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161 results for “Single cell analyses”
bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses - Melanoma tutorial
<p>Downsampled version of the melanoma dataset originally published by <em><a href="https://genome.cshlp.org/content/28/9/1353">Ho et al </a>(1)</em>. The dataset is composed by cells from the 451Lu cell line. There are two samples available:</p> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Description</strong></td> <td><strong>R1/R2</strong></td> </tr> <tr> <td>451LU</td> <td>Parental cell line</td> <td>2500K_451LU_L003_R*_001.fastq.gz</td> </tr> <tr> <td>451LUBR3</td> <td>Vemurafenib-resistant sample treated with targeted BRAF inhibitors</td> <td>500K_451LUBR3_L004_R*_001.fastq.gz</td> </tr> </tbody> </table> <p><br> (1) Ho YJ, Anaparthy N, Molik D, et al. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. <em>Genome Res</em>. 2018;28(9):1353-1363. doi:10.1101/gr.234062.117</p>
Single-cell analyses of axolotl forebrain organization, neurogenesis, and regeneration
<p>Preprint: https://doi.org/10.1101/2022.03.21.485045</p> <p>Abstract:</p> <p>Salamanders are important tetrapod models to study brain organization and regeneration, however the identity and evolutionary conservation of brain cell types is largely unknown. Here, we delineate cell populations in the axolotl telencephalon during homeostasis and regeneration, representing the first single-cell genomic and spatial profiling of an anamniote tetrapod brain. We identify glutamatergic neurons with similarities to amniote neurons of hippocampus, dorsal and lateral cortex, and conserved GABAergic neuron classes. We infer transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific direct and indirect neurogenesis, and unravel conserved signatures. Following brain injury, ependymoglia activate an injury-specific state before reestablishing lost neuron populations and axonal connections. Together, our analyses yield key insights into the organization, evolution, and regeneration of a tetrapod nervous system.</p> <p> </p> <p>File description:</p> <p>all_nuclei_clustered_highlevel_anno.rds - Seurat object including all snRNA-seq data from uninjured pallium, both from microdissections and whole pallium multiome.</p> <p>pallium_metadata_simp.csv - csv file containing a simplified version of the metadata for the uninjured pallium</p> <p>Edu_1_2_4_6_8_12_fil_highvarfeat.rds - Seurat object containing all Div-seq data for the pallium injury time course</p> <p>divseq_predicted_metadata.csv - csv file containing a simplified version of the metadata for the pallium injury time course</p> <p>ep_wpi_srat.rds - Seurat object containing an integrated version of ependymoglia cells from uninjured and injured pallium (see Fig 6 in the preprint).</p> <p>D1_113_sub_b.rds - Seurat object containing a Visium data for the axolotl pallium</p> <p>multiome_integATAC_SCT.rds - Signac object containing the data used for multiome analysis of the uninjured whole pallium</p> <p>predictions_cell2loc.csv - csv file containing cell2location scores for the uninjured pallium cell types in the Visium dataset</p>
microSPLiT single-cell and bulk transcriptomes analysed with STAR - Pseudomonas putida KT2440/pKJK5
<h3>Description of the data and file structure</h3> <p>Data are displayed as 2 files</p> <p><strong>1. Bulk transcriptomics results (Bulk_STAR.csv)</strong></p> <p>STAR processed data combined in a gene x sample table</p> <p><strong>2. microSPLiT single-cell results (microSPLiT_STARsolo.xlsx)</strong></p> <p>STARsolo processed data combined as sublibraries’ gene associated transcript numbers (UMIs) per cell for the control (E1) and experiment (E2) sublibraries (F1-8) - (1 sublibrary per table).</p> <div> <p> </p> </div>
A Galaxy-based training resource for single-cell RNA-seq quality control and analyses
<p>This is the tutorial data for the 'Single-cell quality control with scater' tutorial on the Galaxy Training Network. The data is the same dataset that is used as the inbuilt example dataset within scater, but has been implemented as individual files.</p>
Pan-cancer analyses refine the single-cell portrait of tumor-infiltrating dendritic cells
<p>This is the dataset for "Pan-cancer Analyses Refine the Single-Cell Portrait of Tumor-Infiltrating Dendritic Cells".</p> <p>File "panDC_all_h5ad.gz" contains processed expression .h5ad data.</p> <p>File "panDC_metadata.csv" contains the meta data for this study.</p>
Single-cell analyses of polyclonal Plasmodium vivax infections and their consequences on parasite transmission Source Data
<p>Source Data for the paper by Hazzard et al. entitled <em>Single-cell analyses of polyclonal Plasmodium vivax infections and their consequences on parasite transmission</em>.</p>
Data from: High-resolution single-cell analyses reveal evolutionary constraints and evolvability of sexual circuits in Drosophila
Open the record for dataset details and reuse information.
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</p>
Processed data from "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"
<p>This is the processed data from our manscript "Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits"</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>
Supplementary collection of preprocessed and analysed data objects for the paper: "Gene-level alignment of single cell trajectories"
<p>Supplementary collection of h5ad data files containing preprocessed and analysed single-cell reference and query datasets discussed in the case studies of the paper:<em><strong>"Gene-level alignment of single cell trajectories"</strong></em> (<a href="https://doi.org/10.1101/2023.03.08.531713" rel="nofollow">https://doi.org/10.1101/2023.03.08.531713</a>).</p> <p>Our artificial thymic organoid raw sequencing data are available from ArrayExpress (E-MTAB-12720).</p> <p>Below lists the original sources of the literature datasets we downloaded prior to our own preprocessing and trajectory analysis.</p> <ul> <li> <p>Original pan fetal reference data from: Suo, C. et al. (2022) "Mapping the developing human immune system across organs" (<a href="https://doi.org/10.1126/science.abo0510">https://doi.org/10.1126/science.abo0510</a><a href="http://paperpile.com/b/XTpgLG/TQtQ3">), </a>downloaded from <a href="https://developmental.cellatlas.io/fetal-immune">https://developmental.cellatlas.io/fetal-immune</a>.</p> </li> </ul> <ul> <li> <p>Original healthy/IPF data from: Adams, T. S. et al. (2020), "Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis" (<a href="https://doi.org/10.1126/sciadv.aba1983">https://doi.org/10.1126/sciadv.aba1983</a>), downloaded from GEO (GSE136831).</p> </li> </ul> <p>We have run our scripts in <a href="https://github.com/Teichlab/G2G_notebooks">https://github.com/Teichlab/G2G_notebooks</a> to generate new latent embeddings and infer pseudotime trajectories for the above datasets, and used them to model trajectory alignments with our newly developed framework: ‘Genes2Genes’ <a href="https://github.com/Teichlab/Genes2Genes">https://github.com/Teichlab/Genes2Genes</a>. </p>
Using LLM Models and Explainable ML to Analyse Biomarkers at Single Cell Level
<p>Single-cell RNA sequencing (scRNA-seq) technology has significantly advanced our understanding of the diversity of cells and how this diversity is implicated in diseases. Yet, translating these findings across various scRNA-seq datasets poses challenges due to technical variability and dataset-specific biases. To overcome this, we present a novel approach that employs both an LLM-based framework and explainable machine learning to facilitate generalization across single-cell datasets and identify gene signatures to capture disease-driven transcriptional changes. Our approach uses scBERT, which harnesses shared transcriptomic features among cell types to establish consistent cell-type annotations across multiple scRNA-seq datasets. Additionally, we employ a symbolic regression algorithm to pinpoint highly relevant yet minimally redundant models and features for inferring a cell type’s disease state based on its transcriptomic profile. We ascertain the versatility of these cell-specific gene signatures across datasets, showcasing their resilience as molecular markers to pinpoint and characterize disease-associated cell types. Validation is carried out using four publicly available scRNA-seq datasets from both healthy individuals and those suffering from ulcerative colitis (UC). This demonstrates our approach’s efficacy in bridging disparities specific to different datasets, fostering comparative analyses. Notably, the simplicity and symbolic nature of the retrieved gene signatures facilitate their interpretability, allowing us to elucidate underlying molecular disease mechanisms using these models.</p>
Single cell transcriptomic analyses reveal the impact of bHLH factors on human retinal organoid development
Open the record for dataset details and reuse information.
Integrative analyses of single-cell transcriptome and immune profiling reveal clonal expansion of T cells in the blood and cerebrospinal fluid of Parkinson's disease
<p>An increasing number of studies has indicated that the immune system plays important roles in the pathogenesis of Parkinson's disease. However, little is known about the contribution of adaptive immune responses in Parkinson's disease. Here, we performed comprehensive integrative analyses of single-cell transcriptome and immune profiling of the blood of 8 Parkinson's patients and 13 healthy controls as well as the cerebrospinal fluid of 6 Parkinson's patients, 4 Alzheimer's patients, 5 mild cognitive impairment (MCI) patients and 9 healthy controls. In total, 22 T cell subsets with distinct functions and clonalities were identified from 121,402 T cells. We observed significant clonal expansion of effector CD8+ T cells in Parkinson's patients, which formed a gradient of transcriptional states from central memory CD8+ T cells to early effector CD8+ T cells followed by terminal effector CD8+ T cells. Shared TCRs in this progression suggest TCRs may be involved in the state transition of CD8+ T cells stimulated by antigens. Notably, we also found that a group of clonally expanded cytotoxic CD4+ T cells were significantly increased in Parkinson's patients compared to controls, suggesting their cytotoxic roles in Parkinson's disease. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of patients with Parkinson's disease. These results reveal an adaptive immune response in the blood and cerebrospinal fluid of Parkinson's disease and provide novel evidence of clonal, antigen-experienced T cells patrolling in the blood and cerebrospinal fluid of Parkinson's disease.</p>
Integrative single-cell multi-modal analyses reveal detailed spatial cellular organization directing human heart morphogenesis
<div> <p><span>The heart, which is the first organ to develop, is highly dependent on its form to function. However, how diverse cardiac cell types spatially coordinate to create complex morphological structures critical for heart function remains to be elucidated. Here, we show that integration of single cell RNA-sequencing with high-resolution multiplexed error-robust fluorescent <em>in situ </em>hybridization (MERFISH) not only resolves the identity of cardiac cell types developing the human heart but also provides a spatial mapping of individual cells that enables illumination of their organization into cellular communities forming distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, supporting their specialization according to cellular ecosystem and anatomic region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities revealed signaling pathways orchestrating the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. <em>In vivo </em>conditional genetic mouse models and in vitro human pluripotent stem cell studies confirmed an intricate multicellular PLXN-SEMA crosstalk among specific ventricular cardiomyocyte, fibroblast and endothelial cell subpopulations that directs the compaction of the ventricular wall layers. Thus, these detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodeling the human heart offer new insights into structural heart diseases as well as engineering complex multi-cellular tissues for human heart repair.</span></p> </div>
Multimodal single-cell analyses reveal distinct fusion-regulated transcriptional programs in Ewing sarcoma.
<p>All processed data for analysis at <a href="https://github.com/furlan-lab/EwS_multiome">https://github.com/furlan-lab/EwS_multiome</a></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.