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6,040 results for “Single-Cell”
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>
Data from: Division of functional roles for termite gut protists revealed by single-cell transcriptomes
<p>The microbiome in the hindgut of wood-feeding termites comprises various species of bacteria, archaea, and protists. This gut community is indispensable for the termite, which thrives solely on recalcitrant and nitrogen-poor wood. However, the difficulty in culturing these microorganisms has hindered our understanding of the function of each species in the gut. Although protists predominate in the termite gut microbiome and play a major role in wood digestion, very few culture-independent studies have explored the contribution of each species to digestion. Here, we report single-cell transcriptomes of four protists species comprising the protist population in worldwide pest <em>Coptotermes formosanus</em>. Comparative transcriptomic analysis revealed that the expression patterns of the genes involved in wood digestion were different among species, reinforcing their division of roles in wood degradation. Transcriptomes, together with enzyme assays, also suggested that one of the protists, <em>Cononympha leidyi</em>, actively degrades chitin and assimilates it into amino acids. We propose that C. leidyi contributes to nitrogen recycling and inhibiting infection from entomopathogenic fungi through chitin degradation. Two of the genes for chitin degradation were further revealed to be acquired via lateral gene transfer (LGT) implying the importance of LGT in the evolution of symbiosis. Our single-cell-based approach successfully characterized the function of each protist in termite hindgut and explained why the gut community includes multiple species.</p>
Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders
<p>This record contains raw data related to the article "Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders".</p>
Single-cell profiling identifies pre-existing CD19-negative subclones in a B-ALL patient with CD19-negative relapse after CAR-T therapy
<p>This repository contains necessary files for reproducing the analysis in Rabilloud, Potier et al. (2020). The instructions for reproducing the analysis are given in github (https://github.com/Delphine-Potier/B-ALL-CAR-T) and extra files are available in GEO/SRA (GSE153697 ; SRP269742).</p>
Data from: Morphological identification and single-cell genomics of marine diplonemids
Recent global surveys of marine biodiversity have revealed that a group of organisms known as "marine diplonemids" constitutes one of the most abundant and diverse planktonic lineages [1]. Though discovered over a decade ago [2 and 3], their potential importance was unrecognized, and our knowledge remains restricted to a single gene amplified from environmental DNA, the 18S rRNA gene (small subunit [SSU]). Here, we use single-cell genomics (SCG) and microscopy to characterize ten marine diplonemids, isolated from a range of depths in the eastern North Pacific Ocean. Phylogenetic analysis confirms that the isolates reflect the entire range of marine diplonemid diversity, and comparisons to environmental SSU surveys show that sequences from the isolates range from rare to superabundant, including the single most common marine diplonemid known. SCG generated a total of ∼915 Mbp of assembled sequence across all ten cells and ∼4,000 protein-coding genes with homologs in the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology database, distributed across categories expected for heterotrophic protists. Models of highly conserved genes indicate a high density of non-canonical introns, lacking conventional GT-AG splice sites. Mapping metagenomic datasets [4] to SCG assemblies reveals virtually no overlap, suggesting that nuclear genomic diversity is too great for representative SCG data to provide meaningful phylogenetic context to metagenomic datasets. This work provides an entry point to the future identification, isolation, and cultivation of these elusive yet ecologically important cells. The high density of nonconventional introns, however, also portends difficulty in generating accurate gene models and highlights the need for the establishment of stable cultures and transcriptomic analyses.
scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution - Supplementary data and code
<p>Data and code to reproduce the analyses from the study: "scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution". </p>
Datasets for MambaCpG: Accurate Imputation of Single-cell DNA Methylation Status Using Mamba
Open the record for dataset details and reuse information.
A single-cell atlas of Drosophila trachea reveals glycosylation-mediated Notch signaling in cell fate specification
<p>Processed data files for the following article:</p><p>Li Y, Lu T, Dong P, Chen J, Zhao Q, Wang Y, Xiao T, Wu H, Zhao Q and Huang H. A single-cell atlas of Drosophila trachea reveals glycosylation-mediated Notch signaling in cell fate specification.</p><p>Please also refer to code at https://github.com/Tianfeng-Lu/single-cell-atlas-of-fly-trachea</p>
Seurat objects - conjunctiva single-cell atlas
<p>We provide here the Seurat objects for:</p> <p>1) the whole conjunctiva atlas</p> <p>2) Goblet cells subclusters</p> <p>3) tuft cells subclusters</p> <p>4) basal cells subclusters.</p> <p>All objects can be obtained with the code provided on Github.</p>
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>
Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data
<p>ConDecon is a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. This repository contains the raw data of the benchmarking analyses presented in the original publication using the pipeline of Avila-Cobos et al. (10.1038/s41467-020-19015-1). We used this pipeline to evaluate the ability of ConDecon and 17 other deconvolution methods to infer discrete cell type abundances in bulk tissues. The compressed file in this repository contains the synthetic bulk data, ground truth cell type proportions, and the predicted cell type proportions for each method and dataset associated with these analyses. Additional details can be found in the Methods section of the ConDecon publication.</p>
Raw Data and Scripts used in Regulation of single-cell heterogeneity of capsular polysaccharide synthesis in a human gut symbiont
<p>Raw data used in this publication. Single-cell analysis of promoter inversions reveals differential inversion rates as a determinant of bacterial population heterogeneity.</p> <p> </p> <p>Libx.zip contains raw sequencing reads</p> <p>scripts.zip contains code for analysis of reads and growth curve data</p> <p>SequencingRawDataFilesIndex.xls contains a description of all the raw data in each libx.zip.</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>
DELVE: Feature selection for preserving biological trajectories in single-cell data
<p>Contains preprocessed single-cell data and metadata for feature selection. Preprocessed adata objects can be accessed using the <em>read_h5ad</em> function in anndata. Also contains the source data files for reproducing the Figures and Supplementary Figures referenced in the manuscript.</p> <ul> <li>The RPE iterative indirect immunofluorescence imaging dataset (adata_RPE.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/10.1016/j.cels.2021.10.007</a>) was originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4525425">https://doi.org/10.5281/zenodo.4525425</a>).</li> <li>The PDAC iterative indirect immunofluorescence imaging datasets (listed below) were originally downloaded from the Zenodo repository (<a href="https://doi.org/10.5281/zenodo.7860332">https://doi.org/10.5281/zenodo.7860332</a>). <ul> <li>adata_PDAC_BxPC3_control.h5ad</li> <li>adata_PDAC_CFPAC_control.h5ad</li> <li>adata_PDAC_HPAC_control.h5ad</li> <li>adata_PDAC_MiaPaCa_control.h5ad</li> <li>adata_PDAC_Pa01C_control.h5ad</li> <li>adata_PDAC_Pa02C_control.h5ad</li> <li>adata_PDAC_Pa16C_control.h5ad</li> <li>adata_PDAC_PANC1_control.h5ad</li> <li>adata_PDAC_UM53_control.h5ad</li> </ul> </li> <li>The CD8+ T cell differentiation dataset (adata_CD8.h5ad) from Ref. (<a href="https://doi.org/10.1016/j.cels.2021.10.007">https://doi.org/</a><a href="https://doi.org/10.1126/sciimmunol.aaz6894">10.1126/sciimmunol.aaz6894</a>) was originally downloaded from the Gene Expression Omnibus under the accession code GSE131847 (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138266">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131847</a>).</li> <li>The definitive endoderm differentiation dataset (adata_DE.h5ad) contains multiplexed single-cell RNA sequencing data profiling the differentiation of human embryonic stem cells into the definitive endoderm. </li> </ul>
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>
Processed datasets used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics"
<p>These are the processed anndata objects used in Almet et al. (2024), "Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics".</p> <p>These datasets are stored as .h5ad files and are intended to be used with the <a href="https://scanpy.readthedocs.io/en/stable/api.html">Scanpy</a> package in Python. They contain all relevant cell type annotation, unnormalized and transformed gene expression counts, as well as the inferred intercellular fow networks inferred by FlowSig.</p>
single-cell imaging datasets associated with JupyterLab notebooks
Open the record for dataset details and reuse information.
Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)
<p>View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv).</p> <p>For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.</p>
ScienceDex guides
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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.