Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
5,472
datasets available to search
ShareScore release 0.9.0
Dataset results
5,472 results for “mouse cells”
Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency
Open the record for dataset details and reuse information.
Data for: Mouse sperm DNA-methylation changes induced by age and mechanistic target of rapamycin (mTOR) manipulation in Sertoli cells
Open the record for dataset details and reuse information.
4D light sheet imaging, computational reconstruction, and cell tracking in mouse embryos -- example data (raw .czi and fused .klb light sheet images of mouse E7.5)
Open the record for dataset details and reuse information.
An amphipol-stabilized multi-pass transmembrane protein as an immunogen to generate mouse memory B cells against native VMAT2
Open the record for dataset details and reuse information.
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
Open the record for dataset details and reuse information.
Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells
Open the record for dataset details and reuse information.
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>
Optogenetic response of mouse and macaque retinal ganglion cells
<p>Data presented in "Towards optogenetic vision restoration with high resolution"<br> BioRxiv https://www.biorxiv.org/content/10.1101/470773v1, to appear in PLOS Computational Biology.<br> Please cite this paper if the data have been useful for you.</p> <p>Data contains multi-electrode array recordings of optogenetic response of retinal ganglion cells subject to checkerboard visual stimulation.<br> Mouse: blind rd1 mice (4-5 weeks old) with an AAV2 encoding ReaChR-mCitrine under a pan-neuronal hSyn promoter via intravitreal injections. <br> Macaque: retinal ganglion cells have been targeted with an AAV2 encoding a human codon optimized CatCh under a strong, RGC-specific promoter. Retinas were harvested three months after injection of the virus in the adult macaque retina.</p>
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>
Axial currents recordings in mouse retinal ganglion cells
<p>Patch-clamp recordings in mouse retinal ganglion cells performed by Martijn Sierksma. It comprises axial currents recorded in voltage clamp, spontaneous activity and the response to current pulses. The cells were filled with biocytin and subsequently labelled for ankyrin G. The dataset comprises confocal images of the full neurons morphology and their axon initial segments performed by Sarah Goethals. </p> <p> </p>
Data from: Nanotransfection-based vasculogenic cell reprogramming drives functional recovery in a mouse model of ischemic stroke
<p>Ischemic stroke causes vascular and neuronal tissue deficiencies that could lead to significant functional impairment and/or death. Although progenitor-based vasculogenic cell therapies have shown promise as a potential rescue strategy following ischemic stroke, current approaches face major hurdles. Here we used fibroblasts nanotransfected with <em>Etv2</em>, <em>Foxc2</em>, and <em>Fli1</em> (<em>EFF</em>), to drive reprogramming-based vasculogenesis, intracranially, as a potential therapy for ischemic stroke. Perfusion analyses suggest that intracranial delivery of <em>EFF</em>-nanotransfected fibroblasts led to a dose-dependent increase in perfusion 14 days post-injection. MRI and behavioral tests revealed ~70% infarct resolution and up to ~90% motor recovery for mice treated with <em>EFF</em>-nanotransfected fibroblasts. Immunohistological analysis confirmed increases in vascularity and neuronal cellularity, as well as reduced glial scar formation in response to treatment with <em>EFF</em>-nanotransfected fibroblasts. Altogether, our results suggest that vasculogenic cell therapies based on nanotransfection-driven (i.e., non-viral) cellular reprogramming represent a promising strategy for the treatment of ischemic stroke.</p>
Data from: Early mechanistic events induced by low molecular weight polycyclic aromatic hydrocarbons in mouse lung epithelial cells: a role for eicosanoid signaling
Low molecular weight polycyclic aromatic hydrocarbons (LMW PAHs; <206.3 g/mol) are under regulated environmental contaminants (e.g., secondhand smoke) that lead to gap junction dysregulation, p38 MAPK activation, and increased mRNA production of inflammatory mediators, such as cytokines and cyclooxygenase (COX2), in lung epithelial cells. However, the early mechanisms involving lipid signaling through the arachidonic acid pathway and subsequent eicosanoid production leading to these downstream events are not known. Common human exposures are to mixtures of LMW PAH's, thus C10 cells (a mouse lung epithelial cell line) were exposed to a representative binary PAH mixture, 1-methylanthracene (1-MeA) and fluoranthene (Flthn), for 30 min – 24 h with and without p38 and cytosolic phospholipase A2 (cPLA2) inhibitors. cPLA2 inhibition reversed PAH-induced phospho-p38 MAPK activation and gap junction dysregulation at 30 min. A significant biphasic increase of cPLA2 protein was observed at 30 min, 2, & 4 h, as well as COX2 protein at 2 & 8 h. Untargeted metabolomics demonstrated a similar trend with significantly changing metabolites at 30 min & 4 h of exposure relative to 1 h; a "cPLA2-like" subset of metabolites within the biphasic response were predominately phospholipids. argeted metabolomics showed several eicosanoids (e.g., prostaglandin D2 (PGD2), PGE2α) were significantly increased at 4, 8, and 12 h following exposure to the binary PAH mixture and this effect was p38-dependent. Lastly, PAH metabolism was not observed until after 8 h. These results indicate an early lipid signaling mechanism of PAH toxicity in lung epithelial cells due to parent PAH compounds.
C57BL/6J mouse hippocampus CA1 pyramidal cell morphologies
<p>title : Reconstruction of hippocampus CA1 cell morphologies</p> <p>specimen : Mus musculus</p> <p>sex : male</p> <p>strain : C57BL/6J</p> <p>age : post-natal day 13-16</p> <p>This neuron was recorded and filled with biocytin (3 mg/ml) in a 300 µm thick coronal slice of rat hippocampus, using 2 - 10 MOhm patch pipettes. 3,3′-diaminobenzidine (DAB) was used for revelation. The slice was fixed and the cell reconstructed with Neurolucida using a 100x oil immersion objective.</p>
Imputed multiplexed DNA FISH data from mouse cells
<p>Imputed multiplexed DNA FISH data from the following studies:</p><ol><li>Huang et al 2021 (DOI: 10.1038/s41588-021-00863-6): 5kb resolution chromatin tracing data of mouse embryonic stem cells</li><li>Takei et al 2021 (DOI: 10.1038/s41586-020-03126-2): 5kb and 1Mb resolution DNA seqFISH+ data of mouse embryonic stem cells</li><li>Takei et al 2021 (DOI: 10.1126/science.abj1966): 1Mb resolution DNA seqFISH+ data of mouse brain cells (excitatory neurons not included)</li></ol>
Integrated single-cell RNA-sequencing data of unwounded and wounded mouse skin and fibroblasts.
<p>This repository contains the .h5ad files that store the integrated scRNA-seq data we generated for the work, Almet et al. (2023), "Fibroblasts evolve in single-cell state to drive extracellular matrix and signaling changes across wound healing", to be published in the Journal of Investigative Dermatology.</p><p>The integrated* files contain both raw counts, normalized counts, as well as unspliced and spliced count estimates that were obtained using kallisto|bustools and velocyto. We integrated the data from the following published datasets:</p><ol><li><a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>: Unwounded P21 mice and small wound P21 + 7 mice</li><li><a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020)</a>: Unwounded P49 mice and small wound P49 + 4 mice</li><li><a href="https://doi.org/10.1038/s41467-018-08247-x)">Guerrero-Juarez et al. (2019)</a>: Large wound day 12 mice</li><li><a href="https://doi.org/10.1016/j.stem.2020.07.008">Abbasi et al. (2020)</a>: Large wound day 14 mice</li><li><a href="https://doi.org/10.1126/sciadv.aay3704">Gay et al. (2020)</a>: Large wound fibrotic (hairless) and regenerative (hair follicle neogenesis) day 18 mice</li></ol><p>The unwounded_* files were used to briefly integrated unwounded skin scRNA-seq from mouse models of different ages that have been used to analyze wound healing in <a href="https://doi.org/10.1016/j.celrep.2020.02.091">Haensel et al. (2020),</a> <a href=" https://doi.org/10.7554/eLife.60066">Phan et al. (2021)</a>, and <a href="https://doi.org/10.1016/j.celrep.2022.111155">Vu et al. (2022)</a>, which generated scRNA-seq for unwounded skin from mice aged P21, P49, and P616, respectively. </p><p>The data can be loaded using the Python package Scanpy or AnnData, but you can also load it in R if you use zellkonverter. </p>
Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution
<p>Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration" </p><p><strong>Structure</strong></p><p><strong>ST_berghei_liver</strong></p><p>contains data generated during <i>stpipeline </i>analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h. </p><ul><li><i><strong>count</strong></i> contains gene expression matrix output from stpipeline in .tsv format</li><li><i><strong>spotfiles</strong> </i>contains coordinate files for count matrices</li><li><i><strong>images </strong></i>contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.</li><li><i><strong>masks </strong></i>contains image masks for hepaquery analysis</li><li><i><strong>distances </strong></i>contains distance measurements from original section sorted by timepoint as well as combined across timepoints</li><li><strong>cluster</strong> contains clustering information across spatial positions used in spatial enrichment analysis</li></ul><p><strong>STUtiility_mus_pb_ST.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>ST_berghei_liver</strong></p><p><strong>h5ad</strong></p><p>contains anndata files of ST data (normalized read counts), spot information, distance measurements, images and masks generated using the hepaquery package. </p><p><strong>visium_berghei_liver</strong></p><p>contains data generated with the <i>spaceranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h. </p><ul><li><i><strong>V10S29-135_A1</strong></i> contains spaceranger output for section 1 for infected and control sections at 38h post-infection</li><li><i><strong>V10S29-135_B1</strong></i> contains spaceranger output for section 1 for infected and control sections at 12h post-infection </li><li><i><strong>V10S29-135_C1 </strong></i>contains spaceranger output for section 1 for infected and control sections at 24h post-infection </li><li><i><strong>V10S29-135_D1 </strong></i>contains spaceranger output for section 2 for infected and control sections at 38h post-infection </li></ul><p><strong>se_visium.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>visium_berghei_liver</strong></p><p><strong>snSeq_berghei_liver</strong></p><p>contains data generated with the <i>cellranger </i>pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.</p><p><i><strong>cellranger_cnt_out</strong> </i>contains feature count matrix information from cell ranger output</p><p><strong>final_merged_curated_annotations_270623.RDS </strong>describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in <strong>snSeq_berghei_liver.tar.gz</strong></p><p><strong>raw images.zip </strong>contains raw images for supplementary figures 20-22</p><p><strong>adjusted images.zip </strong>contains brightness and contrast adjusted images for supplementary figures 20-22</p>
Comprehensive single-cell atlas of the mouse retina
<p><strong>Abstract:</strong></p> <p>Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity at the single-cell resolution by classifying and characterizing cell types in multiple tissues and species. While several mouse retinal scRNA-seq reference datasets have been published, each dataset either has a relatively small number of cells or is focused on specific cell classes, and thus is suboptimal for assessing gene expression patterns across all retina types at the same time. To establish a unified and comprehensive reference for the mouse retina, we first generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse whole retinas. This dataset was generated through the targeted enrichment of rare population cells via antibody-based magnetic cell sorting. By integrating this new dataset with public datasets, we conducted an integrated analysis to construct the Mouse Retina Cell Atlas (MRCA) for wild-type mice, which encompasses over 330,000 single cells. The MRCA characterizes 12 major classes and 138 cell types. It captured consensus cell type characterization from public datasets and identified additional new cell types. To facilitate the public use of the MRCA, we have deposited it in CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal for visualization and gene expression exploration. The comprehensive MRCA serves as an easy-to-use, one-stop data resource for the mouse retina communities.</p> <p> </p> <p><strong>Data description:</strong></p> <p>1. MRCA: scRNA-seq of the mouse retina - all cells<br>This file contains the full 330K single cells of the MRCA.<br><br>2. MRCA: scRNA-seq of the mouse retina - bipolar cell subclass<br>This file contains 147K single cells for bipolar cell subcass of the MRCA.</p> <p>3. MRCA: scRNA-seq of the mouse retina - RGC subclass<br>This file contains 77K single cells for retinal ganglion cell subclass of the MRCA.</p> <p>4. MRCA: scRNA-seq of the mouse retina - amacrine cell subclass<br>This file contains 43K single cells for amacrine cell subclass of the MRCA.</p> <p> </p>
Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus
<p>Dataset accompanying the manuscript "Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus". This dataset is used to re-create all figure panels with underlying data in the manuscript.</p>
Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum
<p>Here we provide the data for the paper "Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum". The dataset is saved as MAT-file (version 7.0). The source code for analysis is saved in github. Folder names indicate the contents corresponding to the specific figures in the paper.</p>
Human umbilical cord mesenchymal stem cell-derived exosomes combined with mouse nerve growth factor can more effectively ameliorate the motor disorder and brain pathological injury in mice with cerebral palsy
<p>Supplementary Table 1.The statistics of the pole-climbing experiments and the horizontal network test</p> <p>Supplementary Table 2.Results of Kruskal–Wallis test as presented in pole-climbing experiments and the horizontal network test.</p> <p>Supplementary Figure 1. Identification of human umbilical cord mesenchymal stem cell exosomes (hUC-MSCs-exos)</p> <p>Supplementary Figure 2. Results of horizontal network test and pole-climbing experiments.</p> <p> </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.