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111,247 results for “cells”
Figure 2 in Cell damage induced by polybrominated diphenyl ethers on Chironomus sancticaroli (Diptera: Chironomidae)
Figure 2. Micrographs of larval Chironomus sancticaroli midgut after acute exposition to PBDEs A. Region I midgut lumen belonging to control treatment (water). B. Region I midgut lumen exposed to BDE-99 at 0.5 Μg L-1 exhibiting its narrowing. C. Epithelial cells of midgut, region I, exposed to BDE 47 at 0.5 Μg L-1 showing changes in cell morphology.D Midgut cells, region II, of control treatment (water).E Midgut cells, region II, exposed to BDE-47 at 2.0 Μg L-1 showing the cytoplasm vacuolization. Bars shows the width of the lumen.gc: gastric cecum. ep: gut epithelia. lu: lumen. sv: stomodeal valve. Scale bar: 20 Μm.
Figure 3 in Cell damage induced by polybrominated diphenyl ethers on Chironomus sancticaroli (Diptera: Chironomidae)
Figure 3. Micrographs of the salivary gland of larval Chironomus sancticaroli after acute exposition to PBDEs A. Salivary gland of control treatment (water). B. Salivary gland exposed to BDE 47 at 0.5 Μg L-1 showing formation of the cytoplasm acidophilic granules. C. Salivary gland exposed to BDE 17 at 20 Μg L-1 shows formation of the cytoplasm acidophilic granules. Arrow. Scale bar: 20 Μm.
Figure 1 in Cell damage induced by polybrominated diphenyl ethers on Chironomus sancticaroli (Diptera: Chironomidae)
Figure 1. Micrographs of oenocytes and Cuenot cells ofChironomus sancticaroli larvae after PBDE exposure.A. Oenocytes of control (water).B. Oenocytes exposed to BDE-17 at 20 Μg L-1. C. Oenocytes exposed to BDE-47 at 2.0 Μg L-1. Arrows show cytoplasm vacuolization. D. Cuénot cells of control (water). E. Cuénot cells exposed to BDE 17 at 20 Μg L-1. F. Cuenot cells exposed to BDE 47 at 2.0 ug L-1. G. Cuenot cells exposed to BDE 99 at 0.5 Μg L-1. Arrows show the brush border disruption and displacement in the apex cell. tr: trophocytes, cu: cutile, cc: Cuénot cells, oe: esophagus, cg: gastric caeca. Scale bar: 20 Μm.
Discrete Flow Cytometry of Underway Samples from Gradients 5 (2023) Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 µm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Data used in the study "mRNA vaccination boosts spike-specific T cell memory and promotes expansion of CD45RAint TEMRA-like CD8+ T cells in COVID-19 recovered individuals"
<p>Data associated with an original research study examining T cell responses to mRNA vaccination in COVID-19 recovered individuals. 10X Cell Ranger outputs, bulk TCR sequencing data, and T cell functional (ICS) data in this study have been deposited. Authors KMB and HR contributed equally to this effort. Address correspondence to EWN.</p>
Spatial organization of the mouse retina at single cell resolution by MERFISH
<p><strong>Abstract:</strong><br> <br> The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina1, little is known about their spatial organization. Here, we generated the first single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the first connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.<br> <br> <strong>Data description:</strong><br> 1. VZA105a_integrated_368genes.h5ad<br> This file contains the raw MERFISH count matrix for four samples with 368 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.</p> <p><br> 2. VA45_integrated.h5ad<br> This file contains the raw MERFISH count matrix for six samples with 500 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> <br> 3. merfish_impute.h5ad<br> This file contains the imputed count matrix for ten samples. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> </p>
Fig 2 in Soluble total antigen derived from Toxoplasma gondii RH strain prevents apoptosis, but induces anti-apoptosis in human monocyte cell line
Fig 2. This schematic overview suggests signaling pathways of apoptosis. Apoptosis is induced through either the extrinsic pathway or intrinsic pathway. Toxoplasma gondii can prevent cell apoptosis via inhibition of cytochrome c release from the mitochondria into the cytosol, inhibition of cas-3, -6, -7, and -9 via blocking the mitochondrial pathway, and blocking cas-8 by external pathways. The parasite may also suppress pro-apoptotic protein (Bax) and induce anti-apoptotic Bcl-2 proteins and is able to activate transcription factors STAT6 and STAT3-miR-17-92-Bim pathway to inhibit apoptosis. Some proteins of T. gondii such as ROP18 plays a role in Fas/ CD95-mediated apoptosis to inhibit this process. Similarly ROP16 phosphorylates STAT3 and STAT6 to inhibit cell death. Original (S. Nemati).
Fig 1 in Soluble total antigen derived from Toxoplasma gondii RH strain prevents apoptosis, but induces anti-apoptosis in human monocyte cell line
Fig 1. The comparison of the expression levels of Bcl-2 (A), Mcl-1 (B), Cas-7 (C), Cas-3 (D), and Bax (E) in THP-1 cell line regarding different concentrations of the soluble total antigen of Toxoplasma gondii (Nicolle et Manceaux, 1908) STAg. NS: not significant; * P value <0.05; ** P value <0.01; *** P value <0.001.
Figure 3 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 3. Effect of D. marginatus SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. All experiments have the P value <0.05, on three different time laps. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Figure 2 in The study of exposure times and dose-escalation of tick saliva on mouse embryonic stem cell proliferation
Figure 2. Effect of R. bursa SGE (0-160 µg/ml) on mouse embryonic stem cell proliferation and viability. Values represent relative fold change of cell viability normalized to untreated negative control. The Geisser–Greenhouse correction and Dunnett´s test on multiple comparison were used. The results are mean ± standard deviation (SD) from a representative experiment carried out in triplicate and were seeded in equal amount in 3 different 96-well cultured plates.
Tissue-specific features of the T cell repertoire following allogeneic hematopoietic cell transplantation in human and mouse
<p>T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5-18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, as well as in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleen in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end-stage of an inflammatory disorder following lymphocyte-directed therapy. These findings also underscore the importance of studying T cells in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and post-transplant T cell landscape.</p>
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 4 (2021) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2021 (Gradients 4/TN397) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Perovskite Solar Cells Ageing Dataset
<p>This dataset contains cleaned 2,245 ageing test traces (time vs. MPPT PCE/ maximum power point tracking power conversion efficiency) for perovskite solar cells with various device stacks and architectures in the pickle (.pkl) format.</p> <p>The dataset can be loaded with the following commands on Python.</p> <pre><code class="language-python">import pickle5 as pickle import pandas as pd import numpy as np with open('20230303_mySeriesDrop.pkl', "rb") as fh: mySeriesDrop = pickle.load(fh)</code></pre> <p>The following command can be used to call a specific row (row 0) within the dataset.</p> <pre><code class="language-python">mySeriesDrop[0]</code></pre> <p>The next steps to use the dataset is using scaling/ normalisation (for instance using sklearn.preprocessing.MaxAbsScaler) and smoothing (for instance using Savitzky-Golay filter).</p> <p>The code to run the complete analysis, including self-organising map clustering, can be accessed here: <a href="https://doi.org/10.5281/zenodo.8181602">https://doi.org/10.5281/zenodo.8181602</a>.</p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Data and code for Symbiotic bacteria, immune-like sentinel cells, and the response to pathogens in a social amoeba
<p>This repository contains data and code to do the analyses in the paper "Symbiotic bacteria, immune-like sentinel cells, and the response to pathogens in a social amoeba" published in Royal Society Open Science. The paper investigates how infection by <em>Paraburkholderia</em> symbionts affects host <em>Dictyostelium discoideum</em>'s immune function.</p>
Data for investigation of cell-type-specific expression and regulation in Setbp1 (p.S858R) atypical SGS male mice
<p><strong>processed.tar.gz, contains all files from the data directory associated with this projects and includes the following:</strong></p> <ul> <li><strong>setbp1_targets.csv</strong> : list of human SETP1 gene targets from ChIP-seq experiments converted to mouse and enriched for additional interactions from literature references (https://pubmed.ncbi.nlm.nih.gov/35685777/ and https://www.nature.com/articles/s41467-018-04462-8, </li> <li><strong>seurat_objects/</strong> <ul> <li><strong>kidney_integrated_celltypes.Rdata </strong>: filtered and preprocessed seurat object with cell type assignments for input to SoupX processing</li> <li><strong>kidney_integrated_celltypes_postSoup.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments after SoupX processing</li> <li><strong>setbp1_cerebralintcelltypes.Rdata</strong> : preprocessed and quality controlled seurat object with final cell type assignments</li> </ul> </li> <li><strong>decoupleR_CollecTRI/</strong> <ul> <li><strong>mouse_prior_tri.csv</strong> : accessed May 2023, it is unclear how versioning or accessions are used so we provided the Mouse CollecTRI (https://github.com/saezlab/CollecTRI) used in this study to calculate cell-type-specific TF activity</li> </ul> </li> <li><strong>motif_inputs/</strong> <ul> <li><strong>Mus_motif_all.txt </strong>: TF-motif PANDA input for mm10 reference genome enriched for</li> </ul> </li> <li><strong>expression_inputs/</strong> : expression matrices for all cell types for both tissues and conditions (n = 50) used to construct cell-type-specific PANDA networks</li> </ul> <p><strong>The below files are from the data/results directory of this associated project and include the following:</strong></p> <ul> <li><strong>alpaca.tar.gz</strong> : directory containing differential community analysis outputs from ALPACA</li> <li><strong>seurat.tar.gz </strong>: directory containing SoupX intermediates, as well as the</li> </ul>
Measurement Data for CT of Battery Pouch Cell with Defects
<p>These are the raw radiography images used to calculate the CT volume of a battery pouch cell with defects.</p> <p> </p> <p>Measured on a diondo d2 CT (using XWT-190 CT, Varex 4343DX-I) at TU Dresden.</p> <p>Tube: 150 KV, 100 µA</p> <p>Geometry: FOD 160 mm, FDD 1000 mm</p> <p>Detector: 3000 x 3000 px², 3000 Frames, 5x Framebinning, 3000 ms per Frame</p> <p>Data: 16bit unsigned integer, little endian</p> <p> </p> <p>The sample was prepared by Johannes Münch. For further details see the following paper:</p> <p><a href="https://doi.org/10.1002/ente.202300323">https://doi.org/10.1002/ente.202300323</a></p>
Therapeutic drug monitoring of pazopanib in renal cell carci-noma and soft tissue sarcoma – a systematic review_Primary DATA
<p>Primary dataset of the search made for the systematic review: A systematic search of PubMed and Web of Science databases was conducted using search terms related to pazopanib and TDM. Out of 162 and 225 articles identified, respectively, nine articles were selected for review, as they evaluated treatment outcomes or toxicity concerning drug exposure</p>
Protective role of Pyrroloquinoline quinone against gentamicin induced cochlear hair cell ototoxicity
<p><strong>Abstract:</strong> Gentamicin(GM) is one of the commonly used antibiotics in the aminoglycoside class but ototoxicity as a side effect constantly impacts the quality of human life. Pyrroloquinoline quinone (PQQ) as a redox cofactor produced by bacteria was found in soil and foods that exert an antioxidant and redox modulator. It is well documented that the PQQ can alleviate inflammatory responses and cytotoxicity. However, our understanding of PQQ in ototoxicity remains unclear. We reported that PQQ could protect against GM-induced ototoxicity in House Ear Institute-Organ of Corti 1 (HEI-OC1) cells<em> in vitro</em>. To evaluate reactive oxygen species production and mitochondrial function, ROS and JC-1 staining, oxygen consumption rate (OCR), and extracellular acidification rate (ECAR) measurements in living cells, mitochondrial dynamics analysis was performed. GM-mediated damage by reducing the production of ROS and inhibiting mitochondria biogenesis and dynamics. PQQ ameliorated the cellular oxidative stress, and recovered mitochondrial membrane potential, facilitating the recovery of mitochondrial biogenesis and dynamics. Our <em>in vitro</em> findings improve our understanding of GM-induced ototoxicity with therapeutic implications for PQQ.</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.