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111,247 results for “cells”
Granulosa cell transcriptome data from four different time points before and up to 48 hours after PMSG induction to GTH-depend phase in mice
<p>GTH-dependent follicle development begins with small antrum follicles and ends with preovulatory follicles. GTH-dependent follicle development is mainly controlled by gonadotropins, and the development time is 48h. The purpose of this study is to monitor the changes in gene expression of granulosa cells at four different time points during the GTH-dependent phase to increase our understanding of human GTH-dependent follicle development.Granulosa cell mRNA profiles of GTH-depend phase in mice.There are 12 samples represented four time points 0h (n=3), 12h (n=3), 24h (n=3) and 48h (n=3)</p>
Experimental Results of "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms"
<p>This repository contains the experimental results of the article "Bioprinting Cell- and Spheroid-Laden Protein-Engineered Hydrogels as Tissue-on-Chip Platforms" by Duarte Campos, D., Lindsay, C., Roth, J., LeSavage, B., Seymour, A., Krajina, B., Ribeiro, R., Costa, P., Heilshorn, S., published in <em>Front. bioeng. biotechnol. </em><strong>8, 374</strong> (2020). https://doi.org/10.3389/fbioe.2020.00374</p>
Data from: Theta oscillations coincide with sustained hyperpolarization in CA3 pyramidal cells, underlying decreased firing
<p>Brain-state fluctuations modulate membrane potential dynamics of neurons, influencing the functional repertoire of the network. Pyramidal cells (PCs) in hippocampal CA3 are necessary for rapid memory encoding, preferentially occurring during exploratory behavior in the high-arousal theta state. However, the relationship between the membrane potential dynamics of CA3 PCs and theta has not been explored. Here, we characterize the changes in the membrane potential of PCs in relation to theta using electrophysiological recordings in awake mice. During theta, most PCs behave in a stereotypical manner, consistently hyperpolarizing time-locked to the duration of theta. Additionally, PCs display lower membrane potential variance and reduced firing rate. In contrast, during large irregular activity, a low-arousal state, PCs show heterogeneous changes in membrane potential. This suggests coordinated hyperpolarization of PCs during theta, possibly caused by increased inhibition. This could lead to higher signal-to-noise ratio in the small population of PCs active during theta as observed in ensemble recordings.</p>
Data for "Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis"
<p>The files named <code>df_scran.csv</code>, <code>df_seurat.csv</code>, <code>df_zinbwave.csv</code>, <code>df_dca.csv</code>, and <code>df_scvi.csv</code> contain one row per configuration that we ran successfully.</p> <p>The files named <code>DATASET.METHOD.h5ad</code> are encoded with anndata <code>v0.7.0</code> (be careful as they are not readable with previous versions) and contain 100 embeddings each. The embeddings are in the <code>obsm</code> attribute of the object. All the embeddings can be listed with the <code>obsm_keys()</code> method. The name of the embedding contains the parameters used to generate that embedding and are written like that <code>method=zinbwave.dims=10.epsilon=1000.features=300.gene_covariate=0</code>.</p> <p> </p> <p>For questions on this dataset please contact fraimundo@google.com</p>
Data and scripts for SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures
<p>This is the repository of data and scripts for the analysis of the CellminerCDB-SCLC manuscript and website (<a href="https://discover.nci.nih.gov/SclcCellMinerCDB/">https://discover.nci.nih.gov/SclcCellMinerCDB/</a>)</p> <p> </p> <p>CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) integrates 118 patient-derived cell lines with drug sensitivity and genomic datasets, including high resolution methylome and RNAseq data. CellMiner-SCLC provides a new resource for SCLC research for this “recalcitrant cancer”. Of fundamental importance, we demonstrate the reproducibility and stability of the cell line datasets from different institutions (CCLE, GDSC, CTRP, NCI and UTSW). We validate the classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 and show transcription networks connecting them with the MYC genes (MYC, MYCL1 and MYCN) and the NOTCH and HIPPO pathways. We find that the 4 subsets express specific surface markers for antibody-targeted therapies. The YAP1-driven (SCLC-Y) cell lines differ from the other subsets by expressing the NOTCH pathway, epithelial-mesenchymal-transition (EMT) and antigen-presenting machinery (APM) genes, and by responding to mTOR and AKT inhibitors, suggesting the potential of NOTCH modulators, YAP1 inhibitors and immune checkpoint inhibitors for SCLC-Y tumors.</p>
Single cell RNA-seq Data - Dissecting the functional reprogramming of the microenvironment in bone marrow fibrosis at the single cell level
<p>We provide results regarding the bioinformatic analysis of scRNA-seq from distinct bone marrow fibrosis mouse models and human samples.</p> <p> </p> <p>These include:</p> <p>Robjects&Markdown - R markdown and R objects with QC statistics, UMAP and final scRNA-seq data sets.</p> <p>Markers - Excel tables with cluster specific marker genes.</p> <p>DE Genes - Excel table with DE genes when comparing cells in control vs. disease condition per cluster.</p> <p>GO Analysis - Gene enrichment analysis of either DE genes. These are divided by either UP or down regulated genes.</p>
Data and statistical analysis for: Bacterial nanotubes are a manifestation of cell death
<p>Contains all data and code to reproduce the statistical analysis in the Supplementary file 2 for the paper "Bacterial nanotubes are a manifestation of cell death" to be published in Nature Communications.</p> <p><strong>Contents:</strong></p> <ul> <li>statistical_analysis.Rmd is the main document written in R Markdown</li> <li>statistical_analysis.html is a compiled version of statistical_analysis.Rmd showing all the computed results.</li> <li>The Source data.xls file contains raw data used for the analysis - the sheet names indiciate the figure they refer to. See the main file for code that can read the data.</li> </ul> <p>The code can also be accessed at <a href="https://github.com/cas-bioinf/nanotubes-death">https://github.com/cas-bioinf/nanotubes-death</a></p>
Full summary statistics of mixQTL for GTEx v8 Cells_Cultured_fibroblasts
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Full summary statistics of mixQTL for GTEx v8 Cells_EBV-transformed_lymphocytes
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids - Dataset
<p>This is the umbrella archive that contains all the datasets and code associated with:</p> <p><strong>A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids</strong></p> <p> Vito RT Zanotelli<br> Matthias Leutenegger<br> Xiao‐Kang Lun<br> Fanny Georgi<br> Natalie de Souza<br> Bernd Bodenmiller</p> <p><em>Mol Syst Biol. (2020) 16: e9798</em><br> <a href="https://doi.org/10.15252/msb.20209798">https://doi.org/10.15252/msb.20209798</a></p> <p><em>Please cite this article if you re-use any of the data or code.</em></p> <p>Datasets:</p> <ul> <li>Brightfield plate images: Contains all plate acquisitions for the individual sphere plates before pooling <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991929">10.5281/zenodo.3991929</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991931">10.5281/zenodo.3991931</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991925">10.5281/zenodo.3991925</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991927">10.5281/zenodo.3991927</a><br> </li> </ul> </li> </ul> </li> <li>Slidescan images: Contains all fluorescent slidescan images of the cuts derived from the pooled spheroid sample blocks <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991921">10.5281/zenodo.3991921</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991923">10.5281/zenodo.3991923</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.4066430">10.5281/zenodo.4066430</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991919">10.5281/zenodo.3991919</a><br> </li> </ul> </li> </ul> </li> <li>Spillover acquisitions: Contains all spillover acquisitions associated with the two experiments: <ul> <li>4 cellline experiment: <ul> <li>p173/p176: <a href="http://doi.org/10.5281/zenodo.3991945">10.5281/zenodo.3991945</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161/p165: <a href="http://doi.org/10.5281/zenodo.3991947">10.5281/zenodo.3991947</a><br> </li> </ul> </li> </ul> </li> <li>Imaging mass cytometry acquisitions: <ul> <li>4 cellline experiment: <ul> <li>p173: <a href="http://doi.org/10.5281/zenodo.3991937">10.5281/zenodo.3991937</a></li> <li>p176: <a href="http://doi.org/10.5281/zenodo.3991939">10.5281/zenodo.3991939</a></li> </ul> </li> <li>Overexpression experiment: <ul> <li>p161: <a href="http://doi.org/10.5281/zenodo.3991933">10.5281/zenodo.3991933</a></li> <li>p165: <a href="http://doi.org/10.5281/zenodo.3991935">10.5281/zenodo.3991935</a><br> </li> </ul> </li> </ul> </li> <li>Processed data set: <ul> <li>4 cellline experiment: <a href="https://doi.org/10.5281/zenodo.3991942">10.5281/zenodo.3991942</a></li> <li>Overexpression experiment: <a href="http://doi.org/10.5281/zenodo.4271917">10.5281/zenodo.4271917</a></li> </ul> </li> </ul> <p>Code:</p> <p>A snakemake pipeline to reproduce the analyses from this raw data can be found at: <a href="http://github.com/BodenmillerGroup/SpheroidPublication">http://github.com/BodenmillerGroup/SpheroidPublication</a></p> <p>A version already containing all the containers can be found at: <a href="http://doi.org/10.5281/zenodo.4071861">10.5281/zenodo.4071861</a></p> <p> </p>
Discrete flow cytometry of underway samples from the Gradients 2019 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2019 (Gradients 3/KM1906) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including picoeukaryotes and the cyanobacteria Prochlorococcus and Synechococcus. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Discrete flow cytometry from the Gradients 2019 cruise using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations and heterotrophic bacteria from discrete flow cytometry data collected during the Gradients 2019 (Gradients 3/KM1906) oceanographic research cruise in the Northeast Pacific Ocean. The analysis includes cell abundance, forward light scatter, and pigment fluorescence of individual cells, including bacteria, picoeukaryotes, and the cyanobacteria Prochlorococcus and Synechococcus. Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
EI Single-Cell RNA-Seq Workshop 2020
<p>Datasets to be used for the "Single-Cell RNA-Seq Workshop 2020" at the Earlham Institute, Norwich, UK.</p>
Dataset - A lung-on-chip model reveals an essential role for alveolar epithelial cells in controlling bacterial growth during early M. tuberculosis infection
<p>Description of the sub-folders<br> Name, type of data, corresponding Figure in the manuscript<br> 3D view of the LoC model - .tiff image stack, Figure 1.</p> <p>Bacterial Growth Rate Data - .tiff image stacks, .csv files and MATLAB code to extract the fluorescence intensity over time, Figure 2, Figure 2 - figure supplement 2, Figure 2 - figure supplement 4, Figure 3, Figure 3 - figure supplement 2, Figure 4.</p> <p>AT Characterization - .tiff image stacks and MATLAB code to extract the number and volume of lamellar bodies from the stack of confocal images, Figure 1, Figure 1 - figure supplement 1, Figue 1 - figure supplement 2.</p> <p>AT Infection in LoC model - .tiff image stacks, Figure 2 - figure supplement 1.</p> <p>AT Infection in vivo - .tiff image stacks, Figure 1 - figure supplement 3.</p> <p>Simulations of in vivo infections - .dat files of growth rates in macrophages for the WT and ESX-1 deficient populations and MATLAB code to simulate an infection from this data, Figure 4.</p> <p> </p>
EpiScanpy: integrated single-cell epigenomic analysis
<p>All files below are in "h5ad" format, which can be opened by the Python package AnnData (see https://anndata.readthedocs.io/en/latest). The files are organized as:</p> <p><br> 1. Single-nucleus methylcytosine sequencing (snmC-seq) of neurons from the frontal cortex of young adult mouse brains from Luo et al., 2017. The files are the preprocessed input for EpiScanpy for multiple CG imputed feature spaces, 100k base pair windows, enhancers, gene bodies, promoters and CH gene bodies.<br> - processed_enhancers_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_genebodies_CH_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_promoter_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> - processed_windows_CG_luo_et_al_nov2020_paper_resubmission.h5ad<br> </p> <p>2. Single cell ATAC sequencing (scATAC-seq) from 10X Genomics, preprocessed peak count matrix of Next GEM v1.1 10k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. Peak matrices of Next GEM v1.1 10k PBMCs and whole blood fresh data (GEO:GSE129785) from Satpathy et al. 2019 (Greenleaf's lab). Concatenated 5000bp matrix of Fresh cortex from adult mouse brain (P50) from 10X Genomics and CEMBA180312_3B mouse brain sample from Fang et al. 2019.<br> - atac_pbmc_10k_nextgem_fragments_macs2_peaks_outter_all_chrom.h5ad<br> - atac_pbmc_10k_nextgem_fragments_merged_peaks_for_integration_greenleaf_outter_all_chrom.h5ad<br> - raw_greenleaf_pre_integration_oct_2020.h5ad (Satpathy et al. 2019)<br> - preprocessed_10x_genomics_5k_adulte_mouse_brain_Fang_et_al_2019_CEMBA180312_3B_5kb_windows.h5ad</p>
Cilia density and flow velocity affect alignment of motile cilia from brain cells
<p>Here we store the supplementary Materials and Methods for the publication Cilia density and flow velocity affect alignment of motile cilia from brain cells.</p> <p>In the Supplementary methods we included additional information on the hydrodynamic simulations. </p> <p>Video1 and Video2 are videos referenced in the main text of the paper</p> <p>In the archive 'raw data and code.tar' , we provide raw images and codes to support the article.The complete dataset of raw images is more than 1 Tb. Here we are limited to 50Gb. The full dataset is available upon request.<br> <br> We choose to provide a full dataset of two culture at DIV 16, one treated with shear flow and a control without flow.</p> <p>For each of the two cultures, the videos with propelled particles are in the directory FL,<br> The bright field images without particles are stored in BF. Unfortunately we uploaded only few videos because of their large size. The results of the analysis of this dataset is reported in the directory analysis (available for each culture).</p> <p>Moreover we provide the code to analyse these data.<br> The analysis routine:</p> <p>Step 1: for each field of view (fov) getting the cilia beating direction from the FL images. This is done with PIV. The code is Step1_PIVanalysis.mat</p> <p>Step 2: for each fov getting ciliated cell position and CBF from the BF movies. Gather the cilia beating direction and cilia posion and frequency in a unique figure and matlab class (Res.mat). This is done in Step2_gatherResults.mat</p> <p>The results of these analysis are stored in the analysis folder for each culture.</p> <p>These routines are repeated for each experiment and results are then plotted to get trends. In the folder code4figures we report the code that we used to make the figures in the papers starting from a matlab file "all_results*.mat", where are gathered all the analysis.</p> <p>The code may improve in the future with more comments. please check Nicola's github page for the latest update. Please contact us for any problem. https://github.com/NicolaPellicciotta/Code4-Cilia-density-and-flow-velocity-affect-alignment-of-motile-cilia-from-brain-cells</p> <p>All the raw videos and code are in the archive.</p> <p> </p>
Aberrant development of pancreatic beta cells derived from human iPSCs with FOXA2 deficiency
<p><strong>Project manager(s)</strong><strong>: </strong>Essam M. Abdelalim</p> <p>Induced pluripotent stem cells (iPSCs) were generated from a patient with a heterozygous deletion of the short arm of chromosome 20 at bands p11.22 to p11.21 (~969 kb deletion), which contains only one gene, <em>FOXA2 </em>(<em>FOXA2<sup>+/-</sup></em>iPSCs) as well as healthy controls (Ctr1 iPSCs and Ctr2 iPSCs). <em>FOXA2<sup>+/-</sup></em>iPSCs were differentiated into different stages of beta cell development to understand the role of FOXA2 during pancreatic beta cell development as described in the article entitled "<strong>Aberrant development of pancreatic beta cells derived from human iPSCs with <em>FOXA2</em> deficiency" by Elsayed et al</strong>. The dataset represents RNA-seq data generated from pancreatic progenitors (PP2) and endocrine progenitors (EPs) derived from Ctr1 iPSCs, Ctr2 iPSCs, and three clones of <em>FOXA2<sup>+/-</sup></em>iPSCs. </p> <p>The file name is: Sample name _overall sample number_read direction_001 where:</p> <p>- PP2-Ctr 1: pancreatic progenitors (PP2) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- PP2-Ctr 2: pancreatic progenitors (PP2) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- PP2-FOX1, PP2-FOX2, and PP2-FOX3: pancreatic progenitors (PP2) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- EP-Ctr1: endocrine progenitors (Eps) derived from Ctr1 iPSCs (healthy control 1)</p> <p>- EP-Ctr2: endocrine progenitors (Eps) derived from Ctr2 iPSCs (healthy control 2)</p> <p>- EP-FOX R1, EP-FOX R2, and EP-FOX R3: endocrine progenitors (EPs) derived from three different clones of patient-derived <em>FOXA2<sup>+/-</sup></em>iPSCs.</p> <p>- The RNA-Seq data were generated from two Ctr-iPSC lines and three FOXA2<sup>+/-</sup>iPSC lines.</p> <p>- Read direction: R1 (Forward), R2 (Reverse).</p>
Lactate Increases Stemness of CD8+ T Cells to Augment Anti-Tumor Immunity
<p>The immunological role of lactate in antitumor immunity is not well understood. In this study, we report lactate treatment significantly augments antitumor efficacy of immune checkpoint blockade or T cell vaccine therapy in multiple tumor models. Single cell transcriptomics and flow cytometry analysis revealed an increased subpopulation of stem-like TCF-1-expressing CD8<sup>+</sup> T cells upon lactate treatment.</p>
A novel phosphoproteomic landscape evoked in response to type I interferon in the brain and in glial cells
<p>Type I interferons (IFN-I) are key responders to central nervous system infection and injury. They mediate their effects primarily via transcriptional regulation of several hundred interferon-regulated genes. Using a mouse model for IFN-I-induced neurodegeneration, we identified widespread protein phosphorylation as a new mechanism by which IFN-I mediate their effects. Protein phosphorylation aligned with the clinical hallmarks and pathological outcome, including impaired development, motor dysfunction and seizures. <em>In vitro</em> experiments revealed extensive and rapid IFN-I-induced protein phosphorylation in microglia and astrocytes, the brain’s primary IFN-I-responding cells. Response to acute IFN-I stimulation was independent of gene expression and mediated by a small number of kinase families. The changes in the phosphoproteome affected a diverse range of cellular processes and functional analysis suggested that this response induced an immediate reactive state and prepared cells for subsequent transcriptional responses. Our studies reveal a hitherto unappreciated role for changes in the protein phosphorylation landscape in cellular responses to IFN-I and thus provide insights for novel diagnostic and therapeutic strategies for neurological diseases caused by IFN-I.</p>
Microscope images of human cancer cell lines (U2OS and HL-60)
<p>This is a dataset that contains microscope images from two cell lines, namely, a human osteosarcoma cell line (U2OS) and a human leukemia cell line (HL-60). The dataset was originally prepared for the cell counting task. It contains 165 labeled images (training: 133, test: 32).</p> <p>The file contains three folders:</p> <p>- training: 165 labeled images in .tiff format;<br> - test: 32 labeled images in .tiff format.</p> <p>Each labeled image has the following name: X.Y.N.tiff</p> <p>where:<br> X - the name of the human cancer cell line;<br> Y - a condition identifier (irrelevant);<br> N - the cell count.</p> <p><br> If you use this dataset, please cite the following paper:</p> <ul> <li>Lavitt F, Rijlaarsdam DJ, van der Linden D, Weglarz-Tomczak E, Tomczak JM. Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines. <em>Applied Sciences</em>. 2021; 11(11):4912. https://doi.org/10.3390/app11114912</li> </ul>
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.