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11,105 results for “single cells”
Imaging Mass Cytometry of human normal colon mucosa (CLN1-6) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Four µm-thick sections were cut from each block of samples CLN1-CLN6 with a microtome and used for staining with a panel of 26 antibodies targeting the main immune, stromal and epithelial cell populations of the gastrointestinal tract (Supplementary Table 2). The optimal dilution of each antibody in the panel was identified by staining and ablating FFPE appendix sections. The resulting images were reviewed by a mucosal immunologist (J.S.) and the dilution giving the best signal to background ratio was selected for each antibody (Supplementary Table 2). To perform the staining for IMC, slides were dewaxed after a one-hour incubation at 60°C, rehydrated and heat-induced antigen retrieval was performed with a pressure cooker in Antigen Retrieval Reagent-Basic (R&D Systems). Slides were incubated in a 10% BSA (Sigma), 0.1% Tween (Sigma), and 2% Kiovig (Shire Pharmaceuticals) Superblock Blocking Buffer (Thermo Fisher) blocking solution at room temperature for two hours. Each antibody was added to a primary antibody mix at the selected concentration in blocking solution and incubated overnight at 4°C. After two washes in PBS and PBS-0.1% Tween, the slides were treated with the DNA intercalator Cell-ID™ Intercalator-Ir (Fluidigm) (containing the two iridium isotopes 191Ir and 193Ir) 1.25 mM in a PBS solution. After a 30-minute incubation, the slides were washed once in PBS and once in MilliQ water and air-dried. The stained slides were then loaded in the Hyperion Imaging System (Fluidigm) imaging module to obtain light-contrast high resolution images of approximately four mm<sup>2</sup>. These images were used to select the ROI in each slide. For CLN1-CLN6, 1 mm<sup>2 </sup>ROIs were selected to contain the full thickness of the colon mucosa, with epithelial crypts in longitudinal orientation. ROIs were ablated at a o µm/pixel resolution and 200 Hz frequency.</p> <p>Twenty-eight images from 26 antibodies (Supplementary Table 2) and two DNA intercalators were obtained from the raw .txt files of the ablated regions in CLN1-CLN6 using the data extraction process.</p>
Single-cell analysis of patient-derived PDAC organoids reveals cell state heterogeneity and a conserved developmental hierarchy
<p>scRNA-seq read count matrices from patient-derived PDAC organoids.</p>
Mouse Kidney Atlas (MKA) & MKA-Extended: Single-cell and single-nucleus reference atlases of the mouse kidney
<p>This deposit contains two linked datasets of the mouse kidney:<br><br></p> <h4><strong>1. MKA (Mouse Kidney Atlas)</strong></h4> <p>A comprehensive reference atlas of the healthy adult mouse kidney, generated by integrating approximately <strong>140,000 cells and nuclei</strong> from <strong>59 publicly available single-cell and single-nucleus RNA-sequencing datasets</strong> across <strong>eight independent studies</strong>.<br>The MKA harmonizes annotations through a <strong>hierarchical cell-type model</strong>, enabling robust cross-study comparisons and accurate labeling of novel or rare cell populations.</p> <ul> <li> <p><strong>Original publication:</strong> Novella-Rausell <em>et al.</em>, <em>iScience</em> (2023). <em>“A comprehensive mouse kidney atlas enables rare cell population characterization and robust marker discovery.” </em>DOI: <a target="_new" rel="noopener">10.1016/j.isci.2023.106877</a></p> </li> <li> <p><strong>Format:</strong> <code>AnnData</code> object (~141,401 observations × 3,000 genes)</p> </li> </ul> <h4><strong>2. MKA-Extended</strong></h4> <p>An expanded version of the MKA. This extended atlas incorporates <strong>immune, injury-induced, and polycystic kidney disease–specific</strong> cellular populations, providing a unified reference for <strong>Tangram-based deconvolution</strong> of spatial transcriptomics datasets.</p> <ul> <li><strong>Original publication: </strong>Yasinoglu <em>et al.</em>, <em>JASN</em>, (2025). <em>“Spatial Transcriptomics Reveals Injured Cells, Signature Genes, and Communication Patterns in the Cyst Microenvironment of Polycystic Kidney Disease.” </em>DOI: 10.1681/ASN.0000000894</li> <li> <p><strong>Format:</strong> <code>AnnData</code> object (~303,791 observations × 12,156 genes)</p> </li> </ul>
Processed Single-cell RNA-seq Data for Exercise Rejuvenation Intervention on Mouse Subventricular Zone
<p>Exercise single-cell RNA-seq data of mouse subventricular zone (in processed Seurat object) used in the publication "Cell type-specific aging clocks to quantify aging and rejuvenation in regenerative regions of the brain" (preprint available at <a href="https://doi.org/10.1101/2022.01.10.475747">https://doi.org/10.1101/2022.01.10.475747</a>).</p>
Optimization used in in the publication scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data
<p>OptimizationResults contain the expanded input model for each data set, in the C field are the indices of the core reactions in the expanded input model used to build the multi-cell population for the 121 different parameters, A contains the indices of reaction in the multi-cell population model for the 121 runs, thresh the parameter setting for the cover and REI. The REI is given in %. So 5 REI means 0.05. While A might change because of alternative optimals when run on a different computer, C and the expanded Input model will remain unchanged.</p> <p> </p> <p>Multi_cell_population_CRC and Multi_cell_population_NM are the models obtained by scFASTCORMICS with the optimal setting for Dataset1 and Dataset2, respectively. </p> <p>Please, check the publication (scFASTCORMICS: A contextualization algorithm to reconstruct metabolic multi-cell population models from single-cell RNAseq data) for more details.</p>
Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data
<p>This repository contains the real and synthetic datasets used in the paper "Benchmarking the Autoencoder Design for Imputing Single-Cell RNA Sequencing Data". The zip file includes three folders:</p> <p>1. overall imputation accuracy: the 12 real scRNA-seq datasets used in the evaluation of overall imputation accuracy.</p> <p>2. cell clustering: the 20 real scRNA-seq datasets with cell type labels used in the evaluation of cell clustering.</p> <p>3. DE gene: the 20 scRNA-seq syntehtic datasets with ground-truth DE genes used in the evaluation of DE gene analysis. These datasets are simulated by simulator scDesign and 20 real datasets. </p> <p> </p>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation
<p>Raw microscopy images underlying the publication "Single-cell and spatial transcriptomics reveal aberrant lymphoid developmental programs driving granuloma formation"</p>
Single-cell heterogeneity of EGFR and CKD4 co-amplification is linked to immune infiltration in glioblastoma
<p>This upload contains RDS objects of preprocessed publicly available scRNAseq data, required to run scRNAseq analyses in the manuscript "Single cell heterogeneity of EGFR and CDK4 co-amplification is linked to immune infiltration in glioblastoma". The corresponding GitHub repo <a href="https://github.com/Michorlab/GBM_OR_immune">https://github.com/Michorlab/GBM_OR_immune</a> contains code to analyze the data here, as well as plots and tables generated on the basis of this data.</p>
Single-cell and spatial transcriptomics of cardiac neural crest reveal a dual role of vinculin in Tgf beta signaling and cell-extracellular matrix interaction during cardiac outflow tract development
<p>single-cell RNA-seq data analysis:</p> <p>all.cncc.combined.EMBO.mapped.Rdata: public CNCC single-cell RNA-seq data integration</p> <p>E13.5_CNCC_merged_updated.Rdata: single-cell RNA-seq data of E13.5 CNCC generated in Elly lab</p> <p>all.seurat.GFP.Rdata: scRNA-seq data with GFP detected</p> <p>visium.merge_AB.control.Rdata: R processed ST data for slice A and B</p> <p>visium.merge_CD.mutant.Rdata: R processed ST data for slice A and B</p> <p> </p>
Data for: Single-cell multi-omics in the medicinal plant Catharanthus roseus
<p>Advances in omics technologies now permit the generation of highly contiguous genome assemblies, detection of transcripts and metabolites at the level of single cells and high-resolution determination of gene regulatory features. Here, using a complementary, multi-omics approach, we interrogated the monoterpene indole alkaloid (MIA) biosynthetic pathway in <em>Catharanthus roseus</em>, a source of leading anticancer drugs. We identified clusters of genes involved in MIA biosynthesis on the eight <em>C. roseus</em> chromosomes and extensive gene duplication of MIA pathway genes. Clustering was not limited to the linear genome, and through chromatin interaction data, MIA pathway genes were present within the same topologically associated domain, permitting the identification of a secologanin transporter. Single-cell RNA-sequencing revealed sequential cell-type-specific partitioning of the leaf MIA biosynthetic pathway that, when coupled with a single-cell metabolomics approach, permitted the identification of a reductase that yields the bis-indole alkaloid anhydrovinblastine. We also revealed cell-type-specific expression in the root MIA pathway.</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Single-cell RNA sequencing of Sox17-expressing lineages reveals distinct gene regulatory networks and dynamic developmental trajectories
<p>Two seurat objects contains single-cell RNA sequencing data that captures <em>Sox17</em>-expressing lineages during embryogenesis.</p> <p>sox17_integrated_Figure2B.rds :</p> <p>This is a seurat object that contains single-cell RNA sequencing data from integration of GFP+ cells produced from <em>Sox17<sup>GFPCre</sup></em> allele marking cells that currently express <em>Sox17</em> or short-term progeny of <em>Sox17­-</em>expressing progenitors and TdTomato+ cells produced from <em>R26<sup>LSL.TdTomato</sup></em> reporter allele in the presence of <em>Sox17<sup>GFPCre</sup></em> marking long-term progeny of <em>Sox17</em>-expressing progenitors. Inferred cell types in this seurat object reflects Figure 2B in the article.</p> <p>sox17_Prox1_endoderm_Figure5A.rds :</p> <p>This is a seurat object that contain single-cell RNA sequencing data from integration of <em>Sox17</em>- and <em>Prox1</em>-expressing endoderm dataset. Prox-1 expressing endoderm data is from the Willnow et al. <em>Nature</em>(2021). Inferred cell types in this seurat object reflects Figure 5A in the article.</p>
SMILE: mutual information learning for integration of single-cell omics data
<p>Processed PBMC data for integration tutorial in https://github.com/rpmccordlab/SMILE.</p>
Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis
<p>Codes and single cell data for "<strong>Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis</strong>" paper.</p> <p><strong>File: Tunel_Lyve1_Data.xlsx </strong>– quantifications of Tunel+Lyve1+ cells for n=4 NECs and n=4 neonatal samples.</p> <p><strong>Folder: Cell_ranger_output</strong></p> <p>Data produced using 10x sequencing.</p> <p>This folder contains all the Cell Ranger output raw files from all 11 subjects used for single cell RNA sequencing (5x Neonatal- S1056, S1127, S1212, S1214, S1082, 6x NECs- S1021, S1074, S1095, S1109, S1155, S1193).</p> <p>The neonatal subjects had two 10X runs (except subject 1082) - one done on CD45 negatively selected small intestinal cells and the other on non-selected small intestinal cells.</p> <p>The NEC subjects had one run each (subject 1074 was sequenced twice) with no selection due to low yield in the selected runs.</p> <p><strong>Folder: Seurat_pipeline</strong></p> <p>Done with Seurat package version 3.2.2</p> <p>This folder contains R scripts for Seurat analysis and script for background (BG) subtraction of the data.</p> <ol> <li>BG_subtraction_before_seurat.R - Script that creates a BG subtracted matrix for each CellRanger output before Seurat analysis.</li> <li>seurat_pipline_all_cells_NEC.R - Script for Seurat analysis for the full atlas.</li> <li>Final_seurat_obj_after_filtration_all_cells.rds – rds file of final Seurat object of all cells after pipeline filtration</li> <li>Files description in “RDS_files_for_seurat_pipeline” folder: <ol> <li>list_UMIs_after_BG_subtruction.rds – rds file containing a list of all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> <li>list_metadata_after_BG_subtruction.rds – rds file containing a list of metadata for all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> </ol> </li> </ol> <p><strong>Folder: Deconvolution_analysis</strong></p> <p>Done on Matlab R2019b</p> <p>This folder contains the script for deconvolution analysis and its input files.</p> <ol> <li>Deconvolution_parsing_NEC_for_zenodo.m - Matlab script to create box plots of deconvolution results and their q- values.</li> <li>Files description: <ol> <li>deconvolution_original8_clusters.mat – structure of deconvolution results. Signature cells were split into the 8 major cell type groups shown in figure 1.</li> <li>cellanneal_split_clust_results.mat – structure of deconvolution results. Signature cells were split into sub groups for each cell type group.</li> </ol> </li> </ol> <p><strong>Folder: Ligand-receptor_interaction_analysis</strong></p> <p>This folder contains matlab and R scripts for ligand-receptor interaction analysis.</p> <ol> <li>run_permutations_for_lig_rec_ratio_tensor_Zenodo.m – main script to run ligand-receptor analysis.</li> <li>calculate_ratio_ligand_receptor.m – function used by the main script.</li> <li>Interactions_visualizations_heatmaps.R – R script for heatmap visualization.</li> <li>Files description in “Mat_files_for_analysis” folder: <ol> <li>all_cells_split_for_interactions.mat – single cell structure of all cells. Cells are split into subgroups for ligand receptor analysis.</li> <li>ramilowsky.mat – ligand-receptor lists taken from Ramilowski et al. 2015 to use in main script and function.</li> <li>tensor_real_q100permutations.mat – output mat file of the interactions ratio, q values and ligand-receptor names for heatmaps visualization in R (input file for “Interactions_visualizations_heatmaps.R”).</li> </ol> </li> </ol>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. </p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chrY.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p>
Datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis
<p>20 benchmark scRNA-seq datasets used in Consensus Clustering Problem in Single-cell Transcriptome Data Analysis. In every datasets .zip files, it provided raw data files, the processed R code and the corresponding R objects. The datasets.xlsx file provided the detailed information of 20 datasets.</p>
A molecular cell atlas of the human lung from single cell RNA sequencing
<p>https://cellxgene.cziscience.com/collections/5d445965-6f1a-4b68-ba3a-b8f765155d3a</p> <p>https://www.nature.com/articles/s41586-020-2922-4</p>
Single-nucleus RNA-sequencing reveals oligodendrocytes and their progenitors as vulnerable cell types in prefrontal cortex and anterior cingulate of brains with Parkinson's disease
<p>Several prior studies have proposed the involvement of various brain regions and cell types in Parkinson's disease (PD) pathology. Here, we performed snRNA-seq on the prefrontal cortex and anterior cingulate regions from post-mortem control and PD brain tissue. We found a dramatic association of oligodendrocytes and oligodendrocyte precursor cells with PD-linked risk loci and reported several dysregulated genes and pathways, including regulation of tau-protein kinase activity, regulation of inclusion body assembly and protein processing involved in protein targeting to mitochondria.</p>
Single-Cell RNA-Seq Identifies Pathways and Genes Contributing to the Hyperandrogenemia Associated with Polycystic Ovary Syndrome
<p>Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by hyperandrogenemia of ovarian thecal cell origin, resulting in anovulation/oligo-ovulation and infertility. Our previous studies established that ovarian theca cells isolated and propagated from ovaries of normal ovulatory women and women with PCOS, have distinctive molecular and cellular signatures that underlie the increased androgen biosynthesis in PCOS. To evaluate differences between gene expression in single cells from passaged cultures of theca cells from ovaries of normal ovulatory women and women with PCOS, we performed single-cell RNA sequencing (scRNA-seq). Results from these studies revealed differentially expressed pathways and genes involved in the acquisition of cholesterol, the precursor of steroid hormones, and steroidogenesis. Bulk RNA-seq and microarray studies confirmed the theca cell differential gene expression profiles. The expression profiles appear to be directed largely by increased levels or activity of the transcription factors SREBF1, which regulates genes involved in cholesterol acquisition (<em>LDLR, LIPA, NPC1, CYP11A1, FDX1, FDXR)</em> and GATA6, which regulates expression of genes encoding steroidogenic enzymes (<em>CYP17A1) </em>in concert with other differentially expressed transcription factors (<em>SP1</em>, <em>NR5A2</em>). This study provides insights into the molecular mechanisms underlying the hyperandrogenemia associated with PCOS, and highlights potential targets for molecular diagnosis and therapeutic intervention.</p> <p><strong>scRNA-seq data in the form of 10x Cell Ranger files are available for the following samples:</strong></p> <p>PCOS affected - Mc03, Mc10, Mc16, Mc26, Mc27</p> <p>Normal cycling women - Mc02, Mc06, Mc31, Mc40, Mc50</p> <p>A F in the sample name indicates forskolin treatment and a C indicates untreated control samples.</p> <p> </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.