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FIGURE 2 in Fine Structure Of The Male Genital Systems, Spermatophores And Unusual Sperm Cells Of Saxidromidae (Acari, Actinotrichida)
FIGURE 2: TEM micrographs of details of testis of Rhinodromus lootsi (a-c, e) and Saxidromus delamarei (d). (a) – Overview of testis. Note thick epithelium comprising the glandular part and area with round spermatids composing the germinal part of testis. Scale bar: 20 µm. (b)– Detail of glandular part showing large nuclei, conspicuous nucleoli and Golgi bodies. Scale bar: 5 µm. (c) – Nuclear region of glandular epithelial cell with numerous rough ER cisternae and Golgi bodies. Scale bar: 2 µm. (d) – Detail of germinal part of testis of S. delamarei showing synspermatid containing four nuclei (N1-N4). White arrows indicate cell membrane bordering the synspermatid. Note tubular invaginations at the cell peripheriy. Three acrosomal vacuoles (black arrows) and parts of acrosomal filaments are also seen. The nuclei, at the beginning of chromatin condensation, still have a nuclear envelope (arrowheads). Scale bar: 1 µm. (e) – Distal part of testis with large synspermatid. Two much condensed chromatin bodies are seen. Scale bar: 5 µm.
FIGURE 1 in Fine Structure Of The Male Genital Systems, Spermatophores And Unusual Sperm Cells Of Saxidromidae (Acari, Actinotrichida)
FIGURE 1: Light micrographs of transverse sections through the posterior part of the idiosoma of Rhinodromus lootsi. (a) – Shortly in front of the genital opening. The two vasa deferentia located below the midgut are seen containing several synspermia. (b) – Slightly posterior, the genital opening is appearing. The vasa deferentia are connected by a transverse bridge (asterisk indicates a shrinkage artifact). (c) – More posteriorly, the ejaculatory duct with its dorsal chamber appears (asterisk indicates artifact). (d) – The paired testis are seen consisting of germinal and glandular parts. The dorsomedian excretory organ (i.e. the postcolon; e.g., Alberti and Coons 1999) is seen. Note that all parts of the genital system are located ventral of the digestive system. Scale bar: 50 µm.
FIGURE 6 in Fine Structure Of The Male Genital Systems, Spermatophores And Unusual Sperm Cells Of Saxidromidae (Acari, Actinotrichida)
FIGURE 6: Comparison of mating by Saxidromus delamarei and Rhinodromus lootsi and their corresponding spermatophores as seen in TEM (that of Rh. lootsi partially reconstructed; a, d from Coineau et al., 2006, b from Alberti et al., 2007). (a) – Some details of the mating sequence from above to below: S. delamarei male (black) has captured a female with its forelegs and deposits a rather large spermatophore (arrow). The male then turns round and impales the female onto the spermatophore. The spermatophore almost fills the "entire" female. The male has to separate the upper part of the spermatophore (which is more or less in the female) from the lower part which is attached to the ground. (b) – Longitudinal section of spermatophore of S. delamarei. Note the considerable amount of secretion forming the spermatophore and the rather small sperm chamber containing dense secretion and many synspermia. (c) – Spermatophore of Rh. lootsi drawn to same scale as that of S. delamarei. The head is (largely) represented by one synspermium. Stalk added schematically. Scale bar for b and c: 50 µm. (d) – Mating sequence seen in Rh. lootsi. Note that the spermatophore (arrow) produced by the male is considerably smaller bearing a very small head (i.e., mainly the synspermium). The male can use the same stalk several times depositing further synspermia on it. The male inserts its dorsoanteriad protruding processus into the female's genital opening prior to spermatophore deposition (not shown; see Coineau et al., 2006 for more details).
Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"</p>
T cell dysregulation associated with COVID-19 severity and progression
<p>The dataset refers to a study the objective of which was to retrospectively investigate the immune dysregulation associated with COVID-19 severity and clinical course in hospitalised patients. Immunophenotyipc analyses were performed by multi-colour flow cytometry on whole blood samples, the systemic concentration of selected cytokines was determined in serum samples.</p> <p>The dataset contains de-identified demographic and clinical information, as well as experimental raw data.</p>
Dataset related to article "Systematic review and meta-analysis of preclinical studies testing mesenchymal stromal cells for traumatic brain injury"
<p>Mesenchymal stromal cells (MSCs) are widely used in preclinical models of traumatic brain injury (TBI). Results are promising in terms of neurological improvement but are hampered by wide variability in treatment responses. We made a systematic review and meta-analysis: 1) to assess the quality of evidence for MSC treatment in TBI rodent models; 2) to determine the effect size of MSCs on sensorimotor function, cognitive function and anatomical damage; 3) to identify MSC-related and protocol-related variables associated with greater efficacy; 4) to understand whether MSC manipulations boost therapeutic efficacy.</p> <p>The meta-analysis included 80 studies. After TBI, MSCs improved sensorimotor and cognitive deficits, and reduced anatomical damage. Stratified meta-analysis on sensorimotor outcome showed similar efficacy for different MSC sources and for syngeneic or xenogenic transplants. Efficacy was greater when MSCs were delivered in the first week post-injury, and when implanted directly into the lesion cavity. The greatest effect size was for cells embedded in matrices or for MSC-derivatives.</p> <p>MSC therapy is effective in preclinical TBI models, improving sensorimotor, cognitive and anatomical outcomes, with large effect sizes. These findings support clinical studies in TBI.</p> <p>The present dataset reports extrapolated data used for the meta-analysis</p>
Transcriptome analysis of T47D cells and H2A.J-KO derivatives for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial gland cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
Single cell ATAC-seq Mouse Kidney Data: Chromatin-accessibility estimation from single-cell ATAC data with scOpen
<p>We provide results regarding the bioinformatic analysis of scATAC-seq from mouse UUO kidney at different time points.</p>
Trained network for segmentation of yeast cell from brightfield images in microfluidic traps - DetecDiv (id02)
<pre>Trained network for segmentation of yeast cell from brightfield images in microfluidic traps. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553771">https://doi.org/10.5281/zenodo.5553771</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Trained network for cell-cycle slowdown detection - DetecDiv (id03)
<pre>Trained network for cell-cycle slowdown detection. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553796">doi.org/10.5281/zenodo.5553796</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Trained network for classification of images from yeast cell lifespans - DetecDiv (id01)
<p>Trained network for classification of images from yeast cell lifespans.</p> <p>Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5552642">doi.org/10.5281/zenodo.5552642</a></p> <p>Testset: <a href="https://doi.org/10.5281/zenodo.5866747">doi.org/10.5281/zenodo.5866747 </a></p> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells
<p>Raw data for our publication "Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells", including imaging files (Paravision), ASCII, MATLAB and EC Lab data files organised corresponding to each figure in the paper and supplementary information.</p>
Dataset associated to paper: Nanoscaffold effects on the performance of air-cathodes for microbial fuel cells: Sustainable Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions
<p>This file contains the dataset associated to the published research article "Nanoscaffold effects on air-cathode performance in microbial fuel cells: Fe/N-carbon electrocatalysts for the oxygen reduction reaction under neutral pH conditions". The dataset contains X-ray powder diffraction, Inductively Coupled Plasma Emission Spectroscopy, elemental analysis, measurements of the specific surface area, transmission electron microscopies, x-ray photoelectron microscopy, electrochemistry and microbial fuel cells power outputs data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreements No. 799175 (HiBriCarbon) and No. 748968 (EDGE-FREEMAB). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213 and 19/FFP/6761. SI kindly acknowledges support by the Department of Social Justice State Government of Maharashtra, India. </p>
Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration
<p>Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.</p>
A computational workflow for cell line profiling by Imaging Mass Cytometry.
<p>Imaging Mass Cytometry Data as 32-bit single TIFF with computational analysis from the manuscript: <strong>A computational workflow for cell line profiling by Imaging Mass Cytometry.</strong></p> <p><strong><span lang="EN-US">Breast cancer cell lines SKBR3 MCF7 HCC1143 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">Elongated cell lines HeLa SKOV3 BJ IMC data and CellProfiler pipelines.zip:</span></strong></p> <p><strong><span lang="EN-US">Small cell lines A431 HT29 BxPC3 IMC data and CellProfiler pipelines.zip</span></strong></p> <p><strong><span lang="EN-US">U937 PMA-differentiated cells IMC data and CellProfiler pipeline.zip</span></strong></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data and CellProfiler pipeline.zip</span></strong></p> <p><span lang="EN-US">Contains 1 folder per cell line or drug treatment of single TIFF 32-bit markers exported from MCD/txt files (including Xe131 channel) and their respective cpproj. pipeline file for IMC Cell Line Profiler workstream reproducible analysis</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler high dimensional and correlation analysis R scripts.zip</span></strong></p> <p><span lang="EN-US">Contains three adaptable R scripts for high dimensional analysis, correlation analysis and combination of both scripts for Machine Learning classified datasets.</span></p> <p><strong><span lang="EN-US">Breast cancer cell lines nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, nuclear classes visual rendering, and classifier model files with outputs for two machine learning classifiers (Random Forest and Fast Gentle Boosting) per breast cancer cell line for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">A431 Cisplatin Study IMC data nuclear state classification by CellProfiler Analyst MLs.zip</span></strong></p> <p><span lang="EN-US">Contains SQLite databases, properties files, training datasets, classifier model with outputs for Fast Gentle Boosting and Random Forest per treatment for CellProfiler Analyst workflow reproducibility.</span></p> <p><strong><span lang="EN-US">IMC Cell Line Profiler pseudo-color images with Ki-67 marker Cytoplasm marker and Cell-ID nuclei (Fig2 Fig3), visual nuclei and whole-cell segmentation contours rendered images (Fig4).</span></strong></p> <p><strong><span lang="EN-US">Non-compensated and compensated multiTIFF 32-bit cells lines with Cellprofiler masks SCE objects and FCS files and Datatables.zip</span></strong></p> <p>Contains publicly available compensation matrix (<a href="https://zenodo.org/records/7575859">https://zenodo.org/records/7575859</a>) , R compensation script (<strong>Compensation IMC data with CATALYST.R)</strong>, compensated and non-compensated multiTIFF stacks 32-bit per cell line experiment, exported CellProfiler 16-bit masks per cell line dataset, R single cell experiment script (<strong>Conversion IMC data to Single Cell Experiments Objects and FCS.R)</strong> with inputs and outputs (fcs files, sce files, panel files, metadata files),R<strong> </strong>conversion single cell experiment to datatable script<strong> (Conversion SCE to Datatable and analysis.R)</strong>.</p> <p><strong><span lang="EN-US">Step-by-step guide to assist users with the IMC Cell Line Profiler computational workflow.</span></strong></p>
Nontuberculous mycobacteria persistence in a cell model mimicking alveolar macrophages (genome assembly and annotation dataset)
<p>This dataset includes the assembled contigs (.fasta and .gbk files), the nucleotide sequences of the prediction transcripts (CDS, rRNA, tRNA, tmRNA, misc_RNA) (.ffn files) and the respective amino acid sequences of the translated CDS sequences (.faa files) for the following Nontuberculous mycobacteria (NTM) strains: <em>Mycobacterium smegmatis </em>mc<sup>2</sup>155 (reference strain), <em>Mycobacterium avium</em> ATCC25921 (reference strain), <em>M. avium </em>60/08 (clinical strain), <em>Mycobacterium fortuitum</em> ATCC6841 (reference strain) and <em>M. fortuitum</em> 747/08 (clinical strain).</p> <p>All raw sequence reads used in this study were deposited in the European Nucleotide Archive (ENA) (BioProject PRJEB30455).</p> <p>The associated article can be found here: <a href="https://www.ncbi.nlm.nih.gov/pubmed/31035520">https://www.ncbi.nlm.nih.gov/pubmed/31035520</a></p> <p> </p>
CRISPR knockout of EZH1 in AML cell line
<p>We wanted to ensure the specificity of the EZH1 antibody that I was working with in previous posts, as well as creating a useful reagent to use in future experiments by generating a CRISPR knockout line of EZH1.</p>
Mass cytometry immunophenotyping data of two-week-old mouse pups' spleens depleted of maternal cells
<div> <div> <div> <p>The maternal cells transferred into the fetus during gestation persist long after birth in the progeny. These maternal cells have been hypothesized to promote the maturation of the fetal immune system in utero but there are still significant gaps in our knowledge of their potential roles after birth. To provide insights into these maternal cells' postnatal functional roles, we set up a transgenic mouse model to specifically eliminate maternal cells in the neonates by diphtheria toxin injection and confirmed significant depletion in the spleens. We then performed immunophenotyping of the spleens of two-week-old pups by mass cytometry to pinpoint the immune profile differences driven by the depletion of maternal cells in early postnatal life. We observed a heightened expression of markers related to activation and maturation in some natural killer and T cell populations. We hypothesize these results to indicate a potential postnatal regulation of lymphocytic responses by maternal cells. Together, our findings highlight an immunological influence of maternal microchimeric cells postnatally, possibly protecting against adverse hypersensitivity reactions of the neonate at a crucial time of new encounters with self and environmental antigens.</p> </div> </div> </div>
Dataset for: Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks
<p>Datasets for the paper Synthetic Micrographs of Bacteria (SyMBac) Allows Accurate Segmentation of Bacterial Cells Using Deep Neural Networks, published in BMC Biology.</p>
Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor
<p>These are data related to the manuscript titled "Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor" whose preprint can be found here: https://doi.org/10.1101/2022.05.03.490515</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.