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111,247 results for “cells”
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>
Beyondcell: targeting cancer therapeutic heterogeneity in single-cell RNA-seq
<p><strong><a href="https://gitlab.com/bu_cnio/Beyondcell">Beyondcell</a> </strong>is a methodology for the identification of drug vulnerabilities in single cell RNA-seq data. To this end, <strong>Beyondcell</strong> focuses on the analysis of drug-related commonalities between cells by classifying them into distinct therapeutic clusters. We have validated the tool in a population of MCF7-AA cells exposed to 500nM of bortezomib and collected at different time points: t0 (before treatment), t12, t48 and t96 (72h treatment followed by drug wash and 24h of recovery) obtained from <a href="https://www.nature.com/articles/s41586-018-0409-3"><strong><em>Ben-David U, et al., Nature, 2018</em></strong></a>. Here, you can find the integrated Seurat object obtained from this analysis. This object is meant to help users follow <strong>Beyondcell's</strong> <a href="https://gitlab.com/bu_cnio/Beyondcell/-/tree/master/tutorial/analysis_workflow">analysis workflow</a>.</p> <p> </p>
Pockets of Open Cells database
<p>Database of Pockets of Open Cells (POCs) detected in MODIS Terra imagery using the machine learning algorithm here: https://zenodo.org/record/4381994. Includes segemented masks and basic physical properties such as size and area.</p>
Silver Nanoparticles Alter Cell Viability Ex Vivo and in Vitro and Induce Proinflammatory Effects in Human Lung Fibroblasts
<p>Dataset for data generated and presented in following article by Löfdahl et al in Nanomaterials 2020, 10, 1868. doi:10.3390/nano10091868</p>
GFP-GOWT1 mouse stem cells TIFF file
<p>This is accompanying file to the CellMAPtracer software (<a href="https://doi.org/10.5281/zenodo.3878087">https://doi.org/10.5281/zenodo.3878087</a>).</p>
Data from: Predation drives the evolution of brain cell proliferation and brain allometry in male Trinidadian killifish, Rivulus hartii
<p>The external environment influences brain cell proliferation, and this might contribute to brain plasticity underlying adaptive behavioural changes. Additionally, internal genetic factors influence brain cell proliferation rate. However, to date, researchers have not examined the importance of environmental vs. genetic factors in causing natural variation in brain cell proliferation. Here, we examine brain cell proliferation and brain growth trajectories in free-living populations of Trinidadian killifish, Rivulus hartii, exposed to contrasting predation environments. Compared to populations without predators, populations in high predation environments exhibited higher rates of brain cell proliferation and a steeper brain growth trajectory (relative to body size). To test whether these differences in the wild persist in a common garden environment, we reared first generation fish originating from both predation environments in uniform laboratory conditions. Just as in the wild, brain cell proliferation and brain growth in the common garden were greater in high predation populations than in no predation populations. The similar results in field and common garden studies indicate that population differences in these brain features are intrinsic, probably genetic, differences arising from natural selection acting on overall brain growth and life history rather than differences arising through phenotypic plasticity.</p>
Data for "An optimized genome-wide virus-free CRISPR screen for mammalian cells"
<p>This is the raw data from the article "An optimized genome-wide virus-free CRISPR screen for mammalian cells " and scripts for analyzing it.</p>
Set of loom files for three Ewing sarcoma cell lines CHLA9, CHLA10, TC71 profiled with scRNASeq at single cell level
<p>The raw sequence files were published in <a href="https://www.mdpi.com/2072-6694/12/4/948">Miller et al, 2020, Cancers</a> . The files were downloaded and processed using kallisto mapper.</p> <p>The loom files were used to build a <a href="https://doi.org/10.1101/2021.06.14.448414">model of cell cycle with switches</a> and in the development of <a href="https://github.com/csgroen/scycle">scycle Python package</a>.</p>
Naive B cells, Antibody secreting cells, Plasma cells
<p>Raw Illumina MiSeq data in zipped FASTQ format. The data set includes demultiplexed samples from naive B cells (_20433_), antibody secreting cells (_20434_) and plasma cells (_20435_) obtained from the same Balb/c mouse via sorting. Every sample consists of forward (_R1_), reverse (_R2_) reads (2x250 bp).</p>
Immune repertoire profiling reveals that clonally expanded B and T cells infiltrating diseased human kidneys can also be tracked in the blood
<p>Recent advances in high-throughput sequencing allow for the competitive analysis of the human B and T cell immune repertoire. In this study we compared Immunoglobulin and T cell receptor repertoires of lymphocytes found in kidney and blood samples of 10 patients with various renal diseases based on next-generation sequencing data.</p>
Supplementary Material: Fluorescent Protein‐Tagged Sindbis Virus E2 Glycoprotein Allows Single Particle Analysis of Virus Budding from Live Cells
<p>Supplementary Videos for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(12), 6182-6199; doi:10.3390/v7122926, http://www.mdpi.com/1999-4915/7/12/2926:</p> <p><strong>Video S1A</strong> BHK cells infected with mCherry-E2 virus at 3 h p.i. Glycoprotein containing vesicles are transported to the PM from where individual virions bud out. White arrow point to budding virions. Overall amount of glycoproteins present on the PM and the number of virus particles budding out are relatively reduced compared to the late stage of infection. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S1B </strong>Enlarged area of video S1A showing budding virus particles from PM. White arrow indicates single particle post-budding moving away from the cell. Images were acquired at a rate of 0.99 fps and 75 frames were acquired. Video was generated using these images and played at a rate of 5 fps. Image acquisition time is shown as Time: hour: minute: second: millisecond (h:min:sec:msec ) and the scale bar represents 10 μm.</p> <p><strong>Video S2A</strong> Virus budding and single particle movement associated with filopodial extensions observed from mCherry-E2 virus-infected BHK cells at 6 h p.i. Glycoprotein containing vesicle transport to the PM is also observed. Budded virions travel along the periphery of filopodia and are released from filopodial extensions to the surrounding media. Image acquisition was at a rate of 1 fps and 285 frames were acquired. Movie was generated using these images and played at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S2B</strong> Enlarged area of video S2A showing budding virus particles from filopodia. White arrow indicates virus budding from filopodial extensions. Images were acquired at a rate of 1 fps and the acquired 285 frames were used to generate the video at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S3</strong> BHK cells transfected with RNA from a non-budding cdE2 mutant <sub>416</sub>CC<sub>417</sub>/A2 mCherry-E2 virus. This non-budding mutant is unable to release fluorescent virus particles from the infected cells. The video shows the absence of fluorescent virus particle budding from the PM at 6 h post transfection even though the PM and filopodial extensions contain mCherry-E2. Despite the transport of glycoproteins to the PM, no fluorescent particles were released into the media. Yellow arrows point toward filopodial extensions. For the video, 304 images were acquired at a rate of 0.98 fps and the video was generated using the acquired images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S4</strong> BHK cells transfected with RNA from an E1 Fusion loop (G91D) mutant of mCherry-E2 virus at 6 h post transfection. This non-fusing mutant produces fluorescent virus particles at a slower rate compared to WT that are unable to fuse after entering a new cell. White arrow points to fluorescent particles that are releasing into the media from filopodial extensions. Yellow arrow represents a fluorescent particle that had entered an adjacent un-transfected cell. A total of 149 images were acquired at a rate of 0.98 fps. Video was generated using these images at a rate of 7 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p><strong>Video S5A</strong> Glycoprotein E2 (mCherry-E2; red) colocalizing with Golgi stain (green) in BHK cells infected with mCherry-E2 virus and stained with BODIPY FL C5 ceramide at 5 h p.i. and imaged at 6 h p.i. Glycoprotein-containing red vesicles originate from Golgi as evidenced from the colocalization of red and green and these vesicles display anterograde transport to the PM and the virus particles are released by budding from the PM. Fluorescent particles are also seen budding from filopodial extensions (white arrows). Images were acquired at a rate of 0.13 fps for 295 seconds. Video was generated using these acquired images at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p><strong>Video S5B</strong> An enlarged area of the video S5A near the white arrow showing movement of particles on filopodial extensions between two cells. Movie was played at a rate of 5 fps. Image acquisition time is shown as Time: h:min:sec:msec and the scale bar represents 10 μm.</p> <p> </p> <p> </p>
Accelerated cell divisions drive the outgrowth of the regenerating spinal cord in axolotls - Supplementary file 1
<p>Tiff stack of individual high-resolution images that are shown in Figure 1 – figure supplement 1 (<a href="http://dx.doi.org/10.5281/zenodo.59817">http://dx.doi.org/10.5281/zenodo.59817</a>). It can be opened with Fiji or ImageJ.</p> <p>For details see:</p> <p>Rost, F, Albors, AR, Mazurov, V, Brusch, L, Deutsch, A, Tanaka, EM, Chara, O. 2016. Accelerated cell divisions drive the outgrowth of the regenerating spinal cord in axolotls. <em>bioRxiv</em> 67785. doi: <a href="http://dx.doi.org/10.1101/067785">10.1101/067785</a>.</p>
Atomic Force Microscopy Images of Cell Specimens
<p>This data set consists of seven atomic force microscopy images in MI format as well as corresponding previews in PNG format.</p> <p>The microscopy images are of cell material and have been scanned with AFM equipment from Keysight Technologies. Details on the individual images:</p> <ul> <li>image_0.mi - Chinese hamster ovary cells</li> <li>image_1.mi - Chinese hamster ovary cells</li> <li>image_2.mi - human bladder carcinoma cells</li> <li>image_3.mi - human bladder carcinoma cells</li> <li>image_4.mi - Chinese hamster ovary cells</li> <li>image_5.mi - Chinese hamster ovary cells</li> <li>image_6.mi - Chinese hamster ovary cells</li> </ul> <p>The images can be opened using, e.g. Gwyddion: http://gwyddion.net/<br /> The Python package Magni can be used to load the images into Python (using the magni.afm.io module): https://github.com/SIP-AAU/Magni</p> <p>The images are provided as-is without warranty of any kind.</p>
ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy
<p>Data for submission to Wellcome Open Research entitled "ultraLM and miniLM: Locator tools for smart tracking of fluorescent cells in correlative light and electron microscopy".</p> <p>Data_ultraLM.tif is an image stack from the fluorescence microscope mounted on the ultramicrotome.</p> <p>Data_miniLM.tif is an image stack from the fluorescence microscope mounted in the SBF-SEM.</p> <p>Data_miniLM_EM.tif is an image stack from the SBF-SEM while the miniLM was in-situ.</p>
Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes
<p>This file contains the data reported in the paper:</p> <p>A Bertei, F Tariq, V Yufit, E Ruiz-Trejo, N P Brandon, <em>Guidelines for the rational design and engineering of 3D manufactured solid oxide fuel cell composite electrodes</em>, <strong>Journal of the Electrochemical Society</strong> (2016)</p> <p>All the data here reported can be reproduced by solving the equations reported in the manuscript with the corresponding parameters.</p>
Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods
<p>This is supplementary material to the publication "Accurate modeling of plasma acceleration with arbitrary order pseudo-spectral particle-in-cell methods" by S. Jalas et. al.</p> <p>Particle in Cell (PIC) simulations are a widely used tool for the investigation of both laser- and beam-driven plasma acceleration. It is a known issue that the beam quality can be artificially degraded by numerical Cherenkov radiation (NCR) resulting primarily from an incorrectly modeled dispersion relation. Pseudo- spectral solvers featuring infinite order stencils can strongly reduce NCR – or even suppress it – and are therefore well suited to correctly model the beam properties. For efficient parallelization of the PIC algorithm, however, localized solvers are inevitable. Arbitrary order pseudo-spectral methods provide this needed locality. Yet, these methods can again be prone to NCR. Here, we show that acceptably low solver orders are sufficient to correctly model the physics of interest, while allowing for parallel computation by domain decomposition.</p> <p>The given script can be run with the open source particle in cell codes FBPIC (https://github.com/fbpic/fbpic) and Warp (https://bitbucket.org/berkeleylab/warp)</p> <p>The produced data is conformant with the openPMD standard (openpmd.org) and can be analysed for example with the openPMD-viewer (https://github.com/openPMD/openPMD-viewer).</p>
Transient Optoelectronic Analysis of the Impact of Material Energetics and Recombination Kinetics on the Open-Circuit Voltage of Hybrid Perovskite Solar Cells
<p>This is the data presented in the article 'Transient Optoelectronic Analysis of the Impact of Material Energetics and Recombination Kinetics on the Open-Circuit Voltage of Hybrid Perovskite Solar Cells' published in The Journal of Physical Chemistry C, DOI: 10.1021/acs.jpcc.7b02411.</p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Dynamics of individual T cell repertoires: from cord blood to centenarians
<p>The dataset contains processed T-cell receptor repertoire sequencing data from 79 individuals of different sex and age, originally published in [1] and [2]. Note that [1] describes only a subset of samples, while [2] describes the full cohort.</p> <p>The libraries were prepared using a 5'RACE protocol and sequenced on HiSEQ. The libraries incorporate unique molecular identifier (UMI) tags that were mainly used for counting cDNA molecules. Preprocessing was performed using the MIGEC software [3] as follows: all UMI tags represented by a single sequencing read were discarded, the remaining UMI tags were used to assemble cDNA consensus sequences. Note that this procedure eliminates most of cross-sample contamination (batch effect) as described in [2]. VDJ partitioning and CDR3 extraction was performed using MiTCR software [4], sequencing error correction was performed using ETE option in MiTCR. All datasets are converted into VDJtools [5] format, see http://vdjtools-doc.readthedocs.io/en/latest/input.html#vdjtools-format.</p> <p><strong>Sample description:</strong></p> <ul> <li>The A* in sample identifier is the batch ID.</li> <li>Age and sex data is provided in the metadata.txt file.</li> <li>Samples having age "0" are umbilical cord blood samples.</li> </ul> <p><strong>Contributors:</strong></p> <ul> <li>The T-cell repertoire aging study was a project ran in the Genomics of Adaptive Immunity Lab (Prof. Dmitry Chudakov)</li> <li>The samples were acquired, prepared and sequenced by Dr. Olga Britanova</li> <li>The data was analyzed and uploaded by Dr. Mikhail Shugay</li> </ul> <p><strong>Citations:</strong></p> <ul> <li>[1] OV Britanova, EV Putintseva, M Shugay, EM Merzlyak, MA Turchaninova, et al. Age-related decrease in TCR repertoire diversity measured with deep and normalized sequence profiling. The Journal of Immunology 2014; 192 (6), 2689-2698</li> <li>[2] OV Britanova, M Shugay, EM Merzlyak, DB Staroverov, EV Putintseva, et al. Dynamics of individual T cell repertoires: from cord blood to centenarians. The Journal of Immunology 2016; 196 (12), 5005-5013</li> <li>[3] M Shugay, OV Britanova, EM Merzlyak, MA Turchaninova, IZ Mamedov, et al. Towards error-free profiling of immune repertoires. Nature methods 2014; 11 (6), 653-655</li> <li>[4] DA Bolotin, M Shugay, IZ Mamedov, EV Putintseva, MA Turchaninova, et al. MiTCR: software for T-cell receptor sequencing data analysis. Nature methods 2013; 10 (9), 813-814</li> <li>[5] M Shugay, DV Bagaev, MA Turchaninova, DA Bolotin, OV Britanova, et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS computational biology 2015; 11 (11), e1004503</li> </ul>
Direct chromosome-length haplotyping by single-cell sequencing.
<p>Selected Strand-seq libraries from PMID:27646535 study. Data were originally shared on the European Nucleotide Archive (http://www.ebi.ac.uk/ena) under the accession number: PRJEB14185</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.