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11,105 results for “single cells”
utility: Collection of Tumor-Infiltrating Lymphocyte Single-Cell Experiments with TCR
<p><strong>Introduction</strong></p> <p>The original intent of assembling a data set of publicly-available tumor-infiltrating T cells (TILs) with paired TCR sequencing was to expand and improve the <a href="https://github.com/ncborcherding/scRepertoire">scRepertoire</a> R package. However, after some discussion, we decided to release the data set for everyone, a complete summary of the sequencing runs and the sample information can be found in the meta data of the Seurat object. This repository is the 4th version of the data, with addition of cells and changes to the workflow. </p> <p><strong>Methods</strong></p> <p><em>Single-Cell Data Processing</em></p> <p>The filtered gene matrices output from Cell Ranger align function from individual sequencing runs (10x Genomics, Pleasanton, CA) loaded into the R global environment. For each sequencing run cell barcodes were appended to contain a unique prefix to prevent issues with duplicate barcodes. The results were then ported into individual Seurat objects (<a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a>), where the cells with > 10% mitochondrial genes and/or 2.5x natural log distribution of counts were excluded for quality control purposes. At the individual sequencing run level, doublets were estimated using the scDblFinder (v1.4.0) R package.</p> <p><em>Annotation of Cells</em></p> <p>Automatic annotation was performed using the singler (v1.4.1) R package (<a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a>) with the HPCA (<a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a>) and Monaco (<a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a>) data sets as references and the fine label discriminators. Individual sequencing runs were subsetted to run through the singleR algorithm in order to reduce memory demands. The output of all the singleR analyses were collated and appended to the meta data of the seurat object. Likewise, the ProjecTILs (v0.4.1) R Package (<a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a>) was used for automatic annotation as a partially orthogonal approach. </p> <p><em>Addition of TCR data</em></p> <p>The filtered contig annotation T cell receptor (TCR) data for available sequencing runs were loaded into the R global environment. Individual contigs were combined using the combineTCR() function of scRepertoire (v1.3.5) R Package (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a>). Clonotypes were assigned to barcodes and were multiple duplicate chains for individual cells were filtered to select for the top expressing contig by read count. The clonotype data was then added to the Seurat Object with proportion across individual patients being used to calculate frequency.</p> <p><strong>Citations</strong></p> <p>As of right now, there is no citation associated with the assembled data set. However if using the data, please find the corresponding manuscript for each data set in the meta.data of the single-cell object. In addition, if using the processed data, feel free to modify the language in the methods section (above) and please cite the appropriate manuscripts of the software or references that were used.</p> <p><em>Itemized List of the Software Used</em></p> <ul> <li>Seurat v4.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34062119/">citation</a></li> <li>harmony v1.0 - <a href="https://pubmed.ncbi.nlm.nih.gov/31740819/">citation</a></li> <li>singler v1.4.1 - <a href="https://pubmed.ncbi.nlm.nih.gov/30643263/">citation</a></li> <li>ProjecTILs v2.0.3 - <a href="https://pubmed.ncbi.nlm.nih.gov/34017005/">citation</a></li> <li>UCell v1.0.0 - <a href="https://www.biorxiv.org/content/10.1101/2021.04.13.439670v1">citation</a></li> <li>scRepertoire v1.3.5 - <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7400693/">citation</a></li> </ul> <p><em>Itemized List of Reference Data Used</em></p> <ul> <li>Human Primary Cell Atlas (HPCA) - <a href="https://pubmed.ncbi.nlm.nih.gov/24053356/">citation</a></li> <li>Monaco Data Set - <a href="https://pubmed.ncbi.nlm.nih.gov/30726743/">citation</a></li> </ul> <p><strong>Future Directions</strong></p> <ul> <li>Data Hosting for Interactive Analysis</li> <li>Easy Submission Portal for Researchers to Add Data</li> <li>Using the Data to Build a Reference Atlas</li> </ul> <p>There are areas in which we are actively hoping to develop to further facilitate the usage of the data set - if you have other suggestions, please reach out using the contact information below.</p> <p><strong>Contact</strong></p> <p>Questions, comments, and suggestions, please feel free to contact Nick Borcherding via this repository, <a href="mailto:ncborch@gmail.com">email</a>, or using <a href="https://twitter.com/theHumanBorch">twitter</a>.</p>
Single Cell Profiling of Human Dura and Meningioma Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response
<p>This repository contains the single-cell RNA-seq data described in the publication “Single Cell Profiling of Human Dura and Meningioma Reveals Cellular Meningeal Landscape and Insights into Meningioma Immune Response.” Data is provided as count matrices and Seurat objects.</p>
Single-cell RNA-seq supplementary data
<p>These files are supplementary data for the article Laczik M, Erdős E, Ozgyin L, Hevessy Zs, Csősz É, Kalló G, Nagy T, Barta E, Póliska Sz, Szatmári I and Bálint BL: Extensive proteome and functional genomic profiling of variability between genetically identical human B-lymphoblastoid cells. The files are outputs from the 10x Genomics software Cell Ranger and Loupe Browser, they contain QC data for the single cell sequencing described in the article, and also comparative analyses between 3 datasets (GM22648old, GM22648new, GM22649). For further details please see the article.</p>
Bayesian Network analysis for Single Cell Multiomics
<p>The data from stratified random samples of scRNA expression, surface marker and SNF cluster membership were integrated with high-resolution CT (HRCT) Scores of COVID-19 patients. The healthy and recovered individuals were assigned an HRCT score of zero, indicating absence of active pneumonia. The integrative modeling analysis was carried out using the wiseR package for end-to-end Bayesian network learning, inference and dashboard deployment. All continuous variables in the integrated data were discretized using the k-means algorithm with k=3 for biological interpretability as low, medium and high. A discrete Bayesian Network was learned from the data using hill climbing optimization for finding the directed acyclic graph encoding the structural dependencies between variables. Eleven Bayesian network structures were ensembled averaged to derive the consensus structure. The consensus structure was then parametrized with marginal and conditional probability distributions using Monte Carlo Markov Chain (MCMC) approximate inference method.</p>
Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization
<p>To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca).</p> <p>The following files are included:<br> 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered.<br> 2) int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables.<br> 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses.</p> <p>4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels.<br> <br> This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.</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>
Fast Intratumor Heterogeneity Inference from Single-Cell Sequencing Data (simulated data - Extended Data Figures)
<p>This data repository contains simulated data used for benchmarking HUNTRESS against the existing alternative tools. Results of the benchmarking are shown in Extended Data Figures 1-10 of the paper "Fast Intratumor Heterogeneity Inference from Single-Cell Sequencing Data" (to appear in Nature Computational Science). </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>
scMEGA: Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference
<p>The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.</p>
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies
<p>Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy</p> <p>(ACM) convey high risks for the development of heart failure through unknown mechanisms. Using</p> <p>single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and</p> <p>61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic</p> <p>disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional</p> <p>states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular</p> <p>responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene</p> <p>expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and</p> <p>molecular architectures of human heart failure and suggest candidate therapeutic targets.</p> <p> </p> <p>Link to article: https://www.science.org/doi/10.1126/science.abo1984</p> <p>To match Clinical information (supplementary information) with Sample IDs on Zenodo, please access the Excel table "Additional_note_samples.xlsx" on the github repository: https://github.com/heiniglab/DCM_heart_cell_atlas</p>
Cell Atlas at Single-Nuclei Resolution of the Adult Human Adrenal Gland and Adrenocortical Adenomas
<p><strong>Abstract</strong></p> <p>The human adrenal glands are highly dynamic endocrine organs that are involved in the secretion of various hormones such as steroids and catecholamines. Here we present a single-nuclei and spatial transcriptomic analysis of healthy adult human adrenal glands to provide a complete adrenal gland atlas. With this, we show how such an atlas can be taken advantage when studying adrenocortical diseases, such as adrenocortical adenomas (ACA). Using nornal adrenal as reference, we showed a high intra-tumoural heterogeneity in the single-nuclei transcriptome of ACA, revealing the presence of specific cell populations associated with cortisol secretion and genetic background.</p> <p> </p> <p><strong>Data content</strong></p> <p>This dataset contains the two Visium replicates (NAG-7A and NAG-7B) processed using Space Ranger (spaceranger-1.0.0).</p> <p> </p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</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>
A spatiotemporally resolved single cell atlas of the Plasmodium liver stage
<p>This code is associated with the paper "A spatiotemporally resolved single cell atlas of the <em>Plasmodium</em> liver stage"</p> <p>Data generated in this study have been deposited in Gene Expression Omnibus with the accession code <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181725">GSE181725</a>. The code used to process the raw data to a Scanpy/Seurat structure used for analysis is available at <a href="https://github.com/AmichayAfriat/SpatioTemporal_malaria_liver_stage_atlas/">https://github.com/AmichayAfriat/SpatioTemporal_malaria_liver_stage_atlas/</a>.</p> <p>In this repository, you will find processed scRNAseq data and the codes used for further analysis and data visualization. Codes written in R (4.0.2; packages include seurat, dplyr, tidyr, ggpubr, stats, and EnhancedVolcano) and MATLAB (2019a).</p> <p><strong>Folder: Additional data</strong></p> <ul> <li>Metadata used in the analysis</li> <li>NZ_data.txt - Zonation data extracted from smFISH images using the 'analyze_neighborhood_intensity.m' function </li> </ul> <p><strong>Folder: Figures_source_data</strong></p> <ul> <li>Data underlying the graphical representations used in figures</li> <li>Each file contains clearly labeled sheets for distinct subplots</li> </ul> <p><strong>Folder: functions</strong></p> <ul> <li>Functions used in Matlab scripts</li> </ul> <p><strong>Folder: GSEA_OUTPUT</strong></p> <ul> <li>Gene Set Enrichment Analysis (GSEA, v3.0) output.</li> <li>Curated KEGG and HALLMARK annotations were used</li> <li>Filtered for minimum 15 genes in set and maximum 500. Default setting of 1,000 permutation was used to establish significance. For abortive cells DGE stricter thresholds were used - relative expression > 10<sup>-4</sup>, and minimum 30 genes in set</li> </ul> <p> </p>
An anti-influenza combined therapy assessed by single cell RNA-sequencing
<p>Figures source data for<strong> </strong>"<strong>An anti-influenza combined therapy assessed by single cell RNA-sequencing</strong>"</p> <p> </p>
Data Deposition: Time-resolved analysis of transcription kinetics in single live mammalian cells
<p>Actb_result_ACF_fitting: Results of the steady-state autocorrelation method from Actb gene. </p> <p>Actb_result_FP: Results of the time-resolved measurement from Actb gene with Flavopiridol. </p> <p>Actb_result_Trp: Results of the time-resolved measurement from Actb gene with triptolide</p> <p>Arc_result_ACF_fitting: Results of the steady-state autocorrelation method from Arc gene. </p> <p>Arc_result_FP: Results of the time-resolved measurement from Arc gene with Flavopiridol. </p> <p>Arc_result_Trp: Results of the time-resolved measurement from Arc gene with triptolide</p> <p>Simulation_steady_state_errors_Actb_210707_n100</p> <p>: results of autocorrelation fitting from steady-state simulation of Actb transcription</p> <p>Simulation_steady_state_errors_Arc_210707_n100</p> <p>: results of autocorrelation fitting from steady-state simulation of Arc transcription</p> <p>Simulation_time_resolved_errors_Actb_210707_n100</p> <p>: results of time-resolved model fitting from initiation inhibited simulation of Actb transcription</p> <p>Simulation_time_resolved_errors_Arc_210707_n100</p> <p>: results of time-resolved model fitting from initiation inhibited simulation of Arc transcription</p>
Single-cell RNA-seq datasets derived from human parasitic nematode Brugia malayi microfilariae
<p>Single-cell RNA-seq data of the parasitic nematode <em>Brugia malayi</em> in the microfilariae development stage. Data includes untreated cellular transcriptional states and the transcriptional response to ivermectin (1 µM). </p> <p>The unfiltered gene expression matrix is provided as both a Seurat and AnnData object. Filtered datasets are provided as .csv files for direct import into R. </p>
Synaptic currents through Purkinje cells in response to a single impulse in the granular layer
<p>Synaptic currents through Purkinje cells in response to a single impulse in the granular layer</p>
Single cell suppression profiling of human regulatory T cells
<p>FCS files were generated on a CyTOF Helios (Standard BioTools).</p> <p>Password to unzip the fcs-file folder “scSPOT2024”</p>
The single cell RNA sequencing of Bullous Pemphigoid
<p>The Sparse count tables in 10XGenomics format (barcodes.tsv.gz features.tsv.gz matrix.mtx.gz) of 33 samples as detailed in Nat Commun 15, 5949 (2024). </p> <p> Including single-cell RNA sequencing (scRNA-seq) from five lesions of Bullous Pemphigoid (BP) patients and eight normal skin of healthy donors, eight PBMC of BP patients and eight PBMC of healthy donors, four blister of BP patients.</p> <p>If you utilize this dataset in your research, kindly cite our article: Liu, T., Wang, Z., Xue, X. et al. Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease. Nat Commun 15, 5949 (2024). https://doi.org/10.1038/s41467-024-50283-3.</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.