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2,063 results for “single cell transcriptomics”
Single-cell spatial transcriptomics and proteomics of APOE Christchurch in 5xFAD and PS19 mice
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Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
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Data from: Three-dimensional single-cell transcriptome imaging of thick tissues
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Single-cell spatial transcriptomics of ACAN cKO in WT and 5xFAD mice
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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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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>
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>
Single cell transcriptomics of of Abedinium reveals a new early-branching dinoflagellate lineage
<p>Dinoflagellates possess many unique cellular characteristics with unresolved evolutionary histories including nuclei with greatly expanded genomes and chromatin packaged using histone-like proteins and dinoflagellate-viral nucleoproteins instead of histones, highly reduced mitochondrial genomes with extensive RNA editing, a mix of photosynthetic and cryptic secondary plastids, and tertiary plastids. Resolving the evolutionary origin of these traits requires understanding their ancestral states and early intermediates. Several deep-branching dinoflagellate lineages are good candidates for such reconstruction, however they tend to be delicate and environmentally sparse, so such analyses are not always simple. Here, we employ transcriptome sequencing from manually-isolated and microscopically documented cells to resolve the placement of two cells of one such genus, <i>Abedinium</i>,<i> </i>collected by ROV in deep waters off the coast of Monterey Bay. One cell corresponds to the only described species, <i>A. dasypus</i>, while the second cell is distinct and formally described as<i> Abedinium folium, </i>sp. nov. <i>Abedinium</i> has classically been assigned to the deep-branching dinoflagellates subgroup Noctilucea, which is weakly supported by phylogenetic analyses of the single characterized gene from any member of the genus, small subunit ribosomal RNA (SSU rRNA). However, a phylogenetic analysis based on 221 proteins from the transcriptome places <i>Abedinium </i>in a distinct lineage, separate from and basal to the Noctilucea and the rest of the core dinoflagellates. The transcriptome also contains evidence of a cryptic plastid functioning in the biosynthesis of isoprenoids, iron-sulfur clusters, and heme, a mitochondrial genome with all three expected protein-coding genes (<i>cob</i>, <i>cox1</i>, and cox3), and the presence of some but not all dinoflagellate-specific chromatin packaging proteins.</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 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>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Files for Manuscript "A pulmonologist's guide to perform and analyse cross-species single-lung-cell transcriptomics"
<p>Input Files for Manuscript "A pulmonologist’s guide to perform and analyse cross-species single-lung-cell transcriptomic"</p> <p>See https://github.com/GenStatLeipzig/pulmonologists_interspecies_scRNA for details.</p> <p>Manuscript authored by:</p> <p>Peter Pennitz1,2*, Holger Kirsten3*, Vincent D. Friedrich3,4, Emanuel Wyler5, Cengiz Goekeri1,2,6, Benedikt Obermayer7, Gitta A. Heinz8, Mir-Farzin Mashreghi8,9, Maren Büttner10,11 Jakob Trimpert12, Markus Landthaler5,13, Norbert Suttorp2, Andreas C. Hocke1,2, Stefan Hippenstiel2, Mario Tönnies14, Markus Scholz3, Wolfgang M. Kuebler15,16, Martin Witzenrath1,2,16, Katja Hoenzke1,2 and Geraldine Nouailles1,2,# </p> <p> </p> <p>1 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Division of Pulmonary Inflammation, Berlin, Germany. </p> <p>2 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany. </p> <p>3 University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany. </p> <p>4 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany. </p> <p>5 Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany. </p> <p>6 Cyprus International University, Faculty of Medicine, Nicosia, Cyprus. </p> <p>7 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Unit Bioinformatics, Berlin, Germany. </p> <p>8 Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), A Leibniz Institute, Therapeutic Gene Regulation, Berlin, Germany. </p> <p>9 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Berlin, Germany. </p> <p>10 University of Bonn, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, Bonn, Germany. </p> <p>11 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Systems Medicine, Bonn, Germany. </p> <p>12 Freie Universität Berlin, Institute of Virology, Berlin, Germany. </p> <p>13 Humboldt-Universität zu Berlin, Institute for Biology, IRI Life Sciences, Berlin, Germany. </p> <p>14 HELIOS Clinic Emil von Behring, Department of Pneumology and Department of Thoracic Surgery, Chest Hospital Heckeshorn, Berlin, Germany. </p> <p>15 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Berlin, Germany. </p> <ol> <li> <p>German Center for Lung Research (DZL), Berlin, Germany. </p> </li> </ol> <p> </p> <p>* Authors contributed equally to this work </p> <p> </p> <p>References for original datasets :</p> <p>Human Charité: Hocke A, Hönzke K, Obermayer B, Baumgardt M, Wyler E, Hippenstiel S, Mache C. Charité Berlin /Berlin Institute of Health. GEO accessions GSM5958267, GSM5958272, GSM5958283, GSM5958285</p> <p>Human Travaglini et al.: published at <a href="https://www.synapse.org/">https://www.synapse.org</a> by Travaglini et al. (<a href="https://doi.org/10.1038/s41586-020-2922-4">https://doi.org/10.1038/s41586-020-2922-4</a>)</p> <p>Monkey: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Speranza et al. (<a href="https://doi.org/10.1126/scitranslmed.abe8146">https://doi.org/10.1126/scitranslmed.abe8146</a>)</p> <p>Hamster: Charité Berlin, see: <a href="https://doi.org/10.1038/s41467-021-25030-7">https://doi.org/10.1038/s41467-021-25030-7</a></p> <p>Mouse: Pennitz P, Witzenrath, M, Nouailles G, Berlin Charité.</p> <p>Rat and Pig: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Raredon et al. (<a href="https://doi.org/10.1126/sciadv.aaw3851">https://doi.org/10.1126/sciadv.aaw3851</a>) .</p> <p>Annotation: Ensembl BioMart</p>
Inferring causal cell-cell communication from single-cell transcriptomics
<p>Data used to produce the analysis in "Inferring causal cell-cell communication from single-cell transcriptomics" by Almet and Nie (2022)</p>
Integrated single cell transcriptomics of cerebrospinal fluid cells in early Multiple Sclerosis
<p>h5ad file for "Integrated single cell transcriptomics of cerebrospinal fluid cells in early Multiple Sclerosis"</p>
The transcriptomic landscape of normal and ineffective erythropoiesis at single cell resolution
<p><span>Ineffective erythropoiesis, the death of maturing erythroid cells, is a common cause of anemia. To better understand why this occurs, we studied the fates and adaptations of single erythroid marrow cells from individuals with Diamond Blackfan anemia (DBA), del(5q) myelodysplastic syndrome (del(5q) MDS), and normal controls, and defined an unhealthy (vs. healthy) differentiation trajectory, using velocity pseudotime and cell surface protein assessment. The pseudotime trajectories diverge immediately after the cells upregulate transferrin receptor (CD71), import iron, and initiate heme synthesis, although cell death occurs much later. Cells destined to die highly express heme-responsive genes, including ribosomal protein and globin genes. In contrast, surviving cells downregulate heme synthesis, while upregulating DNA damage response, hypoxia, and HIF1 pathways. Surprisingly, 24±12% of cells from controls follow the unhealthy trajectory, implying that heme also regulates cell fate decisions during normal red cell production. Del(5q) MDS (unlike DBA) results from somatic mutations, so many normal (unmutated) erythroid cells persist. By independently tracking their trajectory, we gained insight into why they cannot expand to prevent anemia. In addition, we show that intron retention is especially prominent during red cell differentiation. The additional information provided by messages with retained introns also allowed us to align data from multiple independent experiments and thus accurately query the transcriptomic changes that occur as single erythroid cells mature. </span></p>
Single-cell transcriptomics data set for plasmodium falciparum 3D7 asexual and sexual stages
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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>
Single-cell transcriptome analysis reveals evolving tumor microenvironment induced by immunochemotherapy in nasopharyngeal carcinoma
<p>18 bulks and 11 single-cell RNA sequencing samples from paired before anti-PD-1 contained treatment and on treatment in patients with treatment-naive high-risk metastatic locally advanced NPCs were obtained. We aim to explore the mechanism of response heterogeneity for locally advanced NPCs underwent immunochemotherapy.</p>
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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.