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111,247 results for “cells”
Fully annotated Human Breast carcinoma cells for 3D segmentation training
<p>Fully annotated dataset for training of 3D segmentation models. We provide the Raw image patches in the Raw directory and instance segmentation labels in the RealMask directory, the semantic segmentation masks are provided in the BinaryMask directory. Manually curated from originally published dataset over here: http://celltrackingchallenge.net/3d-datasets/ by team at Kapoorlabs.</p>
GRAND-SLAM analysis of HCMV infection of ZAP knockout cells from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20)
<p>This is the processed SLAM-seq data from Gonzalez-Perez et al., mBio 2021 (https://doi.org/10.1128/mbio.02683-20). The zip file contains the full output from the processing pipeline (including the mapped reads, the scripts to run the pipeline and the output). The json file is required if you want to start from scratch. The file sars.tsv.gz is the GRAND-SLAM output table.</p> <p><br> To generate the GRAND-SLAM output yourself, first prepare the human (ensembl v90) and the HCMV genome (EF999921). Then run:</p> <p>gedi -e Slam -double -reads sars.cit -genomic h.ens90 HCMV_TB40_BAC4 -prefix grandslam_du/zap_bac4 -plot -progress</p> <p>[Zum Verschieben anwählen und ziehen]</p> <p>To generate the cit file you have to modify the first lines in start.bash to match the paths on your file system, and then run it.</p> <p>You can also start from scratch (i.e., the json file):</p> <p> Prepare the human genome (ensembl v90), the HCMV genome (EF999921), the human rRNA sequence (U13369.1), and the Mycoplasma hominis sequence<br> Prepare the joint STAR index for the human and virus genome by calling gedi -e GenomicUtils -p -m star -g h.ens90 HCMV_TB40_BAC4<br> Modify the starindex entry in the json file to match your file system<br> Run: gedi -e Pipeline -r parallel -j zap_bac4.json rnaseq_mapping.sh report.sh grandslam.sh</p> <p>Software versions:</p> <p> gedi toolkit 1.0.2<br> GRAND-SLAM 2.0.5a<br> Trimmomatic 0.39<br> STAR version 2.5.3a</p>
Processing single-cell RNA-seq datasets using SingCellaR
<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata: The SingCellaR objects generated in Step 4 on a local computer </li> <li>ABM_1.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>eFL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>FL_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>PBM_All.SingCellaR.rdata: The SingCellaR objects generated in Step 3 on a local computer </li> <li>meta.data.txt: meta data includes the donor and batch information </li> </ul>
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking
<p>Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.</p> <p><em>Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.</em></p> <p>Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a></p> <p><strong>This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:</strong></p> <p><a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a></p>
Immune disease risk variants regulate gene expression dynamics during CD4+ T cell activation
<p>During activation, T cells undergo extensive changes in gene expression which shape the properties of cells to exert their effector function. Therefore, understanding the genetic regulation of gene expression during T cell activation provides essential insights into how genetic variants influence the response to infections and immune diseases. We generated a single-cell map of expression quantitative trait loci (eQTL) across a T cell activation time-course. We profiled 655,349 CD4+ naive and memory T cells, capturing transcriptional states of unstimulated cells and three time points of cell activation in 119 healthy individuals. We identified 38 cell clusters, including stable clusters such as central and effector memory T cells and transient clusters that were only present at individual time points of activation, such as interferon-responding cells. We mapped eQTLs using a T cell activation trajectory and identified 6,407 eQTL genes, of which a third (2,265 genes) were dynamically regulated during T cell activation. We integrated this information with GWAS variants for immune-mediated diseases and observed 127 colocalizations, with significant enrichment in dynamic eQTLs. Immune disease loci colocalized with genes that are involved in the regulation of T cell activation, and genes with similar functions tended to be perturbed in the same direction by disease risk alleles. Our results emphasize the importance of mapping context-specific gene expression regulation, provide insights into the mechanisms of genetic susceptibility of immune diseases, and help prioritize new therapeutic targets.</p> <p>This dataset comprises of summary stats for eQTLs identified in the study (parquet files) and the ones which passed significance threshold (tensor_out.tar.gz archive). Files are described by cell subset (CD4 Naive, CD 4 Memory, TEMRA, TCM, etc.), time since activation (16h, 4h, 5days) as described in the publication (preprint https://doi.org/10.1101/2021.12.06.470953)</p>
Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells
<p>Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.</p>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Single-Cell Transcriptomic Atlas of Human Cardiac Arteries
<p>This dataset contains the processed single-cell RNA sequencing data and code in article "Single-Cell Transcriptomic Atlas of Different Human Cardiac Arteries Identifies Cell Types Associated With Vascular Physiology". Please refer to the article for the detailed sample information, sequencing and data processing methods.</p> <p>cardiac_arteries_processed_data.zip: Processed single-cell RNA sequencing data from Cellranger output.</p> <p>merged_all_samples: The output of Cellranger aggr, merging processed data of all samples.</p> <p>Abbreviations in samples: AO, aorta; CA, coronary artery; PA, pulmonary artery.</p> <p>notebook.zip: Jupyter notebooks containing code for data analysis.</p>
Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03)
<p>This dataset represents timeseries lables of cell divisions, to train & test a classifier to detect cell-cycle slowdown.<br> It has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below).</p> <p>It is made of 1 file containing :</p> <ul> <li>Groundtruth Input data (Xdata): 250 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> network</li> <li>Groundtruth outputdata (Ydata): 250 timeseries of classes "1. pre-slowdown", "2. post-slowdown", which have been annotated manually.</li> </ul> <p>The indexes from the Xdata correspond to that of the Ydata. Timeseries from 1->200 were used as training while 201->250 were used as validation.</p> <p>It is related to the trained network <a href="https://https://doi.org/10.5281/zenodo.5553829">doi.org/10.5281/zenodo.5553829</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a></p> <p><a href="http://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p> </p> <p><strong>Data type</strong>: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000).</p> <p><strong>File format</strong>: .mat</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p> <p> </p>
Annotated pixels from brightfield images from yeast cell in microfluidic traps - Training and Test sets - DetecDiv (id02)
<p>This dataset has been generated by manual segmentation from timelapse images of yeast cells dividing using the DetecDiv software (see below).</p> <p>It contains ~1950 raw images (folder "images") associated with their mask (folder "labels") with 3 classes of pixels according to their value on the mask image, "1. background", "2. mother", "3. other".</p> <p>A splitSets.mat indicates which images have been used for training or for testing.</p> <p>It is related to the trained network <a href="https://doi.org/10.5281/zenodo.5553851">doi.org/10.5281/zenodo.5553851</a> from the software DetecDiv: github.com/gcharvin/DetecDiv</p> <p><a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a></p> <p>-------------------------------------------------------</p> <p><strong>Data type</strong>: 2D microscopy images (brightfield) (.tif) + mask image (.tif)</p> <p><strong>Microscopy data type</strong>: Brightfield images</p> <p><strong>Imaging</strong>: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS</p> <p><strong>Cell type</strong>: Budding yeast wild type cell (BY4742)</p> <p><strong>File format</strong>: .tif (16-bit RGB, 1 color per z-stack) + .mat</p> <p><strong>Image size</strong>: Brightfield: 60x60x1 (Pixel size: x,y: 325 nm) // Mask: 60x60x1 (Pixel size: x,y: 325 nm), 3 pixel values (one per class).</p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Raw data to "Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model"
<p>Data that resulted from the conduction of the in vitro part of the project: Intravenous lipid emulsions as a treatment in acute opioid poisoning - pharmacokinetic and pharmacodynamic evaluation in the rabbit model. It served as raw data for the publication Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model (draft title). </p>
Data accompanying MetaChrom and "Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning"
<p>This is the data accompanying the paper " Annotating functional effects of non-coding variants in neuropsychiatric cell types by Deep Transfer Learning" and the GitHub repository https://github.com/bl-2633/MetaChrom. </p> <p><strong>/data/bed_files/ </strong>contains the unprocessed bed file used in analysis</p> <p><strong>/data/seq_data/</strong> contains processed data from the bed files with corresponding partition and labels for each sequence segment.</p> <p><strong>/trained_model/MetaChrom_model/</strong> contains the pre-trained MetaChrom model on neural developmental context</p> <p><strong>/trained_models/MetaFeat_model/ </strong>contains the MetaFeat model used in training</p> <p><strong>/tool/</strong> contains files and software necessary for the processing pipeline.</p>
Cell recognition in clear cell renal cell carcinoma tissue
<p>This dataset contains a set of 426 PNG images extracted from clear cell renal cell carcinoma samplers of the Cancer Genome Atlas. A JSON file included contains the bounding boxes and classes of each cell found in the PNG images. The label 2 is utilized for lymphocytes and the label 1 for cells other than lymphocytes.</p> <p> </p> <p>The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.</p>
Template dataset for cell simulation
<p>Dataset with templates of actual cells observed through fluorescent microscopy. The zip archive contains:</p> <ul> <li>a library of 3D cell and nucleus shapes, in addition of 2D protrusion areas.</li> <li>precomputed distance maps.</li> <li>an index in a CSV file.</li> </ul> <p> </p> <p>Original dataset comes from https://doi.org/10.5281/zenodo.1413489</p> <p>Template dataset initially used by https://github.com/fish-quant/sim-fish/tree/main</p>
miRNA profiling of human nasopharyngeal carcinoma cell lines HONE1 and CNE2 after X-ray therapy
<p>The package contains two file:</p> <p>Supplementary Table 1. Kruskal Wallis test for cell viability rate</p> <p>Supplementary Table 2. Kruskal Wallis test for apoptosis rate</p> <p>The HONE1 and CNE2 cells were irradiated with X-rays at doses of 4 Gy, 8 Gy, 16 Gy and 20 Gy for 24 h. The cell viability rate and apoptosis rate were compared.</p>
Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors - Scripts & Source data
<p>Source data and scripts to reproduce the figures that are part of the publication "Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors".</p> <p>Journal of Cell Science (2022) 135, jcs259685, DOI: 10.1242/jcs.259685</p> <p> </p> <p>An earlier version of this work is published as a preprint: "Heterogeneity and dynamics of ERK and Akt activation by G protein-coupled receptors depend on the activated heterotrimeric G proteins", DOI: <a href="https://doi.org/10.1101/2021.07.27.453948">10.1101/2021.07.27.453948</a></p>
Raw Data for the article: A Radioactive-Free Method for the Thorough Analysis of the Kinetics of Cell Cytotoxicity
<p>The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium (<sup>51</sup>Cr), a radioactive substance dangerous to the operator and expensive to handle and dismiss. The accuracy of the measurements depends on how well the target cells incorporate <sup>51</sup>Cr during labelling which, in turn, depends on cellular division. Due to bystander metabolism, the target cells spontaneously release <sup>51</sup>Cr, producing a high background noise. Alternative radioactive-free methods have been developed. Here, we compare a bioluminescence (BLI)-based and a carboxyfluorescein succinimidyl ester (CFSE)-based cytotoxicity assay to the standard radioactive CRA. In the first assay, the target cells stably express the enzyme luciferase, and vitality is measured by photon emission upon the addition of the substrate d-luciferin. In the second one, the target cells are labelled with CFSE, and the signal is detected by Flow Cytometry. We used these two protocols to measure cytotoxicity induced by treatment with NK cells. The cytotoxicity of NK cells was determined by adding increasing doses of human NK cells. The results obtained with the BLI method were consistent with those obtained with the CRA- or CFSE-based assays 4 hours after adding the NK cells. Most importantly, with the BLI assay, the kinetic of NK cells' killing was thoroughly traced with multiple time point measurements, in contrast with the single time point measurement the other two methods allow, which unveiled additional information on NK cell killing pathways.</p>
SARS-CoV-2 antigen exposure history shapes phenotypes and specificity of memory CD8 T cells
<p>This dataset contains aggregated CellRanger output for six 10x Genomics (5'GEX+abTCR+Feature barcoding) experiments from the study by Minervina, Pogorelyy et al (<a href="https://www.medrxiv.org/content/10.1101/2021.07.12.21260227v3">medrxiv</a>). <br> The scripts to process it further are available at github (<a href="https://github.com/pogorely/COVID_vax_CD8">repository</a>). <br> Raw sequencing data is available at SRA (acc. PRJNA744851)</p>
Effect of heterogeneous substrate adhesivity of follower cells on speed and tension profile of leader cells in primary keratocyte collective cell migration
<p><span>In single keratocyte motility, membrane tension is reported to be high at cell-fronts and believed to establish front coherence. To understand role of membrane mechanics in collective cell migration, we study membrane height fluctuations in cell sheets from fish scales using interference reflection microscopy (IRM). We report the monolayer to have cells lacking substrate adhesion and show that such "non-sticky" cells can form bridges between leader cells and far-away follower cells. Do such interactions alter motility and membrane mechanics in such leaders? We find non-significant, but reduced speed for leaders with "non-sticky" followers in comparison to other leaders. Cells show high phenotypic variability in their membrane fluctuation tension profiles. On average, this tension is found to be lower at cell fronts than the mid-section. However, leaders with non-sticky followers are more prone to display higher tension at their front and have a negative correlation between cell speed and front-mid tension difference. We, thus, conclude that intracellular tension gradients are heterogeneous in cell sheets and substrate adhesivity of followers can control the coupling of the gradient to cell speed.</span></p>
Supplementary materials of Brain Extract of Subacute Traumatic Brain Injury Promotes Neuronal Differentiation of Human Neural Stem Cells Via Autophagy
<p>Figure S1: Characterization of hNSCs. In proliferate medium, the embryo derived hNSCs could form the neurospheres (A). Markers of NSCs (NES, SOX2, SOX1) were detected (B). The SOX2<sup>+</sup> cells accounted for >95% of all cells (C). Bar scale: 60μm;</p> <p>Figure S2: The proliferation of hNSCs in TBI brain extracts with different phases. Differentiated hNSCs could be labeled with EdU in TBI brain extracts with different phases (A). Ratio of EdU<sup>+</sup> cells was decreased with the progression of days post-injury (B). Bar scale: 75μm; ****: P<0.0001;</p> <p>Figure S3: The GO annotations and KEGG pathway analysis of different expressed pro-teins in acute brain extract and subacute brain extract;</p> <p>Table S1: The protein profiles of brain extract in acute phase and subacute phase after TBI.</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.