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1,237 results for “cell characterization”
Data from: Characterization of direct Purkinje cell outputs to the brainstem
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Global Characterization of Megakaryocytes in Bone Marrow, Peripheral Blood, and Cord Blood by Single-cell RNA Sequencing
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Characterization and mutagenesis of Chinese hamster ovary cells endogenous retroviruses to inactivate viral particle release
<p>The Chinese hamster ovary (CHO) cells used to produce biopharmaceutical proteins are known to contain type‐C endogenous retrovirus (ERV) sequences in their genome and to release retroviral‐like particles. Although evidence for their infectivity is missing, this has raised safety concerns. As the genomic origin of these particles remained unclear, we characterized type‐C ERV elements at the genome, transcriptome, and viral particle RNA levels. We identified 173 type‐C ERV sequences clustering into three functionally conserved groups. Transcripts from one type‐C ERV group were full‐ length, with intact open reading frames, and cognate viral genome RNA was loaded into retroviral‐like particles, suggesting that this ERV group may produce functional viruses. CRISPR‐Cas9 genome editing was used to disrupt the gag gene of the expressed type‐C ERV group. Comparison of CRISPR‐derived mutations at the DNA and RNA level led to the identification of a single ERV as the main source of the release of RNA‐loaded viral particles. Clones bearing a Gag loss‐of‐function mutation in this ERV showed a reduction of RNA‐containing viral particle release down to detection limits, without compromising cell growth or therapeutic protein production. Overall, our study provides a strategy to mitigate potential viral particle contaminations resulting from ERVs during biopharmaceutical manufacturing.</p>
Isolation and characterization of cell wall and extracellular polysaccharides from cultures of the mycoparasitic strain Tirochoderma koningiopsis
<p>The results obtained are part of the Miniatura 6 (2022/06/X/NZ9/00569) project</p>
Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology
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Characterization of the semi-transparent perovskite solar cells
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Integration of single-cell RNA-sequencing data across tissues and cancer types towards immune cell characterization
<p>To better understand dendritic cell states and subtypes, we collected individual single-cell RNAseq datasets from various studies and further integrated, batch corrected, and reprocessed the data using Besca (https://github.com/bedapub/besca).</p> <p>The following files are included:<br> 1) study_table_integrated_DCs.xlsx - contains a list of studies from where the datasets were gathered.<br> 2) int_dcs.raw.h5ad - An anndata object file containing the combined raw single-cell counts for DCs from individual studies. The datasets were joined based on the union of variables.<br> 3) intersection_genes_integrated_dcs.tsv - List of genes if the datasets were joined based on the intersection of variables. These variables were used in the subsequent analyses.</p> <p>4) int_dcs.annotated.h5ad - An anndata object file containing single-cell logarithmized counts for DCs data that have been integrated and reprocessed. The rows of the file contain cells, and the columns contain highly variable genes. A sparse matrix containing the logarithmized counts from all the genes (from the intersection genes integrated dcs.tsv file) can also be found (adata.raw.X) in the object. In the observations, cell-type annotation is available at three different hierarchal levels.<br> <br> This data was further used to produce results for the publication (https://jitc.bmj.com/content/10/6/e004268) on the effects of Toll-like receptor 8 agonists on conventional DCs.</p>
CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing
<p><strong>Supplementary dataset for the manuscript:</strong></p> <p><strong>CRAG: De novo characterization of cell-free DNA fragmentation hotspots in plasma whole-genome sequencing</strong></p> <p> Xionghui Zhou1,*, Haizi Zheng1,*, Hailu Fu1,*, Kelsey L. Dillehay McKillip2-3, Susan M. Pinney2,4, Yaping Liu1-2,5-7 #</p> <p>Affiliations:</p> <p>1 Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229</p> <p>2 University of Cincinnati Cancer Center, Cincinnati, OH 45229</p> <p>3 Department of Pathology & Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>4 Department of Environmental and Public Health Sciences, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>5 Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229</p> <p>6 Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229</p> <p>7 Department of Electrical Engineering and Computing Sciences, University of Cincinnati College of Engineering and Applied Science, Cincinnati, OH 45229</p> <p>* These authors contributed equally</p> <p># Email: lyping1986@gmail.com</p>
Bacterial cell surface characterization by phage display coupled to high-throughput sequencing
<p>This record contains the processed high-throughput sequencing data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing." Data was generated using the <a href="https://github.com/caseygrun/phage-seq">Snakemake workflows and Jupyter notebooks in this repository</a> and is intended to be analyzed further using the notebooks in that repository</p> <p>Each tarball within this record, when expanded, populates the <code>results</code> and <code>intermediate</code> directories of one of those workflows: <code>alpaca-library</code> ,<code>panning-small</code>, <code>panning-massive</code>, or <code>panning-extended</code>. Clone the <a href="https://github.com/caseygrun/phage-seq"><code>phage-seq</code> repository</a>, then download one or more of these tarballs to the corresponding directory of that directory. For example:</p> <blockquote> <pre><code>git clone https://github.com/caseygrun/phage-seq.git cd panning-extended wget https://zenodo.org/records/11246658/files/panning-extended-results.tar.gz tar vzxf panning-extended-results.tar.gz</code></pre> </blockquote> <p>More detailed instructions are included in the README for the <a href="https://github.com/caseygrun/phage-seq"><code>phage-seq</code> repository</a>.</p>
Fig. 7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 7. Agarose gel electrophoresis of MCF-7 cell genomic DNA. 1, ladder marker; 2, negative control; 3, cyclophosphamide (CP)-treated; 4–7, ethanolic extracts of Mimosa caesalpiniifolia leaves (EEM) in different concentrations (5.0; 20.0; 160.0 and 320.0 μg/mL). The results are representative of three independent experiments carried out in the same conditions. DNA ladder formation indicates apoptosis as seen in lanes 3–7.
Fig. 6. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 6. MCF-7 cell death (%) after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations (5.0–320.0 μg/mL) of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM) for 24 h, compared to the negative control cells (NC), estimated by the Fast green color dye exclusion. Results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.01) by the Tukey test. Inset: appearance of cells after fast green-hematoxylin–eosin staining; 1, living cell; 2, green dead cell. EEM treatment kills MCF-7 cells in a concentrationdependent manner.
Fig. 3 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 3. Morphology of MCF-7 cells stained with hematoxylin–eosin, after 24 and 48 h incubation. NC, negative control cells; CP, cells treated with 550 μg/mL cyclophosphamide; 5, 80 and 320, cells treated, respectively, with 5.0, 80.0 and 320.0 μg/mL ethanolic extract of Mimosa caenalpiniifolia leaves. All the pictures are typical of three independent experiments, each carried out under identical conditions. Bar = 5 μm. Arrow—nucleolus; R—rounding; CC—chromatin condensation.
Fig. 2. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 2. MCF-7 cell protein content decreasing (%), estimated by the sulforhodamine B assay, after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM 5.0 - 320.0 μg/mL) for 24 and 48 h. The results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.001) by the Tukey test. Protein content was calculated relative to the negative control and 320.0 μg/mL EEM produced the maximum effect.
Fig. 1 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 1. HPLC-DAD-ESI-MS analysis of the ethanolic extract of Mimosa caesalpiniifolia leaves. (A) UV 360 nm; (B) ESI-MS, base peak chromatogram, negative ion mode, m/z 100–1500. No additional peaks were detected when the UV trace was recorded at wavelengths down to 360 nm. Peaks assigned as m/z: 288.97, 318.00, and 576.77 were identified, respectively, as catechin, 2,3 dihydroquercetagetin, and procyanidin B2 [(epi)catechin–(epi)catechin)] (see structures).
Fig. 4. MCF-7 in Ethanolic extract of Mimosa caesalpiniifolia leaves: Chemical characterization and cytotoxic effect on human breast cancer MCF-7 cell line
Fig. 4. MCF-7 cell diameter (μm) after treatment with cyclophosphamide (CP, 550 μg/mL) and different concentrations (5.0–320.0 μg/mL) of the ethanolic extract of Mimosa caesalpiniifolia leaves (EEM) for 24 or 48 h, compared to the negative control cells (NC). Results are expressed as mean ± SEM of three independent experiments. Different letters indicate significant differences (p <0.01) by the Tukey test. Note cell-diameter reduction after treatment, in comparison to the negative control cells, thereby indicating EEM cytotoxicity.
Metadata for the characterization of Platynereis dumerilii cephalic and non-cephalic sensory cell types
<p>Rhabdomeric Opsins (r-Opsins) serve as light-sensory molecules in cephalic eye photoreceptors of many invertebrates, but also play roles in additional sensory organs. This has prompted questions on the evolutionary relationship of the cell types, specifically if ancient r-Opsins cells possessed non-photosensory functions. By extracting and profiling cephalic and non-cephalic <i>r-opsin1</i>-expressing cell types of the marine bristleworm <i>Platynereis dumerilii</i>, we find evidence for shared and distinct identities between these two cell types. We establish that non-cephalic cells possess a mechanosensory signature, but also a full set of phototransduction components. Using cell-culture-based second messenger assays, we determine that <i>Platynereis</i> r-Opsin1 is a Gα<sub>q</sub>-coupled blue-light receptor. Profiling of cells in <i>r-opsin1</i> mutant animals, and comparing cells under dim and bright light conditions reveals that in the non-cephalic cell type, light – mediated by r-Opsin1 – adjusts the expression level of a calcium transporter relevant for auditory mechanosensation in vertebrates. We finally establish a supervised, deep learning-based quantitative behavioral analysis pipeline to assess animal trunk movements. We identify light-dependent differences in the fine-tuning of the worms' undulatory movements in mutant <i>vs.</i> wildtype headless trunks, which are known to require mechanosensory feedback. Altogether, our results provide support for an evolutionary concept in which r-Opsins act as ancient, light-dependent modulators of mechanosensation. Our findings refine the reconstruction of sensory cell type evolution, and suggest that light-independent mechanosensory roles of r-Opsins likely result from secondary evolutionary processes.</p>
Figure Data for Characterization of Thunderstorm Cells Producing Observable Terrestrial Gamma-ray Flashes
<p>This dataset contains the data required to recreate the figures from the paper Characterization of Thunderstorm Cells Producing Observable Terrestrial Gamma-ray Flashes. Each csv file contains the data for the corresponding instrument</p>
Integration of imaging modalities with lipidomic characterization to investigate single cell MSCs potency metrics
<p>This dataset contains images used in the analysis for the publication mentioned above.</p>
Characterization of Clonal B Cell Populations in HCV Infection
ClinicalTrials.gov study NCT00435201. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Characterization of Circulating and Tumor-infiltrating Immune Cells in Malignant Brain Tumors
ClinicalTrials.gov study NCT05831631. IPD Sharing: NO. Countries: 1. Publications: 14.
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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.