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111,247 results for “cells”
Evaluating the effect of 8 M4K compounds on the viability of DIPG patient-derived cells (SU-DIPG-VI, HSJD-DIPG-007 and SU-DIPG-IV)
<p>Viability assay using Celltiter Glo kit has been chosen as the main method for evaluating the efficacy of ALK2 inhibitors on DIPG patient-derived cells due to its robustness. As a pilot experiment, 8 ALK2 inhibitors with differing potencies were chosen and evaluated in 3 DIPG patient-derived cell lines (one with wild-type ALK2 and two with mutant ALK2).</p>
Partis post-processed B cell receptor repertoires from BioProject PRJNA349143
<p>These files correspond to partis annotations of several datasets found in BioProject PRJNA349143. (DOI: 10.5281/zenodo.821659).</p>
NGS data produced in 'Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries'; Nature Communications (2019)
<p>Sharma, G et al. Rapid selection and identification of functional CD8+ T-cell epitopes from large peptide-coding libraries. <em>Nature Communications</em>. Accepted (August 2019)</p> <p><strong>Abstract:</strong></p> <p>Cytotoxic CD8+ T-cells recognize and eliminate infected or malignant cells that present, at their cell surfaces, short peptide epitopes derived from intracellularly processed antigens. However, broadly searching for specific major histocompatibility complex (MHC)-bound peptide epitopes that are naturally processed and capable of eliciting a functional T-cell response has been challenging. Here, we report a method for deep and unbiased T-cell epitope profiling, which is done by using <em>in vitro</em> co-culture of CD8+ T-cells and target cells transduced with high-complexity epitope-encoding minigene libraries. Target cells that are subject to cytotoxic attack from T-cells in co-culture are isolated, before they are lost to apoptosis, by fluorescence-activated cell sorting and characterized by sequencing the minigenes encoded within. In the present study, we validate this highly parallelized method using known murine T-cell receptor/peptide-MHC pairs and diverse minigene-encoded epitope libraries to identify naturally processed and MHC-presented peptide epitopes unambiguously and with high sensitivity.</p>
emmetaobrien/celltypes: Allen Brain Atlas Cell Types
<p>This brain cell database contains a survey of biological features derived from single cell data, from both human and mouse. It is part of a multi-year project to create a census of cells in the mammalian brain.</p> <p>The database contains electrophysiological, morphological, and transcriptomic data measured from individual cells, as well as models simulating cell activity. Thus far, data generation has focused on select areas of cerebral cortex, and thalamic neurons.</p> <p><a href="https://celltypes.brain-map.org/">https://celltypes.brain-map.org/</a></p>
The effect of cell density on biomass and fatty acid productivity during cultivation of Rhodomonas salina in a tubular photobioreactor - the dataset.
<p>The microalga Rhodomonas salina is widely used in aquaculture. There is a need for<br> optimization of the growth of the microalgae and its content of essential fatty acids.<br> Here, the fatty acid profile of Rhodomonas in relation to cell density during cultivation<br> in a tubular PBR is investigated. It is expected that cell density is an important<br> factor in controlling productivity and fatty acid content of the microalgae because<br> cell density is important in determining light availability due to the self-shading of the<br> algae. The carbon productivity as a function of cell density is described by a saturation<br> curve. The carbon productivity and the productivity of total fatty acids are lowest<br> at the lowest cell density, and independent of cell density at higher cell densities.<br> The relative contribution of the two poly-unsaturated fatty acids eicosapentaenoic<br> acid (EPA) and docosahexaenoic acid (DHA) increases with increasing cell density and<br> saturates at 1 × 106 cells/ml. We conclude that large-scale production of Rhodomonas<br> in this tubular PBR should take place at cell densities of 1 × 106 cells/ml, while there<br> are indications for increasing difficulties in maintaining steady-state production in<br> this PBR at higher cell densities.</p>
Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures (raw data)
<p>This data set includes raw data supporting the paper "<strong>Synergistic activity of Hsp90 inhibitors and anticancer agents in pancreatic cancer cell cultures</strong>".</p> <p><strong>The files include the following data</strong>:</p> <p>1. MTT assay results used for calculation of <strong><em>EC</em><sub>50</sub> values</strong> of tested compounds and their combinations in cancer cells;</p> <p>2. The data used for calculating <strong>combination index</strong> in order to evaluate synergistic activity;</p> <p>3. The raw data from compound activity evaluation in <strong>3D tumor spheroid assay</strong>;</p> <p>4. The data from <strong>compound and hyperthermia </strong>effect evaluation in cancer cells.</p>
Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM
<p>This is the example data for the protocol "Time-resolved single-cell RNA-seq using scSLAM-seq and GRAND-SLAM".</p> <p>The test data comprises sequencing data from 12 different cells. Five of them in mock condition with 4sU, five infected with MCMV with 4sU and 2 uninfected cells without 4sU. For each sample, paired-end sequencing was conducted and therefore two files exist for each sample. The files can be found at data/fastq.</p> <p>The reads should be mapped against the mouse genome and the MCMV genome, which are located in data/genome. The sequence is in fasta-fomat and the features in gtf-format.The scripts conducting quality control, read mapping and GRAND-SLAM analysis can be found in the processing folder. For each computational step in this protocol there is a shell script for running the necessary steps. Executing all scripts will reproduce the analysis of our test data</p>
Supplemental Data for: Segmentation-free inference of cell types from in situ transcriptomics data
<p>Supplemental Data for: Segmentation-free inference of cell types from <em>in situ</em> transcriptomics data</p>
Numbers of Single-Cell β-Actins
<p>The dataset is an Excel composed of ten columns representing absolute numbers of single-cell β-actins from cell types of A549 (column A, N<sub>cell</sub> = 14,242), Hep G2 (column B, N<sub>cell</sub> = 35,932), MCF 10A (column C, N<sub>cell</sub> = 16,650), HeLa (column D, N<sub>cell</sub> = 26,151), PC3 (column E, N<sub>cell</sub> = 11,922), SACC-83 (column F, N<sub>cell</sub> = 13,616), CAL 27 (column G, N<sub>cell</sub> = 7,271), CAL 27-LN2 (column H, N<sub>cell</sub> = 6,222), Oral Tumour Patient I (column I, N<sub>cell</sub> = 359) and Oral Tumour Patient II (column J, N<sub>cell</sub> = 175), respectively.</p>
A bioactive compound isolated from Duku (Lansium domesticum Corr) fruit peels exhibits cytotoxicity against T47D cell line
<p><strong>Background: </strong>Breast cancer is a major health problem for women globally. Many attempts have been promoted to cure cancer by finding new anticancer medicines from natural resources. Despite the richness of biodiversity discovered, there are some natural resources that remain unexplored. Fruit peels of Duku (<em>Lansium domesticum</em> Corr.) are rich with compounds that may have the potential to be developed as anticancer drugs. This study aimed to isolate cytotoxic compounds from the fruit peels of <em>L. domesticum</em> and assess their cytotoxic nature against T47D cells.</p> <p><strong>Methods: </strong>Powdered peels were macerated with ethyl acetate and the filtrate was evaporated to give EtOAc extract A. Dried extract A was triturated with n-hexane to give n-hexane soluble fraction B and insoluble fraction C. The cytotoxic nature of these three samples were assessed using MTT assay using T47D cells and doxorubicin as a control</p> <p><strong>Results: </strong>Fraction C that showed the smallest IC<sub>50</sub> (25.56 + 0.64µg/mL) value compared to extract A and fraction BFraction C was further fractionated by vacuum liquid chromatography to give 6 subfractions. Subfraction 2 showed a single compound based on thin layer chromatography, and this compound was identified as Lamesticumin A on the basis of its spectroscopic data. Lamesticumin A demonstrated cytotoxic activity against T47D cell lines with an IC<sub>50</sub> value of 15.68 + 0.30µg/mL.</p>
Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane
<p>This Dataset contains primary data used for the publication "Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane" published online on 04. November 2019<br> doi:10.3390/cells8111388</p> <p><strong>Abstract:</strong> T cell activation is immediately followed by internalization of the T cell receptor (TCR).<br> TCR endocytosis is required for T cell activation, but the mechanisms supporting removal of TCR<br> from the cell surface remain incompletely understood. Here we report that TCR endocytosis is<br> linked to the clathrin-independent carrier (CLIC) and GPI-enriched endocytic compartments<br> (GEEC) endocytic pathway. We show that unlike the canonical clathrin cargo transferrin or the<br> adaptor protein Lat, internalized TCR accumulates in tubules shaped by the small GTPase Cdc42<br> and the Bin/amphiphysin/Rvs (BAR) domain containing protein GRAF1 in T cells. Preventing<br> GRAF1-positive tubules to mature into endocytic vesicles by expressing a constitutively active<br> Cdc42 impairs the endocytosis of TCR, while having no consequence on the uptake of transferrin.<br> Together, our data reveal a link between TCR internalization and the CLIC/GEEC endocytic route<br> supported by Cdc42 and GRAF1.</p> <p> </p> <p>Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication.</p> <p>Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)</p> <p> </p> <p>In case this Dataset is updated, new version will be available with doi:10.5281/zenodo.3545842</p>
Single-cell Hi-C matrix Nagano 2017
<p>Single-cell Hi-C interaction matrix in mcool format, provided in 10kb and 1Mb. Data based on Nagano 2017: Cell-cycle dynamics of chromosomal organization at single-cell resolution</p>
Text-fig. 2. Chara socotrensis. Branchlet segments with mucronate end cells. in Some Finds Of Charophytes From East-Africa (Zambia, Tanzania, Kenya And Somalia)
Text-fig. 2. Chara socotrensis. Branchlet segments with mucronate end cells.
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019)
<p>The dataset contains a binary classification whether at least one cloud-to-ground lightning flash as detected by the ALDIS [1] lightning location system occurred in the previous hour in an ERA5 grid cell. Data with an amplitude between -2 kA and +15 kA are excluded.<br><br></p> <p>The data cover 8.25E to 16.75E longitude and 45.25N to 49.75N latitude.<br><br>Non-commercial use is allowed conditional on proper citation of the data source, ALDIS, and the accompanying manuscript <em>Ehrensperger, G., Simon, T., Mayr, G. J., and Hell, T.: Identifying lightning processes in ERA5 soundings with deep learning, Geosci. Model Dev., 18, 1141–1153, <a href="https://doi.org/10.5194/gmd-18-1141-2025">https://doi.org/10.5194/gmd-18-1141-2025</a>, 2025</em>.</p>
Figure 3 in Investigation of blood cells of Miniopterus schreibersii (Mammalia: Chiroptera) in Turkey
Figure 3. Cells of eosinophil (left) and basophil (right) cells of M. schreibersii.
Proportion and distribution of neurotransmitter-defined cell types in the ventral tegmental area and substantia nigra pars compacta - Tabular Dataset
<p>Tabular dataset for all figures in preprint "Proportion and distribution of neurotransmitter-defined cell types in the ventral tegmental area and substantia nigra pars compacta". </p>
Figure 2 in High Efficiency All-Polymer Solar Cells
Figure 2. – Desmodema polystictum, 14.8 cm total length, caught off Lakshadweep waters.
DivIVA controls the dynamics of septum splitting and cell elongation in Streptococcus pneumoniae
<ul> <li>Raw phase contrast and fluorescence images to analyze the localization of DivIVA-HT in <em>Streptococcus pneumoniae</em>.</li> <li>Raw phase contrast and fluorescence images to analyze the localization of GFP-aPBP1a in <em>Streptococcus pneumoniae </em>wild-type and D<em>divIVA</em> cells.<em><br></em></li> <li>Raw phase contrast and fluorescence images to analyze the localization of GFP-aPBP2a in <em>Streptococcus pneumoniae </em>wild-type and D<em>divIVA</em> cells.</li> <li>Raw bright field (BF), diffraction-limited (DL) and reconstructed dSTORM images to analyze peptidoglycan synthesis in <em>S. pneumoniae </em>wild-type and mutant cells (D<em>divIVA</em>, D<em>pbp1a, </em>D<em>pbp2a, </em>D<em>pbp2b mltG(Y488D)</em>.</li> </ul>
CO2 Protects Cells from Iron-Fenton Oxidative DNA Damage in E. coli and Humans
<p>Whereas hydroxyl radical is commonly named as the Fenton product responsible for DNA and RNA<br>damage in cells, here we demonstrate that the cellular reaction generates carbonate radical<br>anion due to physiological levels of bicarbonate. Analysis of the metabolome, the transcriptome,<br>and the human genome shows a consistent buffering of H2O2-induced oxidative<br>stress leading to one common pathway, namely guanine oxidation. Particularly revealing are nanopore-<br>based studies of direct RNA sequencing of cytosolic and mitochondrial ribosomal RNA along with<br>glycosylase-dependent qPCR studies of oxidative DNA damage in telomeres. The focusing of oxidative<br>modification on one pathway is consistent with the highly evolved base excision repair suite of enzymes<br>and their involvement in gene regulation in response to oxidative stress.</p>
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.