu, Ying Ma, and Xiang Zhou. “Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single-Cell RNA-Seq Analysis.” Genome Biology 20, no. 1 (10 2019): 269. <a href="https://doi.org/10.1186/s13059-019-1898-6">https://doi.org/10.1186/s13059-019-1898-6</a>.</p> <p>Townes, F. William, Stephanie C. Hicks, Martin J. Aryee, and Rafael A. Irizarry. “Feature Selection and Dimension Reduction for Single-Cell RNA-Seq Based on a Multinomial Model.” Genome Biology 20, no. 1 (23 2019): 295. <a href="https://doi.org/10.1186/s13059-019-1861-6">https://doi.org/10.1186/s13059-019-1861-6</a>.</p> <p>Tsuyuzaki, Koki, Hiroyuki Sato, Kenta Sato, and Itoshi Nikaido. “Benchmarking Principal Component Analysis for Large-Scale Single-Cell RNA-Sequencing.” Genome Biology 21, no. 1 (20 2020): 9. <a href="https://doi.org/10.1186/s13059-019-1900-3">https://doi.org/10.1186/s13059-019-1900-3</a>.</p> <p>Wagner, Florian, Dalia Barkley, and Itai Yanai. “Accurate Denoising of Single-Cell RNA-Seq Data Using Unbiased Principal Component Analysis.” BioRxiv, June 17, 2019, 655365.<a href="https://doi.org/10.1101/655365"> https://doi.org/10.1101/655365</a>.</p> <p>Wagner, Florian. “Monet: An Open-Source Python Package for Analyzing and Integrating ScRNA-Seq Data Using PCA-Based Latent Spaces.” BioRxiv, 2020. <a href="https://doi.org/10.1101/2020.06.08.140673">https://doi.org/10.1101/2020.06.08.140673</a>.</p>
BecomingLTi - Dataset : RNA-seq from Embryo Periphery and Fetal Liver at stage 13.5 and Single-cell RNA-seq for Embryo Periphery at stage 13.5 and 14.5
<p><strong>Dataset from article</strong> : Distinct waves from the hemogenic endothelium give rise to layered Lymphoid Tissue Inducer cell ontogeny</p> <p><strong>Summary:</strong> During embryogenesis Lymphoid Tissue Inducer (LTi) cells are essential for lymph node organogenesis. These cells are part of the Innate Lymphoid Cell (ILC) family. Although their earliest embryonic hematopoietic origin is unclear, other innate immune cells were shown to be derived from both early hemogenic endothelium in the yolk-sac as well as the aorta-gonad-mesonephros. A proper model to discriminate between these locations was unavailable. In this study, using a new Cxcr4-CreERT2 lineage tracing model, we identify a major contribution from embryonic hemogenic endothelium, but not yolk-sac, towards the LTi progenitors. Conversely, embryonic LTi cells are replaced by hematopoietic stem cell derived cells in adult. We further show that within the fetal liver common lymphoid progenitors differentiate into highly dynamic alpha-lymphoid precursor cells, which at this embryonic stage preferentially mature into LTi precursors and establish their functional LTi cell identity only after reaching the periphery.</p> <p><strong>Data </strong>:</p> <p>1. SPlab_BecomingLTi_Bulk_Stage13.5_2tissues_00_RawData : Bulk RNA-seq data for Mouse Embryo Periphery and Fetal Liver at Stage 13.5. It contains matrix of gene expression counts for each sample in the dataset</p> <p>2. SPlab_BecomingLTi_Stage13.5_Periphery_CellRangerV3_00_RawData : Single-cell RNA-seq data for Mouse Embryo Periphery at stage 13.5. It contains the full output of CellRanger count (v3) analysis.</p> <p>3. SPlab_BecomingLTi_Stage14.5_Periphery_CellRangerV3_00_RawData : Single-cell RNA-seq data for Mouse Embryo Periphery at stage 14.5. It contains the full output of CellRanger count (v3) analysis.</p>
Becoming LTi - Dataset : Single-cell RNA-seq of Embryo Fetal Liver tissue at stage 13.5 days
<p><strong>Dataset from article</strong> : Distinct waves from the hemogenic endothelium give rise to layered Lymphoid Tissue Inducer cell ontogeny</p> <p><strong>Summary:</strong> During embryogenesis Lymphoid Tissue Inducer (LTi) cells are essential for lymph node organogenesis. These cells are part of the Innate Lymphoid Cell (ILC) family. Although their earliest embryonic hematopoietic origin is unclear, other innate immune cells were shown to be derived from both early hemogenic endothelium in the yolk-sac as well as the aorta-gonad-mesonephros. A proper model to discriminate between these locations was unavailable. In this study, using a new Cxcr4-CreERT2 lineage tracing model, we identify a major contribution from embryonic hemogenic endothelium, but not yolk-sac, towards the LTi progenitors. Conversely, embryonic LTi cells are replaced by hematopoietic stem cell derived cells in adult. We further show that within the fetal liver common lymphoid progenitors differentiate into highly dynamic alpha-lymphoid precursor cells, which at this embryonic stage preferentially mature into LTi precursors and establish their functional LTi cell identity only after reaching the periphery.</p> <p><strong>Data </strong>:</p> <p>1. SPlab_BecomingLTi_Stage13.5_FetalLiver_00_RawData : Single-cell RNA-seq data for Mouse Embryo Fetal Liver at stage 13.5. It contains the full output of CellRanger count (v3) analysis.</p>
Single-cell metabolic profiling (scMEP) of human cytotoxic T cells
<p>Datasets for Hartmann FJ et al. (2020) Single-cell metabolic profiling of human cytotoxic T cells. Nature Biotechnology</p> <p>Contains single-cell mass cytometry (CyTOF) datasets for metabolic analysis of human whole blood populations, <em>in vitro</em> T cell activation and analysis of metabolic states in human tissues as well as MIBI-TOF multiplexed images and segmented single-cell data of colorectal carcinoma and healthy colon.</p> <ul> <li>All CyTOF datasets have been manually gated on single, live cells to enable direct import into data analysis software (e.g. R environment)</li> <li>MIBI-TOF images have undergone noise removal as described in Keren et al. (2018) Cell</li> <li>Segmentation masks for MIBI-TOF data contain large non-cellular regions that need to be removed during downstream processing</li> <li>MIBI-TOF derived single-cell data is cell size normalized, arcsinh transformed and percentile normalized and contains manually annotated FlowSOM clustering results</li> </ul>
Single-cell Transcriptomic Analysis Identifies Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons
<p>Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including 3 previously undescribed populations of tendon fibroblasts. This table contains differential gene expression for specific genes identified in distinct populations of cells within tendon tissue. </p>
ScienceDex guides
Understand access before you commit
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.