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6,040 results for “Single-Cell”
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>
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>
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>
A dataset for testing a single-cell Hi-C softwares
Open the record for dataset details and reuse information.
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>
Data from: Effect of stretching on inflammation in a subcutaneous carrageenan mouse model analyzed at single-cell resolution
<p>Understanding the factors that influence the biological response to inflammation is crucial, due to its involvement in physiological and pathological processes, including tissue repair/healing, cancer, infections, and autoimmune diseases. We have previously demonstrated that in vivo stretching can reduce inflammation and increase local pro-resolving lipid mediators in rats, suggesting a direct mechanical effect on inflammation resolution. Here, we aimed to explore further the effects of stretching at the cellular/molecular level in a mouse subcutaneous carrageenan-inflammation model. Stretching for 10 minutes twice a day reduced inflammation, increased the production of pro-resolving mediator pathway intermediate 17-HDHA at 48h post carrageenan injection, and decreased both pro-resolving and pro-inflammatory mediators (e.g., PGE<sub>2</sub> and PGD<sub>2</sub>) at 96h. ScRNAseq analysis of inflammatory lesions at 96h showed that stretching increased the expression of both pro-inflammatory (<em>Nos2</em>) and pro-resolution (<em>Arg1</em>) genes in M1 and M2 macrophages at 96 hours. An intercellular communication analysis predicted specific ligand-receptor interactions orchestrated by neutrophils and M2a macrophages, suggesting a continuous neutrophil presence recruiting immune cells such as activated macrophages to contain the antigen while promoting resolution and preserving tissue homeostasis.</p>
Oxygen concentration profiles data and code from: Langet et al., 2023, Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment
<p>This archive contains the data of measured oxygen profile and the scripts used to generate the oxygen surface fluxes for the article</p><p>Langlet, D., Mermillod-Blondin, F., Deldicq, N., Bauville, A., Duong, G., Konecny, L., Hugoni, M., Denis, L., and Bouchet, V. M. P.: Single-celled bioturbators: benthic foraminifera mediate oxygen penetration and prokaryotic diversity in intertidal sediment, Biogeosciences, 20, 4875–4891, https://doi.org/10.5194/bg-20-4875-2023, 2023.</p>
Single-cell RNA sequencing reveals dysregulated cellular programs in the inflamed epithelium of Crohn's disease patients.
<p><strong>Crohn’s disease (CD) is a complex inflammatory disorder of incompletely understood molecular aetiology. We generated a large single-cell RNA sequencing dataset from the terminal ileal biopsies of two independent cohorts comprising a total of 50 CD patients and 71 healthy controls. We performed transcriptomic analyses to reveal genes, cell types and mechanisms perturbed in CD, leveraging the power of the two cohorts to confirm our findings and assess replicability. In addition to mapping widespread alterations in cytokine signalling, we provide evidence of pan-epithelial upregulation of MHC class I genes and pathways in CD. Using non-negative matrix factorization we revealed intra- and inter-cellular upregulation of expression programs such as G-protein coupled receptor signalling and interferon signalling, respectively, in CD. We observed an enrichment of CD heritability among marker genes for various activated T cell types and myeloid cells, supporting a causal role for these cell-types in CD aetiology. Comparisons between our discovery and replication cohort revealed significant variation in differential gene-expression replicability across cell types. B, T and myeloid cells showed particularly poor replicability, suggesting caution should be exercised when interpreting unreplicated differential gene-expression result in these cell types. Overall, our results provide a rich resource for identifying cell-type specific biomarkers of Crohn’s disease and identifying genes, cell types and pathways that are causally and replicably associated with disease.</strong></p>
Spotiphy enables single-cell spatial whole transcriptomics across the entire section
<p><span>Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage. Here we present Spotiphy (<u>Spot</u> <u>i</u>mager with <u>p</u>seudo single-cell resolution <u>h</u>istolog<u>y</u>), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimer’s disease (AD) and normal </span><span>mouse brains, </span><span>Spotiphy</span><span> delivers the most precise cellular compositions. For the first time, </span><span>Spotiphy reveals</span><span> novel astrocyte </span><span>regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.</span></p>
Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
<p>Hereditary diseases manifest clinically in certain tissues, however their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in 1,459 hereditary diseases. Overall, we identified 114 cell types affected by 1,140 diseases. We corroborated our findings by literature text-mining and recapitulation in mouse corresponding tissues. Based on these findings, we explored features of disease-affected cell types and cell classes, highlighted cell types affected by mitochondrial diseases and heritable cancers, and identified diseases that perturb intercellular communication. This study expands our understanding of disease mechanisms and cellular vulnerability.</p>
Efficient statistical method for single-cell QTL analysis
<p>This upload contains data objects associated with our paper "Efficient statistical method for single-cell QTL analysis" introducing the SAIGE-QTL method (preprint available soon!).</p>
SeuratExtend Tutorial: Curated Example Datasets for Single-Cell Analysis
<p>This repository contains example datasets specifically curated for the SeuratExtend tutorial, aimed at facilitating advanced analyses and visualization techniques in single-cell genomics. The datasets have been derived from publicly available data obtained from the 10X Genomics website and have undergone careful preprocessing to serve specific tutorial goals.</p> <p>The collection includes the following datasets:</p> <ol> <li> <p><strong>Myeloid Subset from PBMC 10k Dataset:</strong> This subset focuses on myeloid cells extracted from the larger PBMC 10k dataset, showcasing a preprocessed SeuratObject stored as an RDS file. The data serve as a primary example for demonstrating the capabilities of SeuratExtend differentiation trajectory analysis.</p> </li> <li> <p><strong>Velocyto LOOM File of Myeloid Subset from PBMC 10k Dataset:</strong> Accompanying the first dataset, this Velocyto-generated LOOM file represents a subset of the same myeloid cells, focusing on RNA velocity analyses. It provides a dynamic perspective on gene expression changes over time, enriching the tutorial with advanced single-cell transcriptomics insights.</p> </li> <li> <p><strong>SCENIC-Processed PBMC 3k Dataset:</strong> An outcome of running the SCENIC workflow on the PBMC 3k dataset, this LOOM file represents a refined dataset highlighting gene regulation networks. It serves as an advanced example for users interested in exploring gene regulatory mechanisms using SeuratExtend.</p> </li> </ol> <p>Each dataset has been subsetted and processed, making them ideal for users ranging from beginners to advanced researchers in the field of single-cell genomics. The provided data are intended for educational and tutorial purposes, allowing users to gain hands-on experience with real-world single-cell analysis scenarios.</p> <p> </p>
Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'
<p>The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.</p>
Data from: Evaluating single-cell variability in proteasomal decay
<p>Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. <br>This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. <br>Here we estimate the single-cell protein decay rates of two degron GFPs in \textit{Saccharomyces cerevisiae} using time-lapse microscopy. <br>We find substantial cell-to-cell variability in the decay rates of the degron GFPs.<br>We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. <br>We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs.<br>Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. <br>A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.</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.