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111,247 results for “cells”
Development of ferret immune repertoire reference resources and single-cell-based high- throughput profiling assays
<p>We performed long read transcriptome sequencing of ferret splenocyte and lymph node samples full-length, non-chimeric circular consensus sequencing (CCS) reads to obtain over 120,000 high-quality immunoglobin (Ig) and T cell receptor (TCR) transcripts.</p>
Supplementary dataset to the publication "Ultraviolet C inactivation of Coxiella burnetii for production of a structurally preserved whole cell vaccine antigen"
<p>The dataset supplements the journal article "Ultraviolet C inactivation of <em>Coxiella burnetii </em>for production of a structurally preserved whole cell vaccine antigen" published by Katja Mertens-Scholz, Amira A. Moawad, Elisabeth M. Liebler-Tenorio, Andrea Helming, Jennifer Andrack, Peter Miethe, Heinrich Neubauer, Mathias W. Pletz and Ina-Gabriele Richter in the journal BMC Microbiology (https://doi.org/10.1186/s12866-024-03246-z). The file "NMII 100µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase II with 100µW in a time dependent manner. The file "NMI 100 and 250µW" contains all data regarding inactivation of <em>C. burnetii</em> Nine Mile phase I with 100µW and 250µW in a time dependent manner. The file "surviving fraction" contains all data regarding inactivation of <em>C. burnetii </em>Nine Mile phase I and II after UVC treatment. The file "serology" contains all data obtained from ELISA experiments. The file "diameter" contains all data regarding the bacterial diameter after UVC treatment.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Single-cell transcriptomic profiling unveils dysregulation of cardiac progenitor cells and cardiomyocytes in a mouse model of maternal hyperglycemia
<p>Congenital heart disease (CHD) is the most prevalent structural malformations of the heart affecting ∼1% of live births. To date, both damaging genetic variations and adverse environmental exposure such as maternal diabetes have been found to cause CHD. Clinical studies show ∼fivefold higher risk of CHD in the offspring of mothers with pregestational diabetes. Maternal pregestational diabetes affects the gene regulatory networks key to proper cardiac development in the fetus. However, the cell-type specificity of these gene regulatory responses to maternal diabetes and their association with the observed cardiac defects in the fetuses remains unknown. To uncover the transcriptional responses to maternal diabetes in the early embryonic heart, we used an established murine model of pregestational diabetes. In this model, we have previously demonstrated an increased incidence of CHD. Here, we show maternal hyperglycemia (matHG) elicits diverse cellular responses during heart development by single-cell RNA-sequencing in embryonic hearts exposed to control and matHG environment. Through differential gene-expression and pseudotime trajectory analyses of this data, we identified changes in lineage specifying transcription factors, predominantly affecting Isl1+ second heart field progenitors and Tnnt2+cardiomyocytes with matHG. Using in vivo cell-lineage tracing studies, we confirmed that matHG exposure leads to impaired second heart field-derived cardiomyocyte differentiation. Finally, this work identifies matHG-mediated transcriptional determinants in cardiac cell lineages elevate CHD risk and show perturbations in Isl1-dependent gene-regulatory network (Isl1-GRN) affect cardiomyocyte differentiation. Functional analysis of this GRN in cardiac progenitor cells will provide further mechanistic insights into matHG-induced severity of CHD associated with diabetic pregnancies.</p>
Metadata for RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation
<p>This is the Metadata of our recent publication https://doi.org/10.3390/ijms25094729 </p> <p>Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. In the attached files you will find</p> <ol> <li>Current database for photons</li> <li>Current database for particle radiation</li> <li>Helping supplementary information</li> <li>Tips for help with our Database</li> </ol>
Data for a publication "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells"
<p>A dataset containing data for the published article "Argon plasma-modified bacterial nanocellulose: Cell-specific differences in the interaction with fibroblasts and endothelial cells".</p> <p> </p> <p>For more details, please read the <strong>README - Description of data and analysis informations.txt</strong> file.</p> <p><strong>Dataset versions:</strong></p> <p><strong>V1:</strong> The first dataset containing a majority of the data.</p> <p><strong>V2:</strong> Dataset contains all the data mentioned in the article in the appropriate file formats for long-term preservation and accessibility.</p>
Functionalized mm-scale vapor cells for alkali-metal spectroscopy and magnetometry
<p>The entry contains numerical data displayed in Figures 3 and 4 of the manuscript titled above, see arXiv preprint: <a href="2405.10715" target="_blank" rel="noopener">https://arxiv.org/abs/2405.10715</a></p> <p><strong>Authors:</strong> <a href="https://arxiv.org/search/physics?searchtype=author&query=Raghavan,+H">Harini Raghavan</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Tayler,+M+C+D">Michael C. D. Tayler</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mouloudakis,+K">Kostas Mouloudakis</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Rae,+R">Rachel Rae</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=L%C3%A4hteenm%C3%A4ki,+S">Sami Lähteenmäki</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Zetter,+R">Rasmus Zetter</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Laine,+P">Petteri Laine</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Haesler,+J">Jacques Haesler</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Balet,+L">Laurent Balet</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Overstolz,+T">Thomas Overstolz</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Karlen,+S">Sylvain Karlen</a>, <a href="https://arxiv.org/search/physics?searchtype=author&query=Mitchell,+M+W">Morgan W. Mitchell</a></p> <p><strong>Abstract:</strong> We describe micro-fabricated rubidium vapor cells with integrated temperature-control functionality and demonstrate their suitability for use in miniaturized ultra-sensitive magnetometers. These functionalized vapor cells (FVCs) embody a dual-chamber design in low-conductivity silicon with anti-permeation coatings and micro-structured thin-film platinum surface traces as resistive heaters and temperature sensors. Thermal tests show our ability to control alkali metal distribution within the FVCs, ensuring a clean sensing chamber for optical measurements. Optical absorption spectroscopy is used to correlate the temperature readings with vapor density and to measure buffer gas pressure, of interest for optimizing sensitivity. Finally, we demonstrate zero-field resonance magnetometry with 18 fT/√Hz sensitivity in the 10 Hz to 100 Hz band, limited by laser noise and magnetic shield noise, which indicates that the functionalization does not introduce significant magnetic noise.</p>
Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software
<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: <strong>- Dynamic_PEFC_data.h5</strong> # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z <strong>- liquid_simulation.h5</strong> # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). <strong>- phasetran_simulation.h5</strong> # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: <strong>- astra_nonrigid.zip</strong> # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. <strong>- reconstruction_PEFC.py</strong> # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * <strong>!!!</strong> Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. <strong>- reconstruction_simulation.py</strong> # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>
Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.
<p>Bulk ATAC-seq data of tumour samples result in an averaged signal across different cell-types (cancer, stromal, vascular and immune cells). We propose a deconvolution framework called EPIC-ATAC (<a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>), which relies on newly identified cell-type specific ATAC-Seq marker peaks and reference profiles for all major cancer-relevant cell-types to predict the proportions of each cell-type.</p> <p>To evaluate EPIC-ATAC, we generated a bulk ATAC-Seq dataset from peripheral blood mononuclear cells (PBMCs) samples, from which the number of cells in each cell-type has been estimated using flow cytometry, as ground truth for cell proportions. The data provided in this Zenodo deposit correspond to:</p> <p>- The raw counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts.txt</p> <p>- The normalized (TPM-like) counts matrix for each peak called in this ATAC-Seq dataset: PBMC_counts_norm.txt</p> <p>- The cell fractions of each cell type in each sample: PBMC_cell_fractions.txt</p> <p>- The peaks called in each sample using MACS2 (*narrow.peaks): *_normalized.narrowPeak</p> <p>- Bed files listing ATAC-Seq fragments for each sample: *.bed</p> <p>We also evaluated EPIC-ATAC on multiple pseudobulks generated from single-cell ATAC-Seq data. We provide rds files containing the pseudobulks data used in our work for the evaluation of EPIC-ATAC. The rds files are located in the zip file "pseudobulks.zip".</p> <p>The file "additional_data.zip" contains additional files used to generate the reference profiles in EPIC-ATAC and to reproduce the main analyses performed in the manuscript: <a href="https://doi.org/10.7554/eLife.94833.1">https://doi.org/10.7554/eLife.94833.1</a>. These files are required to run the code available on the following GitHub repository: GfellerLab/EPIC-ATAC_manuscript. </p>
Dataset - Gravisensors in plant cells behave like an active granular liquid
<p>Dataset corresponding to data shown in article : https://hal.archives-ouvertes.fr/hal-01425298v2 (avalanches of statoliths pile in wheat coleoptile cells, avalanches of silica micro-particles in biomimetic cells, vertical fluctuations of statoliths in wheat coleoptile cells or extracted from their cells).</p> <p>Examples of python scripts that open and plot the data are also included.</p>
Dataset of "Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells"
<p><strong>Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells & Computational analysis of bulk and single-cell RNA-Seq data.</strong></p> <p> </p> <p><em><strong>Detailed quantitative analysis of GFP expression in SAHA and TCR-treated cells.</strong></em></p> <p>Cells were prepared for single cell analysis at the Genome Technology Facility (GTF) of the University of Lausanne. Cells were loaded on Fluidigm C1 IFC plates (5-10 μm), with run ID smart33, smart34 and smart35, corresponding to untreated, SAHA- and TCR-treated conditions respectively. After single cell capture on the Fluidigm C1 IFC plate, each chamber was inspected visually by microscopy and pictures were captured with a Zeiss Axiovert 200 M fluorescence microscope equipped with a Roper Scientific CoolSnap HQ camera using a Plan-Neofluar 10X lens (smart34 run) or 20X lens (for smart35 run). For each capture chamber, pictures in bright field and FITC channel were taken with the MetaMorph 6.3 software. Picture analysis was then performed using ImageJ 1.50b software (open access software: website). Brightness and contrast were adjusted for qualitative assessment of the pictures.</p> <p><em><strong>Computational analysis of bulk and single-cell RNA-Seq data.</strong></em></p> <p>Upon bulk or single cell isolation, RNA extraction and library preparation was performed according to Illumina protocols. Bulk and single-cell RNA-Seq data analysis are detailed here.</p> <p> </p> <p>Linked to the paper published in Cell Reports (doi:10.1016/j.celrep.2018.03.102): </p> <p><strong>Single-Cell RNA-Seq Reveals Transcriptional Heterogeneity in Latent and Reactivated HIV-infected Cells</strong></p> <p>Despite effective treatment, HIV can persist in latent reservoirs, which represent a major obstacle towards HIV eradication. Targeting and reactivating latent cells is challenging due to the heterogeneous nature of HIV infected cells. Here, we used a primary model of HIV latency and single-cell RNA sequencing to characterize transcriptional heterogeneity during HIV latency and reactivation. Our analysis identified transcriptional programs leading to successful reactivation of HIV expression.</p> <p> </p> <p> </p>
Single-cell RNA-seq of breast cancer infiltrating T cells (case 1)
<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 2) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads. Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>
Single-cell RNA-seq of breast cancer infiltrating T cells (case 2)
<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 1) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads (this sample was divided into two channels). Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Comparing the integration of bone cells on an even and a nano structured surface
<p>Bone cells develop better on a nano structured surface.</p> <p>Successful cellular integration is extremely important to the long-term viability of dental implants. The sooner cells can attach and surround the implant, the faster the patient recovers, and the lower the incidence of infection and site contamination. The specific biological response of the surrounding tissues depends enormously on the surface characteristics of the particular biomaterial. Moreover mouth infections are currently regarded as the main reason why dental implants fail. Therefore, antibacterial properties are another requirement for preventing potential bacterial infection.</p> <p>The multi-beam optical module developed within LASER4SURF, will be able to obtain functionalized metallic surfaces with textures around 1μm or less, enabling the required tolerances to reach the best cell adhesion and antibacterial properties. Thus, it will lead to extended implant life, reduced rejection and improved overall quality of life for patients.</p>
Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition
<p>This repository contains code to accompany the manuscript titled</p> <p><strong>Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition</strong></p> <p>by <strong>Carly D. Kenkel and Line K. Bay</strong><br> </p> <p>In this study, we used a phylogenetically controlled design to investigate the role of vertical symbiont transmission, an evolutionary mechanism predicted to enhance cooperation and holobiont fitness of reef-building corals. Six species of coral, three vertical transmitters and their closest horizontally transmitting relatives, were fragmented and subjected to a two-week thermal stress experiment. Symbiont cell density, photosynthetic function and translocation of photosynthetically fixed carbon between symbionts and hosts were quantified to assess changes in physiological metrics of fitness and cooperation. Amplicon sequencing of the <em>Symbiodinium</em> ITS-2 locus was used to investigate differences in symbiont community composition among focal species. We did not observe universally higher levels of cooperation in vertically transmitting species. However, the reduction in cooperation at the onset of bleaching was marginally associated with symbiont community diversity. Analysis of ITS2 amplicon sequence data suggest that it may not be vertical transmission <em>per se</em> that influences host-symbiont cooperation, but genetic uniformity of the symbiont community.</p> <p>Repository contents:</p> <ul> <li> <p><strong>TraitDataAnalysis.R:</strong> Annotated R script for generating figures and re-creating statistical analyses</p> <ul> <li> <p><strong>RsquaredGLMM.R:</strong> Accessory R script for running RsquaredGLMM analyses, called by <strong>TraitDataAnalysis.R</strong></p> </li> <li> <p><strong>NSF_RunningPam.csv</strong>: Input file for statistical analysis. Contains photophysiological data. Column headers are as follows:</p> <ul> <li> <p>Tank: Number of experimental tank in which experimental coral fragment was held</p> </li> <li> <p>Treatment: short-hand notation for sample treatments (e.g. ctrl1-5 = control temperature, genotypes 1-5)</p> </li> <li> <p>Water: source sump for temperature controlled water jackets for each set of treatment tanks</p> </li> <li> <p>Position: numerical rack position of coral fragment within experimental treatment tank</p> </li> <li> <p>Species: Coral species (Amil=<em>A. millepora</em>, Maqe=<em>M. aequituberculata</em>, Gast=<em>G. astreata</em>, Gach=<em>G. acrhelia</em>, Plob=<em>P. lobata</em>, Gcol=<em>G. columna</em>)</p> </li> <li> <p>Genotype: source colony origin of individual coral fragments within species</p> </li> <li> <p>Temp: experimental temperature treatment (ctrl: 27°C ; heat: 31°C)</p> </li> <li> <p>Treat: whether experimental corals received C14-labeled bicarbonate (bicarb), artemia or were sampled separately for Gene Expression Analysis (not presented in this manuscript)</p> </li> <li> <p>EQY: Effective quantum yield of <em>Symbiodinium</em> photosystem II as measured using PAM fluorometry</p> </li> <li> <p>Date: Actual calendar date of measure</p> </li> <li> <p>Transmission: coral symbiont transmission mode</p> </li> <li> <p>Reef: reef site of original coral collection</p> </li> <li> <p>Date: experimental date of measure</p> </li> </ul> </li> <li> <p><strong>TraitData.csv:</strong> Input file for statistical analysis. Contains all physiological trait data.</p> <ul> <li> <p>Includes columns as described above for the Running_Pam file in addition to columns containing raw trait data as described in the manuscript.</p> </li> </ul> </li> <li> <p><strong>TraitData_DaysAsCols.csv:</strong> Reformatted input file with trait data split by sampling day across columns</p> </li> </ul> </li> <li> <p><strong>DADA2Analysis.R:</strong> Annotated R script for generating figures and running ITS2 amplicon analyses</p> <ul> <li> <p>GeoSymbio_ITS2_LocalDatabase_verForPhyloseq.fasta: FASTA file of the GeoSymbio ITS2 reference database <a href="https://sites.google.com/site/geosymbio/">https://sites.google.com/site/geosymbio/</a>, formatted for use with the R prograom Phyloseq</p> </li> <li> <p>SeqVars_6Feb.fasta: FASTA file of identified sequence variants resulting from DADA2 analysis</p> </li> <li> <p>OutputDADA_6Feb.csv: Counts of sequence variants by sample</p> </li> <li> <p>Raw FASTQ paired end read files can be downloaded from NCBI's SRA: PRJNA338365</p> </li> </ul> </li> </ul>
Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells
<p>Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by </p> <p>Nathalie R. Reinhard<sup>1</sup>, Sanne van der Niet<sup>1</sup>, Anna Chertkova<sup>1</sup>, Marten Postma<sup>1</sup>, Theodorus W.J. Gadella Jr.<sup>1</sup>, Peter L. Hordijk<sup>1,2</sup>, and Joachim Goedhart<sup>1*</sup><br> </p> <p><strong>Affiliations:</strong></p> <p><sup>1 </sup>University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands</p> <p><sup>2 </sup>Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands</p> <p> </p> <p>*Correspondence to: j.goedhart@uva.nl</p>
Case report: Papillary squamous cell carcinoma of the penis
<p><strong>Case history</strong></p> <p>62-year-old male with a verruciform tumor located in distal penis and involving glans.</p> <p> </p> <p><strong>Histologic findings</strong></p> <p>The microphotographs show an exophytic verruciform tumor mass characterized by papillomatosis, slight to moderate acanthosis, and hyperkeratosis. Papillae are complex, some with blunt and others with spiky tips. Fibrovascular are present in most but not all papillae and are irregularly shaped. Tumor base is jagged and there is a prominent stromal reaction. Neoplastic cells in papillae and infiltrative tumor nests are well to moderately differentiated (grades 1-2) and no koilocytic atypia is observed.</p> <p> </p> <p><strong>Discussion</strong></p> <p>Verruciform penile tumors comprise about one-quarter of all penile squamous cell carcinomas (SCC) and include papillary, verrucous, and warty carcinomas, as well as giant condylomas and carcinoma cuniculatum. As a group, verruciform carcinomas are characterized by the presence of papillomatosis, acanthosis, and hyperkeratosis. However, there are distinctive morphological features for each one of these tumors. For papillary SCC, the most distinguishing feature is the presence of complex papillae with irregularly shaped fibrovascular cores. Papillary carcinomas tend to be polymorphic with some areas exhibiting condylomatous papillae and others with a more verrucous-like aspect. Fibrovascular cores are readily found in the former and are scant or even absent in the latter.</p> <p>Another important clue in the differential diagnosis with other verruciform tumors is the presence of a jagged tumor-stroma interface. In verrucous and cuniculatum carcinomas the tumor front is broad-based. The absence of koilocytosis allows the distinction from warty carcinomas and giant condylomas, tumors in which koilocytes are conspicuous. HPV status may be helpful in problematic cases, since in papillary carcinomas the HPV detection rate is very low or even null. Immunohistochemistry for p16<sup>INK4a</sup> is also useful since the vast majority of papillary carcinomas do not overexpress this protein. In order for a penile tumor to be considered as p16<sup>INK4a</sup> positive all neoplastic cells should be stained. Cases like this one in which some cells stain and others do not should be regarded as negative for p16<sup>INK4a</sup> overexpression.</p> <p>The inguinal metastatic rate of penile papillary carcinomas is very low and the prognosis is good. Less than one-fifth of all patients present inguinal involvement and even in these cases the mortality rate is low. Even when tumors invade penile erectile tissues prognosis is good as long as no high-grade areas (observed in a minority of the patients) are identified.</p> <p>Clinically patients should be managed using risk-group stratification systems and taking into account histological grade, anatomical level of maximum tumor infiltration, and the presence of vascular and perineural invasion. </p> <p> </p> <p><strong>References</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/pubmed/20061934">Chaux et al. Am J Surg Pathol. 2010 34(2): 223-30</a></p> <p><a href="https://www.ncbi.nlm.nih.gov/pubmed/22641955">Chaux & Cubilla. Semin Diagn Pathol. 2012 29(2): 72-82</a></p> <p><a href="https://www.ncbi.nlm.nih.gov/pubmed/22641955">Chaux & Cubilla. Semin Diagn Pathol. 2012 29(2): 67-71</a></p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Single Cell RNAseq of Mouse Testis
<p>This is the processed data from the publication: "Unified single-cell analysis of testis gene regulation and pathology in 5 mouse strains" (https://doi.org/10.1101/393769)</p> <p>The raw data is avaliable at GEO: GSE113293</p> <p>Associated software is at https://zenodo.org/badge/latestdoi/140632831</p> <p>SDA_objects.zip contains key tables required for many functions, download this to use the shiny app. Contents:</p> <ul> <li>cell_data: data.table containing metadata of the cells. 80 columns including cell id, SDA component cell scores, Tsne coordinates, pseudotime, experimental group etc.</li> <li>data: a sparse matrix of normalised read counts (20322 cells by 19262 genes)</li> <li>gene_annotations: data.table containing gene locations, enrichment vs other tissues from bulk data, infertility gene status</li> <li>GO_enrich: data.table containing gene ontology enrichments for each component, the genes enriched, p.values and enrichment values</li> <li>principal_curves: list of output of princurve() containing the pseudotime trajetories</li> <li>SDAresults: output of SDA run loaded using SDAtools::load_results()</li> </ul> <p>Other R objects include:</p> <ul> <li>QC_count_matrix: Sparse matrix of raw count values for QC cells and genes</li> <li>cell_imputation_AUCs: data.table of PRAC AUC values for unimputed (train), mean cell, SDA (predict), ICA, PCA, NNMF, and MAGIC</li> <li>HocomocoV11_motifProbs_matrix: Matrix of regulation probabilities from MotifFinder using fixed motifs from HocomocoV11 database</li> <li>motifFinder_denovo: list of results of MotifFinder denovo motifs from promoters regions of genes from each component</li> <li>motifFinder_denovo_fixed: list of results of MotifFinder using 125 denovo motifs with fixed motif on all genes.</li> <li>tomtom_matched_motifs: data.table of TOMTOM matches of denovo motifs, plus metadata</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.