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11,105 results for “single cells”
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - single cells dataset
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". dataset of 2300 single cells. File names indicate unique sequencing runs. In the manuscripts, the informations about cell lines are found in the Supplemental Table 1. </p>
Sequence data for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol" - Protocol optimization
<p>Sequence data (Illumina MiSeq runs) for the article "Whole transcriptome analysis of thousands of FACS-sorted single cells with the single cell nanoCAGE protocol". Optimization of the protocol. Files names indicate unique run identifiers. In the manuscript, the link between unique run identifiers, cells and purpose of the experiment is found in the Supplemental Table 1. </p>
Single-cell ATAC-seq control of cross-contaminations (experiment 2)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program. To evaluate damage and carry-over of debris from FACS-sorting, two IFCs were run in two C1 machines in parallel. In the first (flowcell ID 1772-123-155), the cells not washed and in the second, they were washed (ID 1772-123-158).</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Single-cell ATAC-seq control of cross-contaminations (experiment 1)
<p>On the Fluidigm C1 platform for single-cell analysis, the cells are captured in 96 chambers arranged serially, and then washed before further processing. Thus, debris present from the loading medium or released by captured cells upstream are a possible source of contamination. We generated a control datasets using the single-cell ATAC-seq protocol available from Fluidigm's ScriptHub. We cultivated human Hep G2 and mouse Hepa 1-6 (both are liver cancer cell lines), stained them with green and red calceins (respectively), and loaded them at equal concentration in a Fluidigm medium flow cell (old design), before running the C1 single-cell ATAC-seq program.</p> <p>The data deposited here is a sequencing run (Illumina MiSeq) of these ATAC-seq libraries. The metadata indicating the contents of each well is being uploaded separately and this record will be updated once the DOIs are available.</p>
Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells
<p>Single cell gene expression data for the paper "Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells" to appear in PLOS Pathogens.</p> <p>Package contains two single cell data sets, one consisting of single cells from PBMCs in three animals, and the other three tissues from one animal.</p> <p>The single-cells are grouped based on the SIV viral genes that they express.</p> <p> </p>
Analysis of lac operon induction with single cell resolution using the DIMM microfluidics chip and the MoMA software
<p>In this work, we demonstrate the power of the DIMM (Dual Input Mother Machine), a new microfluidics setup allowing long term environmental control and single cell resolution, and MoMA (Mother Machine Analyzer), its companion image analysis sofware.</p> <p>We study the induction of the <em>lac</em> operon in <em>Escherichia coli</em> MG1655 when nutrients alternate between glucose and lactose, using a translational fusion of LacZ with GFP integrated at the endogeneous locus.</p> <p>The dataset is made of two archives:</p> <ul> <li>DIMM_MoMA_images.tar.gz contains the image data produced in this study in a form ready to be used in MoMA</li> <li>DIMM_MoMA_data.tar.gz contains textual data files produced by MoMA and its postprocessing, with the cell identifier, length and fluorescence intensity of each cell in each frame.</li> </ul> <p>Detailed instructions are provided in the ReadMe.md file (NB: this is a text file using markdown syntax, you can open it in your favorite text editor).</p>
Human bone marrow assessment by single-cell RNA sequencing
<p>Seurat objects and CyTOF data for <a href="https://doi.org/10.1172/jci.insight.124928">10.1172/jci.insight.124928</a></p> <p>Please run UpdateSeuratObject() after loading. Small object contains annotated metadata with cca and tsne analyses. Large object contains additional reductions (eg umap). </p>
Comparison of Fixed Single Cell RNA-seq Methods to Enable Transcriptome Profiling of Neutrophils in Clinical Samples
<p>Monitoring neutrophil gene expression is a powerful tool for understanding disease mechanisms, developing new diagnostics, therapies and optimizing clinical trials. Neutrophils are sensitive to the processing, storage and transportation steps that are involved in clinical sample analysis. This study is the first to evaluate the capabilities of technologies from 10X Genomics, PARSE Biosciences, and HIVE (Honeycomb Biotechnologies) to generate high-quality RNA data from human blood-derived neutrophils. Our comparative analysis shows that all methods produced high quality data, importantly capturing the transcriptomes of neutrophils. 10X FLEX cell populations in particular showed a close concordance with the flow cytometry data. Here, we establish a reliable single-cell RNA sequencing workflow for neutrophils in clinical trials: we offer guidelines on sample collection to preserve RNA quality and demonstrate how each method performs in capturing sensitive cell populations in clinical practice.</p> <p><strong>This dataset includes the FACS, 10X 3', Parse, 10X Flex, and Hive data and analysis.</strong></p>
Cross-disease integration of single-cell RNA sequencing data from lung myeloid cells reveals TAM signature in in vitro model
<p>Single cells from a 3D human cell-based model comprising tumor cell line-derived spheroids, cancer-associated fibroblasts and primary monocytes were dissociated and analyzed using scRNAseq. 4 monocyte donors were used in the 3D model, and 3 monocyte donors were used for 2D differentiation of macrophages.</p>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
Single-cell RNAseq of Day 49 midbrain organoids from healthy and alpha-synuclein triplication iPSC lines
<p>Unbiased single-cell RNAseq of Day 49 midbrain dopaminergic organoids (10x 3' v3) of the Patikas et al. publication.</p> <p> </p> <p>The mono-unt-celltypes.h5ad refers to the object shown at Fig 2A and contains 3 cell lines:</p> <ol> <li>KOLF2 ( Control cell line)</li> <li>SNCA-3x alpha Synuclein triplication Parkinson's Disease patient-derived iPSC line</li> <li>SNCA-corr (SNCA-3x isogenic control with the triplication mutation corrected)</li> </ol> <p>The all-celltypes.h5ad refers to the object shown at Fig 4 onwards and contains the 3 cell lines that are included in model-dataset.h5ad and 7 other single-cell RNAseq samples:</p> <ol> <li>SNCA-3x+KOLF2 (chimera organoid condition of SNCA-3x and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>SNCA-corr+KOLF2 (chimera organoid condition of SNCA-corr and KOLF2 of iPSCs grown together in a midbrain organoid)</li> <li>5 paired rotenone conditions. For each condition (KOLF2, SNCA-3x, SNCA-corr, SNCA-3x+KOLF2, SNCA-corr+KOLF2) a paired condition with acute 24h rotenone treatment in an antioxidant-free medium.</li> </ol>
Multiplexed single-cell characterization of alternative polyadenylation regulators (HEK293FT & K562 Perturb-seq data)
<p>This site provides access to datasets from the CPA-Perturb-seq <a href="https://www.biorxiv.org/content/10.1101/2023.02.09.527751v1">manuscript</a> Kowalski*, Wessels*, Linder* et al., including processed Perturb-seq datasets from HEK293FT and K562. We release these data as Seurat objects, where each object contains single-cell quantifications of gene expression (RNA assay), and in addition, quantifications of polyA site usage (polyA site assay). To explore these data, please install the <a href="https://github.com/satijalab/PASTA">PASTA</a> (PolyA Site analysis using relative Transcript Abundance) package, which provides infrastructure and analytical tools to explore alternative polyadenylation at single-cell resolution. For each dataset, we also include a fragment file which enables visualization of read coverage plots across groups of cells. </p> <p>The files include:</p> <p>1. CPA_K562.Rds : Seurat object containing the K562 CPA-Perturb-seq dataset </p> <p>2. CPA_K562_fragments.tsv.gz : Fragment file for the K562 dataset </p> <p>3. CPA_K562_fragments.tsv.gz.tbi : Fragment file index for the K562 dataset </p> <p> </p> <p>R code below:</p> <pre><code>library(PASTA) k562 <- readRDS("CPA_K562.Rds") # Add fragments for plotting Fragments(k562) <- CreateFragmentObject(path = "download/CPA_K562_blocks.tsv.gz", cells = Cells(k562)) # visualize polyA site usage PolyACoveragePlot(k562, region ="chr7-26212195-26213351")</code></pre>
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 RNA Transcriptomics of Mantle Cell Lymphoma Reveals the Presence of Treatment-Resistant Subclones at the Time of Diagnosis - Supplementary Data
<p><strong>Supplementary Table 4.</strong> Results of optical genomic mapping for patient P009. The table lists genomic aberrations shared for diagnosis and relapse and those exclusive at both timepoints.</p> <p><strong>Supplementary Table 5.</strong> <strong>Sheet A:</strong> Results of differential gene expression analysis between relapse and diagnosis MCL cells of four patients (P009, P022, P027 and P087) performed using Wilcoxon rank-sum test. Only genes with adjusted p-value < 0.01, absolute log2 fold change > 1, and expression in at least 25% of cells in the upregulated group are included. <strong>Sheet B: </strong>Results of GO biological process enrichment analysis performed using clusterProfiler on upregulated and downregulated genes from each patient comparison. Multiple testing correction was performed using the Benjamini-Hochberg method. Only terms with q-value < 0.01 are shown.</p> <p><strong>Supplementary Table 6.</strong> Proportions of cells at four cell cycle phases (G0, G1, S, and G2M) at diagnosis (DG) and their changes after relapse (REL) in four aggressive MCL patients. Statistical analysis was performed using chi-square test.</p> <p><strong>Supplementary Table 7. </strong>Characterization of lymphoma sub-clones detected using CNV inference. <strong>Sheet A: </strong>Cell counts and proportions (prop) relative to total MCL cells in each subclone at diagnosis (DG) and relapse (REL). prop_FC – proportion fold change at relapse compared to diagnosis (calculated as prop_REL/prop_DG for expanding clones, or prop_DG/prop_REL for contracting clones). Subclone labels indicate putative treatment sensitivity: "Sens" denotes subclones that contracted or disappeared at relapse (suggesting chemotherapy sensitivity), while "Res" denotes subclones that expanded at relapse (suggesting chemotherapy resistance). <strong>Sheet B:</strong> CNV profiles of MCL subclones identified through inferCNV analysis. Each row specifies chromosomal coordinates (chr, start, end), copy number state (cn), cytogenetic location (cytobands), and genes within altered regions. Copy number states: cn=1 (deletion), cn=2 (normal), cn=3+ (amplification).</p> <p><strong>Supplementary Table 8.</strong> Differential gene expression analysis between resistant and sensitive MCL subclones. <strong>Sheet A:</strong> Summary of pairwise subclone comparisons. For each comparison between resistant (Res) and sensitive (Sens) subclones, the table reports counts of total, upregulated and downregulated differentially expressed genes and their ratio, enriched Gene Ontology (GO) terms and Hallmark gene sets in each direction. <strong>Sheet B:</strong> Complete list of differentially expressed genes from all pairwise subclone comparisons. Each row represents a single gene with statistical metrics including p-value, adjusted p-value, average log2 fold change (avg_log2FC), expression percentages in each subclone (pct.1, pct.2), regulatory direction (up/down), and gene description. <strong>Sheet C:</strong> Gene Ontology (GO) enrichment analysis results for differentially expressed genes. Only significantly enriched (q-value < 0.05) biological processes are shown with enrichment statistics including gene ratio, background ratio, fold enrichment, z-score, adjusted p-values, and lists of genes contributing to each enriched term. <strong>Sheet D: </strong>Hallmark gene set enrichment analysis (GSEA) results identifying coordinated expression programs differentially active between resistant and sensitive subclones. Results include normalized enrichment scores (NES), statistical significance, leading edge metrics, and core enrichment genes for each pathway.</p> <p><strong>Supplementary Table 9.</strong> Somatic variants in P069 samples. The variants were identified using a consensus approach integrating three variant callers (Mutect2, VarScan2, and Strelka2) and filtered using SomaticSeq with a PASS threshold score of 0.5. The table includes sample identifiers, genomic coordinates (hg38), variant allele frequencies (VAF) and genotypes (GT) for matched normal and tumor samples, sequencing depth (DP), and comprehensive functional annotations generated by Funcotator using GENCODE and HGNC databases. Key annotation fields include gene symbols, variant classifications, transcript information, protein changes, and gene ontology details. Only variants passing consensus filtering criteria with VAF ≥1% for heterozygous and ≥85% for homozygous calls are included.</p> <p><strong>Supplementary Data Object 1. </strong>SingleCellExperiment object with 57751 cells post-QC and 36601 genes. Data columns include sample, patient, timepoint, compartment, cell type annotation (cell_type_manual), cell cycle phase (tricyclePhase), tumor subclones (subclone_label).</p>
A dataset for testing a single-cell Hi-C softwares
Open the record for dataset details and reuse information.
Phospho-seq: Integrated, multi-modal profiling of intracellular protein dynamics in single cells
<p> </p><p>Datasets to go along with the publication listed:</p><p>full_object.rds: Brain Organoid Phospho-Seq dataset with ATAC, Protein and imputed RNA data</p><p>rna_object.rds: Reference whole cell scRNA-Seq object on Brain organoids</p><p>multiome_object.rds: Bridge dataset containing RNA and ATAC modalities for Brain organoids</p><p>metacell_allnorm.rds: Metacell object for finding gene-peak-protein linkages in Brain organoid dataset</p><p>fullobject_fragments.tsv.gz: fragment file to go with the full object</p><p>fullobject_fragments.tsv.gz.tbi:index file for the full object fragment file</p><p>multiome_fragments.tsv.gz: fragment file to go with the multiome object</p><p>multiome_fragments.tsv.gz.tbi:index file for the multiome object fragment file</p><p>K562_Stem.rds : object corresponding to the pilot experiment including K562 cells and iPS cells</p><p>K562_stem_fragments.tsv.gz: fragment file to go with the K562_stem object</p><p>K562_stem_fragments.tsv.gz.tbi: index file for the K562_stem object fragment file</p><p>retina.rds : object corresponding to the retinal organoid phospho-seq experiment</p><p>retina_fragments.tsv.gz: fragment file to go with the retina object</p><p>retina_fragments.tsv.gz.tbi: index file for the retina object fragment file</p><p>retina_multi.rds : object corresponding to the retinal organoid phospho-seq-multiome experiment</p><p>retina_multi_fragments.tsv.gz: fragment file to go with the retina_multi object</p><p>retina_multi_fragments.tsv.gz.tbi: index file for the retina_multi object fragment file</p><p>To use the K562, multiome, retina and retina_multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the K562 and multiome datasets provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file:</p><p>obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p>To use the "fullobject" dataset provided, please use these lines of code to import the object into Signac/Seurat and change the fragment file path to the corresponding downloaded fragment file: </p><p>#load the stringr package library(stringr) #load the object obj <- readRDS("obj.rds") # remove fragment file information Fragments(obj) <- NULL #Remove unwanted residual information and rename cells obj@reductions$norm.adt.pca <- NULL obj@reductions$norm.pca <- NULL obj <- RenameCells(obj, new.names = str_remove(Cells(obj), "atac_")) # Update the path of the fragment file Fragments(obj) <- CreateFragmentObject(path = "download/obj_fragments.tsv.gz", cells = Cells(obj))</p><p> </p><p> </p>
Data from: Mouse gingival single cell transcriptomic atlas identified a novel fibroblast subpopulation activated to guide oral barrier immunity in periodontitis
<p>Periodontitis, one of the most common non-communicable diseases, is characterized by chronic oral inflammation and uncontrolled tooth supporting alveolar bone resorption. Its underlying mechanism to initiate aberrant oral barrier immunity has yet to be delineated. Here, we report a unique fibroblast subpopulation activated to guide oral inflammation (AG fibroblasts) identified in a single-cell RNA sequencing gingival cell atlas constructed from the mouse periodontitis models. AG fibroblasts localized beneath the gingival epithelium and in the cervical periodontal ligament responded to the ligature placement and to the discrete topical application of Toll-like receptor stimulants to mouse maxillary tissue. The upregulated chemokines and ligands of AG fibroblasts linked to the putative receptors of neutrophils in the early stages of periodontitis. In the established chronic inflammation, neutrophils together with AG fibroblasts appeared to induce type 3 innate lymphoid cells (ILC3s) that were the primary source of interleukin-17 cytokines. The comparative analysis of <em>Rag2-/-</em> and <em>Rag2gc-/-</em> mice suggested that ILC3 contributed to the cervical alveolar bone resorption interfacing the gingival inflammation. We propose that the AG fibroblast–neutrophil–ILC3 axis as a previously unrecognized mechanism which could be involved in the complex interplay between oral barrier immune cells contributing to pathological inflammation in periodontitis.</p>
ChromoPhyloGen: characterizing copy number alteration patterns in heterogeneous tumor cell populations at Single-Cell Resolution
<p>The human liver cancer cell line Huh7 was obtained from the American Type Culture Collection (ATCC). Huh7 cells were cultivated in Dulbecco's Modified Eagle Medium (DMEM, Gibco, C11995), supplemented with 1% penicillin/streptomycin (Gibco, 15140122), and 10% fetal bovine serum (FBS, Excell, FSP500). Huh7 cell line was maintained under a 95% O2 and 5% CO2 humidified atmosphere in an incubator at 37˚C. </p> <p>The scDNA-seq library was performed using the Chromium Single cell DNA Library & Gel Bead kit (10x Genomics, PN1000040) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip C and D (10x Genomics, 1000022 and 1000042, respectively) according to the manufacturer's user guide and subsequently run on a thermocycler. The barcoded libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p>The scRNA-seq library was generated using the 10x Genomics Chromium Single Cell 3' & Gel Bead Kit v3 (10x Genomics, PN100075) in combination with the Chromium instrument. The samples were processed on Chromium Single cell Chip B (10x Genomics,1000154) according to the manufacturer's protocol and subsequently run on a thermocycler. The 3' gene expression libraries were sequenced using the Novaseq 6000 300 cycle high-output flow cells.</p> <p> </p>
Single-Cell RNA-Sequencing Reveals Placental Response under Environmental Stress
<p>This repository provides scRNA-seq data corresponding to the manuscript "Single-Cell RNA-Sequencing Reveals Placental Response under Environmental Stress" by Van Buren, Azzara, Rangel-Moreno, de la Luz Garcia-Hernandez, Murphy, Cohen, Lin, and Park. The repository includes both count by gene matrices output from CellRanger version 6.0.1 (file names *_filtered_feature_matrix.h5 for each of the eight samples Control_1_M, Control_1_F, Control_2_M, Control_2_F, As_1_M, As_1_F, As_2_M, As_2_F), and a finalized Seurat object including cell type assignments as used for analyses in the manuscript (file name final_Seurat_obj.RData). Accompanying code used in analysis can be found at https://github.com/edvanburen/placenta_code.</p>
Gleaning Euglenozoa-specific DNA polymerases in public single-cell transcriptome data
<p><span>Multiple genes encoding family A DNA polymerases (famA DNAPs), which are evolutionary relatives of DNA polymerase </span><span>I</span><span> (Pol</span><span>I</span><span>) in bacteria and phages, have been found in eukaryotic genomes, and many of these proteins are used mainly in organelles. Among members of the phylum Euglenozoa, distinct types of famA DNAP, Pol</span><span>I</span><span>A, Pol</span><span>I</span><span>BCD+, POP, and eugPolA, have been found. It is intriguing how the suite of famA DNAPs had been established during the evolution of Euglenozoa, but the DNAP data have not been sampled from the taxa that sufficiently represent the diversity of this phylum. In particular, little sequence data were available for basal branching species in Euglenozoa until recently. Thanks to the single-cell transcriptome data from symbiontids and phagotrophic euglenids, we have an opportunity to cover the "hole" in the repertory of famA DNAPs in the deep branches in Euglenozoa. The current study identified 16 new famA DNAP sequences in the transcriptome data from 33 phagotrophic euglenids and two symbiontids, respectively. Based on the new famA DNAP sequences, the updated diversity and evolution of famA DNAPs in Euglenozoa are discussed.</span></p>
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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.