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1,921 results for “single cell analysis”
Single cell analysis of human mesenchymal stem cells
<p>This dataset including the RDS files, including meta data, labeled assays, and reduction map information in order to ensure the reproducibility of the scRNA-seq data of human MSCs.</p> <p>The research "Multi-omics analysis of human mesenchymal stem cells shows cell aging that alters immunomodulatory activity through the downregulation of PD-L1" has been published on Nature Communications (https://doi.org/10.1038/s41467-023-39958-5).</p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Data from: Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight
<p>3-dimensional super-resolution microscopy volumes of human PBMCs recorded on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope.</p> <p>Sample preparation and image capture:</p> <p>Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1:1 mixing the cell suspensions with 2× concentrated fixative (10% Sucrose (w/v) 120 mM KCl, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 °C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# ab176753, Abcam plc., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3 × 10<sup>5</sup> cells per well density in glass-bottom 96-well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1:15,000 (w/v)) for 16 hours in a 37 °C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 × <em>g</em> and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 µL of antifade reagent (Vector Prolong Gold (ThermoFisher)). Samples were imaged immediately after this procedure on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24-40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super-resolution volumes of (358 × 358 × 70 pixels, 0.035 × 0.035 × 0.13 µm/voxel resolution) were recorded in the above-determined positions using Definite Focus autofocusing. A Plan-Apochromat 63 × 1.40 Oil lens, Airyscan2 SR (super-resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings.</p> <p>File naming:</p> <p>Four zip files were deposited named as <Donor#id>.zip, where id goes from 1 to 4.</p> <p>Each zip file contains the following Zeiss Microscopy format image files: <Condition>_<Donor#id>_<Stain#batch>_<Cell>.czi</p> <p><Condition>:</p> <ul> <li>1G – Control culturing in 6-well plates for 25h</li> <li>uG – simulated microgravity culturing for 25h in NASA Rotating Wall Vessels</li> <li>1G+TLR – as above, with TLR 7/8 agonist (1 μM R848)</li> <li>uG+TLR– as above, with TLR 7/8 agonist (1 μM R848)</li> <li>1G+CyD– as above, with cytochalasin D</li> <li>uG+CyD– as above, with cytochalasin D</li> <li>1G+Q – as above, with quercetin 50µM</li> <li>uG+Q – as above, with quercetin 50µM</li> </ul> <p><Donor#id>: 1-4 indicates biological replicates</p> <p><Stain#batch>: 1-2 indicates experimental replicates of phalloidin staining and imaging session</p> <p><Cell>: arbitrary number to distinguish images within the same condition/donor/stain set.</p> <p>See image analysis pipelines used with these data at: https://github.com/gerencserlab/Superresolution-actin-and-mitochondria-analysis</p>
Single cell multiomic analysis identifies key genes differentially expressed in innate lymphoid cells from COVID-19 patients
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Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data
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Single-cell multi-modal analysis of tumor microenvironment in human non-small cell lung cancer tissues
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Data from: Single cell RNA-seq analysis reveals that prenatal arsenic exposure results in long-term, adverse effects on immune gene expression in response to Influenza A infection
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Data from: Single-cell transcriptomic analysis of tumor-derived fibroblasts and normal tissue-resident fibroblasts reveals fibroblast heterogeneity in breast cancer
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Data from: Subsets of tissue CD4 T cells display different susceptibilities to HIV infection and death: Analysis by CyTOF and single cell RNA-seq
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Single-cell Transcriptomic Analysis Identifies Extensive Heterogeneity in the Cellular Composition of Mouse Achilles Tendons
<p>Tendon is a dense connective tissue that stores and transmits forces between muscles and bones. Cellular heterogeneity is increasingly recognized as an important factor in the biological basis of tissue homeostasis and disease, yet little is known about the diversity of cell types that populate tendon. To address this, we determined the heterogeneity of cell populations within mouse Achilles tendons using single-cell RNA sequencing. In assembling a transcriptomic atlas of Achilles tendons, we identified 11 distinct types of cells, including 3 previously undescribed populations of tendon fibroblasts. This table contains differential gene expression for specific genes identified in distinct populations of cells within tendon tissue. </p>
Single-cell Roadmap dataset "Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment" (Snabel et al.)
<p>View the temporal single-cell transcriptomics data (UMAP, PCA, Heatmaps and Violin plots) using the Shiny App interface of iSEE (<a href="https://doi.org/10.12688/f1000research.14966.1">doi:10.12688/f1000research.14966.1</a>) for easy visualization of the single-cell data described in "Single-cell roadmap of cardiac differentiation identifies roles for ZNF711 and retinoic acid in balanced epicardial and cardiomyocyte lineage commitment" (Snabel et al., bioRXiv).</p> <p>For instructions on how to use this data, please visit https://github.com/Rebecza/scRoadmap_CardiacDiffs/.</p>
Material for Single-cell RNA analysis (I) BIO463 Week 9
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Teaching Datasets for Single-Cell RNA-seq Analysis Course
<p>This repository contains teaching datasets used in the Single-Cell RNA-seq Analysis Course, which is part of the SeuratExtend project (<a href="https://github.com/huayc09/SeuratExtend">https://github.com/huayc09/SeuratExtend</a>). The course materials are available at <a href="https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1">https://huayc09.github.io/SeuratExtend/#single-cell-rna-seq-analysis-course-new-in-v110-1</a>, with code and scripts hosted at <a href="https://github.com/huayc09/single-cell-course">https://github.com/huayc09/single-cell-course</a>.</p> <p>The datasets include:</p> <ol> <li>3k Peripheral Blood Mononuclear Cells (PBMCs)</li> <li>Paired PBMC samples processed with 10x Genomics' 3' kit</li> <li>Paired PBMC samples processed with 10x Genomics' 5' kit</li> </ol> <p>All original data were obtained from 10x Genomics (<a href="https://www.10xgenomics.com/resources/datasets">https://www.10xgenomics.com/resources/datasets</a>) and processed for educational purposes.</p>
Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions
<p><em><strong>Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions</strong></em></p> <p> </p> <p>Tools and scripts were used to generate results in Zhang et al 2024.</p> <p>Supplementary files that were not included in the initial submission.</p> <p>Full summary statistics of eQTLs including top eQTLs and all SNP-gene pairs of each cell type, consistent.tar.gz for consistent eQTLs per cell and response.tar.gz for response eQTLs, respectively.</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
<p>Single <span>cell RNA sequencing (scRNAseq) is a powerful technique that continues to expand across various biological applications. However, incomplete 3' UTR annotations can impede single cell analysis resulting in genes that are partially or completely uncounted. Performing scRNAseq with incomplete 3' UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single cell isoform sequencing (ScISOr-Seq) in tandem with scRNAseq can rapidly improve 3' UTR annotations. Using threespine stickleback fish (</span><em>Gasterosteus aculeatus</em><span>), we show that gene models resulting from a minimal embryonic ScISOr-Seq dataset retained 26.1% greater scRNAseq reads than gene models from Ensembl alone. Furthermore, pooling our ScISOr-Seq isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the ScISOr-Seq only dataset. In addition, isoforms identified by ScISOr-Seq included thousands of new splicing variants. The improved gene models obtained using ScISOr-Seq lead to successful identification of cell types and increased the reads identified of many genes in our scRNAseq stickleback dataset. Our work illuminates ScISOr-Seq as a cost-effective and efficient mechanism to rapidly annotate genomes for scRNAseq.</span></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>
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>
scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis
<p>Analysis code for the paper "scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis"</p>
Seurat objects for "Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state" (https://doi.org/10.1038/s41467-023-42762-w)
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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.