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111,247 results for “cells”
Data from: Coordinated ARP2/3 and glycolytic activities regulate the morphological and functional fitness of human CD8+ T cells
<p>CD8<sup>+</sup> T cells rely on actin cytoskeleton remodeling to search for target cells and assemble the immunological synapse (IS) for lethal hit delivery. We here investigated how the energy expenditure related to actin remodeling might influence the fitness of human cytotoxic T cells. We first established that the spreading ability of CD8<sup>+</sup> T cells in conditions of LFA-1 and TCR engagement mirrored the cytotoxic potential of these cells. Morphological and functional fitness were both potentiated by IL-2, which co-stimulated the transcription of glycolytic enzymes, actin isoforms and the subunits of the ARP2/3 complex. This molecular program scaled with F-actin content and cell spreading. Blockade of glycolysis impaired F-actin remodeling at the lamellipodium, chemokine-driven motility and synaptic adhesion, while blockade of mitochondrial OXPHOS affected F-actin less severely and selectively reduced cell elongation during confined migration. Although T cells deficient for the ARP2/3 subunit ARPC1B increased their ATP content upon IL-2 exposure, their morphological and functional defects were only partially corrected, pointing to the pivotal position of ARP2/3 mediated actin polymerization as integrator of T cell energetic state. Our study therefore highlights that the ability of effector T cells to migrate, form IS and ultimately kill target cells depends on a tight coordination of their metabolic and actin remodeling activities.</p>
Supplemental information for: Inhibition of CSF1R and KIT with pexidartinib reduces inflammatory signaling and cell viability in endometriosis
<p>Endometriosis is a common and debilitating disease, affecting ~170 million women worldwide. Affected patients have limited therapeutic options such as hormonal suppression or surgical excision of the lesions, though therapies are often not completely curative. Targeting receptor tyrosine-kinases (RTKs) could provide a nonhormonal treatment option for endometriosis. We determined that two RTKs, Macrophage colony stimulating factor receptor (CSF1R) and Mast/stem cell growth factor receptor KIT (KIT), are overexpressed in endometriotic lesions and could be novel nonhormonal therapeutic targets for endometriosis. The kinase activity of CSF1R and KIT is suppressed by pexidartinib, a small molecule inhibitor that was recently approved by the US Food and Drug Administration (FDA). Using immunohistochemistry, we detected CSF1R and KIT in endometriotic tissues obtained from peritoneal lesions, colorectal lesions, and endometriomas. Specifically, we show that KIT is localized to the epithelium of the lesions, while CSF1R is expressed in the stroma and macrophages of the endometriotic lesions. Given the high epithelial expression of CSF1R and KIT, 12Z endometriotic epithelial cells were used to evaluate the efficacy of dual CSF1R and KIT inhibition with pexidartinib. We found that pexidartinib suppressed activation in 12Z cells of JNK, STAT3 and AKT signaling pathways, which control key pro-inflammatory and survival networks within the cell. Using quantitative real time PCR, we determined that pexidartinib suppressed interleukin 8 (<em>IL8) </em>and cyclin D1 (<em>CCND1) </em>expression<em>.</em> Lastly, we demonstrated that pexidartinib decreased cell growth and viability.<em> </em>Overall, these results indicate that pexidartinib-mediated CSF1R and KIT inhibition reduces pro-inflammatory signaling and cell viability in endometriosis.</p>
Cell-type-specific mRNA transcription and degradation kinetics in zebrafish embryogenesis from metabolically labeled scRNAseq
<p><span>During embryonic development, pluripotent cells assume specialized identities by adopting particular gene expression profiles. However, systematically dissecting the relative contributions of mRNA transcription and degradation to shaping those profiles remains challenging, especially within embryos with diverse cellular identities.<span> Here, we </span>combine<span> </span>single-cell RNA-Seq and metabolic labeling to capture temporal cellular transcriptomes of zebrafish embryos where newly-transcribed (zygotic) and pre-existing (maternal) mRNA can be distinguished. We then introduce kinetic models to quantify mRNA transcription and degradation rates within individual cell types during their specification. These models reveal highly varied regulatory rates across thousands of genes, coordinated transcription and destruction rates for many transcripts, and link differences in degradation to specific sequence elements. They also identify cell-type-specific differences in degradation, namely selective retention of maternal transcripts within primordial germ cells and enveloping layer cells, two of the earliest specified cell-types. Our study provides a quantitative approach to study mRNA regulation during</span> a dynamic spatio-temporal response<span>.</span></p> <p> </p> <p>This repository contains the raw microscopy data that is analyzed in Figures 6F-I and Supplementary Figure S4 B-D.</p>
Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models
<p>Data and pipelines used to predict mechanistic subtypes of Parkinson's disease using patient-derived stem cell model.</p> <p><strong>Lists of files included;</strong></p> <ul> <li>chemPredPD2022_Imaging process pipelines: pipelines to extract tabular data in Columbus Image Data Storage and Analysis System</li> <li>demo_data_images: a set of data for the Demo</li> <li>ImageData</li> <li>TabularData</li> <li>New test data_PINK1_isoCTRL</li> <li>New test data_SNCA_isoCTRL</li> <li>Tabular_demo_data</li> </ul>
Cell Monolayer Friction Data 1
<p>In this work an improved test bed to simulate tissue including an endothelial cell (HUVEC) monolayer was developed and tested in a sliding friction test using different probe materials.</p>
Cell Monolayer Friction Data 2
<p>In this work an improved test bed to simulate tissue including an endothelial cell (HUVEC) monolayer was developed and tested in a sliding friction test using different probe materials.</p>
RNA-sequencing of meningioma cell lines with miRNA constructs
<p>RNA-sequencing of meningioma cell lines with miRNA constructs.</p>
Profiling of linear B-cell epitopes against human coronaviruses in pooled sera sampled early in the COVID-19 pandemic
<p>Background: Antibodies play a key role in the immune defence against infectious pathogens. Understanding the underlying process of B cell recognition is not only of fundamental interest; it supports important applications within diagnostics and therapeutics. Whereas the nature of conformational B cell epitope recognition is inherently complicated, linear B cell epitopes offer a straightforward approach that potentially can be reduced to one of peptide recognition.</p> <p>Methods: Using an overlapping peptide approach representing the entire proteomes of the seven main coronaviruses known to infect humans, we analysed sera pooled from eight PCR-confirmed COVID-19 convalescents and eight pre-pandemic controls. Using a high-density peptide microarray platform, 13-mer peptides overlapping by 11 amino acids were in situ synthesised and incubated with the pooled primary serum samples, followed by development with secondary fluorochrome-labelled anti-IgG and -IgA antibodies. Interactions were detected by fluorescence detection. Strong Ig interactions encompassing consecutive peptides were considered to represent "high-fidelity regions" (HFRs). These were mapped to the coronavirus proteomes using a 60% homology threshold for clustering.</p> <p>Results: We identified 333 human coronavirus derived HFRs. Among these, 98 (29%) mapped to SARS-CoV-2, 144 (44%) mapped to one or more of the four circulating common cold coronaviruses (CCC), and 54 (16%) cross-mapped to both SARS-CoV-2 and CCCs. The remaining 37 (11%) mapped to either SARS-CoV or MERS-CoV. Notably, the COVID-19 serum was skewed towards recognising SARS-CoV-2-mapped HFRs, whereas the pre-pandemic was skewed towards recognising CCC-mapped HFRs. In terms of absolute numbers of linear B cell epitopes, the primary targets are the ORF1ab protein (60%), the spike protein (21%), and the nucleoprotein (15%) in that order; however, in terms of epitope density, the order would be reversed.</p> <p>Conclusion: We identified linear B cell epitopes across coronaviruses, highlighting pan-, alpha-, beta-, or SARS-CoV-2-corona-specific B cell recognition patterns. These findings could be pivotal in deciphering past and current exposures to epidemic and endemic coronavirus. Moreover, our results suggest that pre-pandemic anti-CCC antibodies may cross-react against SARS-CoV-2, which could explain the highly variable outcome of COVID-19. Finally, the methodology used here offers a rapid and comprehensive approach to high-resolution linear B-cell epitope mapping, which could be vital for future studies of emerging infectious diseases.</p>
Data supporting 'Small molecule and cell contact-inducible systems for controlling expression and differentiation in stem cells'
<p>Data supporting Soliman et al. 2024. Manuscript describes data collection practices and experimental design.</p>
The VSMC RNA-seq datasets for "Strategies for Arterial Grafts Optimization at Single Cell Level"
<p>This dataset collection contains the human vascular smooth muscle cell (VSMC) bulk RNA-seq data generated in the study <strong><em>Strategies for Arterial Grafts Optimization at Single Cell Level</em></strong>.</p> <p>Primary human VSMCs (CP-H167, Pricella) were cultured in DM/F12 medium supplemented with FBS, EGF, bFGF, insulin, and penicillin (CM-P167, Pricella). The media was replaced per 48 hours. VSMCs were treated with 100ng/ml MIF (HY-P7387, MCE) for 24 hours in the absence or presence of 50 µMol ISO1 (HY-16692, MCE). The VSMCs with the passage number 3 to 5 were used in the study.</p> <p>Total RNA was extracted from cultured VSMC under different treatments using RNeasy Mini Kit (74104, Qiagen). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. The index of the reference genome was built using Hisat2 v2.0.5. and paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5.</p> <p>The sample info for the BAM files and their index files are listed as follows:</p> <table> <tbody> <tr> <td><strong>SampleCode</strong></td> <td><strong>Sample Description</strong></td> </tr> <tr> <td>C1</td> <td>VSMC (replicate 1)</td> </tr> <tr> <td>C2</td> <td>VSMC (replicate 2)</td> </tr> <tr> <td>C3</td> <td>VSMC (replicate 3)</td> </tr> <tr> <td>M1</td> <td>VSMC treated with MIF (replicate 1)</td> </tr> <tr> <td>M2</td> <td>VSMC treated with MIF (replicate 2)</td> </tr> <tr> <td>M3</td> <td>VSMC treated with MIF (replicate 3)</td> </tr> <tr> <td>M_I1</td> <td>VSMC treated with MIF and ISO1 (replicate 1)</td> </tr> <tr> <td>M_I2</td> <td>VSMC treated with MIF and ISO1 (replicate 2)</td> </tr> <tr> <td>M_I3</td> <td>VSMC treated with MIF and ISO1 (replicate 3)</td> </tr> </tbody> </table>
Enhancing comparative T-cell receptor repertoire analysis in small biological samples through pooling homologous cell samples from multiple mice
<p>All data files used to generate the figures in the paper are shared in this project.</p> <p>Scripts are available on <a href="https://github.com/i3-unit/CRM_24" target="_blank" rel="noopener">GitHub</a>.</p>
Data from: "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells"
<h1><strong>Intro</strong></h1> <p>Dataset from the publication "Lithium-ion battery degradation: comprehensive cycle ageing data and analysis for commercial 21700 cells", DOI: https://doi.org/10.1016/j.jpowsour.2024.234185</p> <p>Full details of the study can be found in the publication, including thorough descriptions of the experimental methods and structure. A basic desciption of the experimental procedure and data structure is included here for ease of use.</p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 4 different SoC ranges [0-30, 70-85, 85-100, 0-100]%, as well as a further [0-100]% SoC range experiment which utilised a drive-cycle discharge instead of constant-current. The same C-rates (0.3C / 1 C, for charge / discharge) were used in all tests; multiple cells were tested under each condition. These are listed in the table below.</p> <table> <tbody> <tr> <td> <div> <p><strong>Experiment</strong></p> </div> </td> <td> <div> <p><strong>SOC Window</strong></p> </div> </td> <td> <div> <p><strong>Cycles per ageing set</strong></p> </div> </td> <td> <div> <p><strong>Current</strong></p> </div> </td> <td> <div> <p><strong>Temperature</strong></p> </div> </td> <td> <div> <p><strong>Number of Cells</strong></p> </div> </td> </tr> <tr> <td> <div> <p>1</p> </div> </td> <td> <div> <p>0-30%</p> </div> </td> <td> <div> <p>257</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p> </p> </div> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>2,2</p> </div> </td> <td> <div> <p>70-85%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> <div> <p>3</p> </div> </td> <td> <div> <p>85-100%</p> </div> </td> <td> <div> <p>515</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>4</p> </div> </td> <td> <div> <p>0-100% (drive-cycle)</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / noisy D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> <div> <p>5</p> </div> </td> <td> <div> <p>0-100%</p> </div> </td> <td> <div> <p>78</p> </div> </td> <td> <div> <p>0.3C / 1D</p> </div> </td> <td> <div> <p>10°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>25°C</p> </div> </td> <td> <div> <p>2</p> </div> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td> <div> <p>40°C</p> </div> </td> <td> <div> <p>3</p> </div> </td> </tr> </tbody> </table> <p>Cells were base-cooled at set temperatures using bespoke test rigs (see our linked publications for details; the supporting information file contains detailed descriptions and photographs). Cells were subject to break-in cycles prior to beginning of life (BoL) performance tests using the ‘Reference Performance Test’ (RPT) procedures. They were then alternately subject to ageing sets and RPTs until the end of testing. Full details of each of these procedures are described in the linked publication.</p> <p>The data contained in this repository is then described in the Data section below. This includes a description of the folder structure and naming conventions, file formats, and data analysis methods used for the ‘Processed Data’ which has been calculated from the raw data.</p> <p>An 'experimental_metadata' .xlsx file is included to aid parsing of data. A jupyter notebook has also been included to demonstate how to access some of the data.</p> <h1>Data</h1> <p>Data are organised according to their parent ‘Experiment’, as defined above, with a folder for each. Within each Experiment folder, there are 3 subfolders: ‘Summary Data’, ‘Processed Timeseries Data’, and ‘Raw Data’.</p> <h2>Summary Data</h2> <p>This folder contains data which has been extracted by processing the raw data in the ‘Degradation Cycling’ and ‘Performance Checks’ folders. In most cases, the data you are looking for will be stored here.</p> <p>It contains: </p> <h3>Performance Summary</h3> <p>A summary file for each cell which details key ageing metrics such as number of ageing cycles, charge throughput, cell capacity, resistance, and degradation mode analysis results. Each row of data corresponds to a different SoH.</p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge. See our <a href="https://doi.org/10.1021/acsaem.2c02047">ACS publication</a> for more details.</p> <p>Data includes:</p> <p>· Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>· Ageing Cycles: number of ageing cycles the cell has been subject to. *this is not equivalent full cycles.</p> <p>· Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>· Days of degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>· Age set average temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps).</p> <p>· Charge throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cumulative total since BoL (not including RPTs, and not including break-in cycles).</p> <p>· Energy throughput: as with "charge throughput", but for energy.</p> <p>· C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT.</p> <p>· C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT.</p> <p>· 0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition).</p> <p>· Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower SoCs of each electrode, and the capacity fraction of graphite in the negative electrode.</p> <p>· Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>· DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>· RMSE data: the root mean squared error of the optimisation function calculated from the residual between the measured and simulated voltage vs capacity profiles.</p> <h3>Ageing Sets Summary</h3> <p>Data from the ageing cycles, summarised on an average per cycle and an average per ageing set basis. Metrics include mean/ max/ min temperatures, voltages etc.</p> <h2>Processed Timeseries data</h2> <p>Timeseries data (voltage, current, temperature, etc.) from each subtest (pOCV, GITT, etc.) of the RPTs, all grouped by subtest-type and by cell ID.</p> <p>Contains the same data as in the ‘Performance Checks’ subfolder of the 'Raw Data' folder, but has been processed to slice into relevant subtests from the RPT procedure and includes only limited variables (time, voltage, current, charge, temperature). These are all saved as .csv files. In general this data will be easier to access than the raw data, but perhaps not as rich.</p> <h2>Raw Data</h2> <p>These are the raw data from the performance checks and from the degradation cycles themselves. The data from here has already been processed by me to get values of ‘energy throughput’, ‘charge throughput’, ‘average ageing temperature’, etc., which are all saved in the ‘Summary Data’ folder as described in the relevant section above.</p> <p>The data in the ‘Degradation Cycling’ folder are organised by ageing set (where an ageing set is a defined number of ageing cycles, as described in the paper). In theory, each cell should have one datafile in each ageing set subfolder. However, due to experimental issues, tests can sometimes be interrupted midway though, requiring the test to be subsequently resumed. In this case, there may be multiple datafiles for each cell in a given ageing set; during analysis, these should be concatenated according to the descriptor in the filename (e.g., ‘cycling7’ + ‘cycling7 (part 2)').</p> <p>Similarly, the unprocessed raw data from the performance checks (i.e. RPTs) is stored in the 'Performance Checks' folder, and structured in the same way.</p> <p>The raw data are saved in the .mpr format produced by the Biologic battery cycler. This is a binary format which is storage-efficient but can be more difficult to process for analysis purposes. We have therefore also exported the data into .txt files (called .mpt) for the performance checks (RPTs) which make analysis easier. However, the exported .mpt files could not be included for the degradation cycling files due to their larger size. If you require access these degradation cycle data, the .mpr binary file can be parsed using the <a href="https://github.com/echemdata/galvani">Galvani</a> package in python, or you can use Biologic’s (proprietary) BT-Lab software to export the data into .txt files.</p> <h3>File Naming Convention</h3> <p>The raw datafiles are named with a standard format. This is:</p> <p> <em>NDK - LG M50 deg - exp 1 - rig 1 - 10degC - cell A - RPT1_01_MB_CB1</em></p> <p> {NDK - LG M50 deg} - {exp 1} – {rig 1} – {10degC} – {cell A} – {RPT1}_{01}_{MB}_{CB1}</p> <p>{Standard prefix} – {experiment number} – {ID of test rig} – {control temperature} – {Cell ID} – {RPT number <em>or</em> aging cycle number}_{step number for the characterisation procedure (see above)}_{experimental technique name (will always be “MB”)}_{battery cycler channel ID used (always the same for a particular cell/experiment)}</p> <p> </p>
Calcium dynamics upon heating in murine neonatal cardiomyocytes transduced with truncated human TRPV1(−109 a.a.) and the localization of TRPV1 in these cells
<h3>Abstract</h3> <p>The expression of truncated human TRPV1(−109 a.a.) in cardiomyocytes allowed us to induce action potentials (APs) by pulse irradiation with an infrared (IR) diode laser. We studied the calcium dynamics in these cells (Ca_TRPV.zip image set). Murine cardiomyocytes were transducted by AAV-based vectors bearing construction of TRPV1(−109 a.a.) with mRuby (for expression detection) and vectors bearing GCaMP6s calcium sensor. Cardiomyocytes were heated by IR laser with a repetition rate of 2 Hz and 30 ms pulse width in a calcium-free medium.<br>To explain the observed calcium dynamics, we also studied the subcellular localization of human TRPV1 in Murine cardiomyocytes ER_a-flag_100x_param1_Z-stack002.nd2</p> <h3>Primary cell production and transformation</h3> <p>Experiments were carried out using C57Bl/6J mice (The Jackson Laboratory, #000664, RRID: IMSR_JAX:000664). The mixed mouse primary neonatal cardiomyocyte cell culture was obtained using a neonatal heart dissociation kit (Miltenyi Biotec, 130-098-373) according to the manufacturer’s instructions. The cells were cultured in DMEM/F12, 1:1 mixture (BioloT, 1.3.7.2.) supplemented with 10% FBS, penicillin 100 U/ml /streptomycin 100 mg/ml, and L-glutamine 0.365 g/l. The culture was seeded on 10mm coverslips coated with 10 mg/ml gelatin diluted in PBS and maintained at 37℃ in 5% CO2. For transient expression of the hTRPV1 channel, a reporter protein, and a fluorescent Ca2+ sensor GCaMP6s, we used AAV-based vectors with the encoded genes above. We used the AAV-DJ serotype at a MOI of 12,000 VG/cells for cTnT_hTRPV1(sh)_P2A_mRuby based viruses, and a MOI of 2,500 VG/cells for cTnT_GCaMP6s ones. The cells were infected on the next day after plating, and the transgene expression peak was observed on the third day after the infection.</p> <h3>Distant heating system</h3> <p>The system was equipped with a fiber coupled laser diode (LD) 4PN-117 (SemiNex) as a powerful heating laser, providing radiation at a wavelength of 1375 nm with an average power of up to 4.3 W through a multimode fiber with a core diameter of 105 μm and 0.22NA. The LD was mounted onto a TEC-controlled plate “264 TEC HP LaserMount” (A.I.), which was operated by TEC driver TECSource 5305 (A.I.); current stabilization and control for LD were performed with LD driver LaserSource 4320 (A.I.). The laser was controlled via the TTL output from the HEKA EPC-10 amplifier. Different laser intensities and pulse widths were used. Laser power, P, can be computed from trigger voltage, U, by an equation: P [W] = -0.28 + 1.42 * U [V].</p> <p>Intracellular calcium recordings of single cardiac cells<br>Neonatal cardiomyocyte cells transduced with GCaMP6s sensor were viewed and acquired under a water immersion Olympus LUMPLFLN40×W objective with 40X magnification. Data acquisition was performed at 20 fps using a Scientifica SliceScopePro 2000 microscope (Scientifica, UK) equipped with a Hamamatsu Orca Flash 4.0 CMOS monochrome digital camera (Hamamatsu Photonics) connected to a PC running the free software uManager. A CoolLED pE-300ultra was used as a light source. It was synchronized with the laser heating system via BNC-TTL output from the Heka Elektronik EPC 10 USB Patch Clamp Amplifier. To synchronize the pacing and the GCamp6s signal registration, the light source was switched off for one acquisition cycle at the beginning of the pacing. The GCamp6s signal was analyzed using Fiji software.</p> <h3><br>TRPV1 channel localization</h3> <p>To understand the localization of the expressed TRPV1 channels in neonatal mice cardiomyocytes, we utilized cells infected with AAV-PHP.S serotype viruses with pAAV_cTnT_hTRPV1_P2A_FLAG-tag at a MOI of 2500 VG/cells. To visualize endoplasmic reticulum, we stained the live cells with ER-Tracker™ Red (BODIPY™ TR Glibenclamide, Thermo Fisher Scientific, E34250) according to the manufacturer’s instructions. After the staining with ER-tracker, cardiomyocytes were fixed with 4% paraformaldehyde (Sigma-Aldrich, 158127-100G) for 5 minutes at room temperature and washed trice with 0.3% Tween 20 (Sigma-Aldrich, P1379-250ML) diluted in PBS (5 minutes each). The fixed cells then were blocked with PBS containing 0.12% tween 20, 1% bovine serum albumin (BSA, PanEko, 68100.10г), and 10% goat serum (Thermo Fisher Scientific, 16210072) for 40 minutes at room temperature. After the buffer removal, the cardiomyocytes were labeled with of DYKDDDDK Tag Recombinant Rabbit Monoclonal Antibody (8H8L17, Invitrogen, MA1-142-A488) at 1:500 dilution in 1% BSA, 10% goat serum, and 89% PBS for 2 hours at room temperature. The samples were washed three times with PBS after the incubations. Then, the cells were stained with Goat anti-Rabbit IgG (H+L) Cross-Adsorbed Secondary Antibody Alexa Fluor™ 488 (Invitrogen, A-11008) at dilution 1:500 for 1 hour at room temperature. The removal of non-conjugated antibodies was performed in parallel with cell nuclei staining. The cells were incubated with PBS supplemented with 2 μg/ml DAPI (Miltenyi Biotec, 130-111-570) for 15 minutes at room temperature. For further experiments, glasses with the labeled cells were placed onto the Superfrost Plus adhesion slides (Epredia, EPBRSF41296SP) in 20 µl VECTASHIELD Vibrance Antifade Mounting Media (Vector Laboratories, H-1700-2) and stored at +4℃ in the dark.<br>The samples were analyzed using an inverted Nikon A1 confocal microscope and visualized using Nikon NIS-Elements software. We pictured the sample in each channel individually exciting DAPI, DYKDDDDK Tag-Alexa Fluor 488, and ER-tracker by laser lines 405 nm, 488 nm and 561 nm, respectively. Colocolization analysis was performed using ImageJ software.</p>
10K-Cell Subset of PBMC CITE-Seq Dataset for CITEViz
<p>This repository contains an example CITE-Seq data (10K peripheral blood mononuclear cells) to test the CITEViz program. The CITEViz preprint is available <a href="https://www.biorxiv.org/content/10.1101/2022.05.15.491411v1">here</a>, and the documentation website is located <a href="https://maxsonbraunlab.github.io/CITEViz/">here</a>. The original data underlying this article are available in GEO (Gene Expression Omnibus) at <a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>, and can be accessed with GSE164378. </p>
Data from: ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism detecting alteration of Septate Junction integrity in Drosophila epithelial cells
<p><span>Barrier functions of proliferative epithelia are constantly challenged by mechanical and chemical constraints. How epithelia respond to and cope with disturbances of barrier functions to allow tissue integrity maintenance is poorly characterized. Cellular junctions play an important role in this process and intracellular traffic contribute to their homeostasis. Here, we reveal that, in <em>Drosophila</em> pupal <em>notum</em>, alteration of the bi- or tricellular septate junctions (SJs) triggers a mechanism with two prominent outcomes. On one hand, there is an increase in the levels of E-cadherin, F-Actin and non-muscle Myosin II in the plane of adherens junctions. On </span><span>the other hand, β-integrin/Vinculin-positive cell contacts are reinforced along the lateral and basal membranes. We found that the weakening of SJ integrity, caused by the depletion of bi- or tricellular SJ components, alters ESCRT-III/Vps32/Shrub distribution, reduces degradation, and instead favours recycling of SJ components, an effect that extends to other recycled transmembrane protein cargoes including Crumbs, its effector β-Heavy Spectrin</span><span> Karst, and </span><span>β-integrin</span><span>. We propose a mechanism by which epithelial cells, upon sensing alterations of the septate junction</span><span>,</span><span> reroute the function of Shrub to adjust the balance of degradation/recycling of junctional cargoes and thereby compensate for barrier junction defects to maintain epithelial integrity.</span></p>
Polarisation camera movies of T cells on a cover glass
<p>The plasma membrane of live T cells (J8 LFA-1) was labelled using the Nile red-derivative NR4A, and 3D movies were recorded using a polarisation camera. Two unprocessed datasets are included here:</p> <ul> <li><strong>Tcell_NR4A_EPI_stack</strong>: A 3D stack of a single T cell, imaged with the excitation in EPI configuration.</li> <li><strong>Tcell_NR4A_HILO_stack_movie</strong>: A 3D movie of live T cells interacting with an OKT3 antibody-coated cover glass, imaged with the excitation in HILO configuration.</li> </ul> <p>For each .tif file, the corresponding Micro-Manager metadata file is included.</p> <p><strong>Optical setup:</strong></p> <p>Imaging was performed on a widefield microscope equipped with a polarisation camera (CS505MUP, Thorlabs). The sample was excited using a 515 nm laser, coupled into a square-core multi-mode fiber (M97L02, Thorlabs) with a custom vibration-motor-based mode scrambler. The measured power density at the image plane was on the order of 0.004 kW/cm^2. A dichroic (Di03-R514-t1, Semrock) was used to seperate fluorescence from the excitation. The emission was filtered using a long-pass filter (FF01-515/LP, Semrock) and bandpass filter (FF01-650/200, Semrock) before detection.</p> <p><strong>Sample preparation:</strong></p> <p>J8 LFA-1 cells were incubated overnight (~18 h) in complete-RPMI (StableCell RPMI-1640 media (Sigma) supplemented with 10 % (v/v) fetal calf serum (FCS), 1 % (v/v) HEPES buffer, and 1 % (v/v) pen/strep antibiotics. 1 mL of cells were collected by centrifugation and resuspended in phenol-red free RPMI supplemented with 1 % HEPES.</p> <p>Round coverslips were rinsed with IPA, MilliQ, dried, and Ar-plasma cleaned for 20 minutes. Grace Bio-Labs CultureWells were attached, and the slide was incubated with OKT3 antibody (provided by the Human Immunology Unit, WIMM, Oxford) for 30 minutes. The slide was washed 5 times with phenol-red free RPMI supplemented with 1 % HEPES and a final wash with phenol-red free RPMI supplemented with 1 % HEPES and 200 nM NR4A before imaging.</p>
Rapid and Accurate Identification of Stem Cell Differentiation Stages via SERS and Convolutional Neural Networks
<p><a name="OLE_LINK2"></a><span>Monitoring the transition of cell states during induced pluripotent stem cell (iPSC) differentiation is crucial for clinical medicine and basic research. However, both identification category and prediction accuracy need further improvement. Here, we propose a method combining Surface-Enhanced Raman spectroscopy (SERS) with convolutional neural networks (CNN) to precisely identify and distinguish cell states during stem cell differentiation. First, mitochondria-targeted probes were synthesized by combining AuNRs and mitochondrial localization signal (MLS) peptides to obtain effective and stable SERS spectra signals at various stages of cell differentiation. Then, the SERS spectra served as input datasets, and their distinctive features were learned and distinguished by CNN. As a result, rapid and accurate identification of six different cell states, including the embryoid body (EB) stage, was successfully achieved throughout the stem cell differentiation process with an impressive prediction accuracy of 98.5%. Furthermore, the impact of different spectral feature peaks on the identification results was investigated, which provides a valuable reference for selecting appropriate spectral bands to identify cell states. This is also beneficial for shortening the spectral acquisition region to enhance spectral acquisition speed. These results suggest the potential for SERS-CNN models in quality monitoring of stem cells, advancing the practical applications of stem cells.</span></p>
Data from: Gastrointestinal gd T cells reveal upregulated T-cell transcripts and signaling pathways during peanut oral immunotherapy
<p>Oral immunotherapy (OIT) has been successful in desensitizing patients to offending food allergens, although identification of tissue-resident T cell subsets and cognate pathways leading to desensitization has been challenging. The T cells are a major T-cell subset of mucosal intraepithelial lymphocytes (IELs) and play a significant role in tissue homeostasis and repair. Studies in mouse models suggested a regulatory role of gd T cells in food allergy (FA). Also, peripheral gd T cells from patients analyzed over 24 weeks of peanut OIT were shown to undergo dynamic changes in expression profiles, implicating pathways involved in immune homeostasis. To our knowledge, the role of gd T cells in the intestinal mucosa of FA patients during immunotherapy has not been examined. To this end, we investigated whether gd T cells in the gastrointestinal (GI) tract are modulated during peanut OIT. We hypothesized that GI-resident gd T cells in FA patients would increase during the course of peanut OIT and reveal transcripts and pathways relevant to the mechanisms of peanut desensitization.</p>
Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment
<p>Source data for the paper, "Respiratory Complex I Regulates Dendritic Cell Maturation in Explant Model of Human Tumor Immune Microenvironment."<br><br>This includes nanostring gene expression profiling of primary human tumor material ("fresh" in the data, these samples are taken directly after enzymatic digestion) and the corresponding 3D Patient-Derived Explant Culture (PDEC). <br><br>transfer_257851_files_0a119f4d.zip refers to the mouse spatial transcriptomics data.<br><br>DE_results_Metformin/LPS files refer to differentially expressed genes of human monocyte-derived dendritic cells of 6 individual donors to control untreated dendritic cells after 24hr treatment. <br><br> Also includes the original Seurat.rds file for the scSEQ of primary tumor material (fresh) vs. PDEC<br><br><br><br><br> </p>
A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing
<p>This is a public repository containing secondary datasets described in the publication <a href="https://doi.org/10.26508/lsa.202302481">"A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing"</a>. Primary data for this project are available at NCBI/SRA under the BioProject accession numbers PRJNA600820 and PRJNA834955, and include the following sequencing datasets:</p> <p>REH cell line:</p> <ul> <li>PacBio WGS</li> <li>ONT Ultralong WGS</li> <li>Illumina short-read PCR-free WGS</li> <li>IsoSeq RNA-seq</li> <li>Illumina short-read RNA-seq</li> </ul> <p>GM12878 cell line:</p> <ul> <li>Illumina short-read RNA-seq</li> </ul> <p>This dataset includes the following files:</p> <p><strong>Depth of Coverage analysis</strong></p> <ul> <li>Output from `samtools coverage`: <em>samtools.coverage.illumina.txt, samtools.coverage.ont.txt, samtools.coverage.pb.txt</em></li> <li>Output from `copycat` (binned coverage): <em>copycat.ont.coverage.10kb.csv, copycat.pb.coverage.10kb.csv, copycat.pcrfree.coverage.10kb.csv</em></li> </ul> <p><strong>Structural Variant (SV) callsets</strong></p> <ul> <li><strong>Raw:</strong> <em>illumina.tiddit.vcf, ont.sniffles.vcf, pb.sniffles.vcf</em></li> <li><strong>Filtered: </strong><em>REH.svs.filtered.csv</em></li> </ul> <p><strong>SNV callsets</strong></p> <ul> <li><strong>Filtered and annotated:</strong> <em>REH.mutect.filtered.ann.vcf.gz</em></li> </ul> <p><strong>Fusion gene callsets</strong></p> <ul> <li><strong>Short-read: </strong><em>GM12878.fusionreport.txt, illumina.all.txt, illumina.filtered.csv, REH.arriba.fusions.tsv, REH.fusioncatcher.fusion-genes.txt, REH.pizzly.txt, REH.squid.fusions.annotated.txt, REH.starfusion.abridged.tsv, REH.pdf</em></li> <li><strong>Long-read: </strong><em>cupcake.long.csv, cupcake.std.csv, jaffa_results.csv</em></li> <li><strong>Filtered: </strong><em>REH.fusions.filtered.csv</em><br> </li> </ul> <p> </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.