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579 results for “cell death”

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zenodo44/100

Type III interferons may suppress viral infections by triggering cell death -- Imaging Dataset

This dataset accompanies the article "Type III interferons may suppress viral infections by triggering cell death". Earlier version is available as a preprint, <a href="https://doi.org/10.1101/2024.09.09.612051" target="_blank" rel="noopener">https://doi.org/10.1101/2024.09.09.612051</a>. The updated dataset includes quantifications for Figure 7C and Figure 7D.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death

This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021. Abstract: DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.

opencc-by-4.0Aug 2021View details →

zenodo40/100

Data and statistical analysis for: Bacterial nanotubes are a manifestation of cell death

Contains all data and code to reproduce the statistical analysis in the Supplementary file 2 for the paper "Bacterial nanotubes are a manifestation of cell death" to be published in Nature Communications. Contents: <ul> <li>statistical_analysis.Rmd is the main document written in R Markdown</li> <li>statistical_analysis.html is a compiled version of statistical_analysis.Rmd showing all the computed results.</li> <li>The Source data.xls file contains raw data used for the analysis - the sheet names indiciate the figure they refer to. See the main file for code that can read the data.</li> </ul> The code can also be accessed at <a href="https://github.com/cas-bioinf/nanotubes-death">https://github.com/cas-bioinf/nanotubes-death</a>

opencc-by-4.0Aug 2020View details →

zenodo40/100

Cell growth dataset for "Suppression of bacterial cell death underlies the antagonistic interaction between ciprofloxacin and tetracycline in Escherichia coli"

<div>This dataset contains growth data of E. coli cells measured by optical density at a wavelenght of 600 nm (OD600) to determine the conditions where the combination of ciprofloxacin (CIP) and tetracycline (TET) is antagonistic (or suppressive), using a medium that supports fast, intermediate and slow growth (M9 based medium supplemented with: glucose and amino acid, glucose, and glycerol, respectively).</div> <div>Two types of assays were performed: a checkerboard assay with a 2-dimension gradient of antibiotic concentrations shown in "Fig-S1-Growth_rates-Checkerboard_assay.zip", and bulk growth rates assay  shown in "Fig-S2-Bulk_doubling_rates-Bioreactor.zip". </div> <div>These experiments are presented in the supplementary figures of the following manuscript published as preprint in bioRxiv:  https://doi.org/10.1101/2024.04.18.590101.</div> <div> </div> Fig-S1-Growth_rates-Checkerboard_assay.zip: <div>- growth-OD600-Blank_correct.xlsx: contains the blank corrected (subtracted by initial OD of sterile medium) OD600 data for each well measured in the checkerboard assay. The negative control is named as "Blank B" in the spreasheet and the antibiotic concentrations for each well is defined in column C (Content).</div> <div>- growth_data.xlsx: contains the processed data from "Growth-OD600-Blank_correct.xlsx":growth curves were smoothed with a 5-window moving median and outliers corrected using the filloutliers function in MATLAB. Outliers that were missed were manually corrected.</div> <div>- time_h.xlsx: contains the time in hours used by the matlab function to build the dose response figure S1B.</div> <div>- analysis_code_checkerboard_assay.m: matlab function used to build the dose response figure S1B.</div>   Fig-S2-Bulk_doubling_rates-Bioreactor.zip: - "bulk_doubling rates_gly/glu/gluaa.csv": contains the calculated doubling rates for each of three experimental replicates (rows) and for each antibiotic treatment (columns: Ctrl, CIP, TET, CIP-TET) as shown in Figure S2. These values were used to calculated the Bliss independence (for further details see https://gitlab.com/MEKlab/single-cell-suppression-2024/figure plotting.ipynb    - Folders containing OD600 measurements and calculated growth rates for each growth medium, which are further separated into folders for each experimental replicate. Each replicate folder is labelled as the date the experiment was performed (denoted as "*" hereon). Each contains the following: raw optical density data (*.txt files), summary of experiment and results (*.docx file), the function compute_growth_rates.m, and the script Growth_curves_*.m. <div>For more information on methods, strains used and table header descriptions, please refer to the README.txt. </div>

opencc-zeroJul 2024View details →

zenodo40/100

[Data from:] Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

Background Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa. Results A germplasm screening of B. rapa 56 accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 contains also LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract. Conclusions This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.

opencc-by-4.0Dec 2020View details →

zenodo40/100

Sphingolipids are involved in Pieris brassicae egg-induced cell death in Arabidopsis thaliana

This table contains mean + SEM values of sphingolipid levels by LC-MS analysis in Arabidopsis thaliana (wild-type and mutant lines) and Brassica nigra (wild-type) in response to egg extract of Pieris brassicae, as well as P-values for selected comparisons by Welsch t-test. These data were used for Fig. 7 and Fig. 8 of Groux et al. 2022

opencc-by-4.0Feb 2022View details →

dryad40/100

Species-specific effects and the ecological role of Programmed cell Death in the microalgae Ankistrodesmus (Sphaeropleales, Selenastraceae)

Reports of programmed cell death (PCD) in phytoplankton raise questions about the ecological evolutionary role of cell death in these organisms. We induced PCD by nitrogen deprivation and unregulated cell death (non-PCD) in one strain of the green microalga Ankistrodesmus densus and investigated the effects of the cell death supernatants on phylogenetically related co-occurring organisms using growth rates and maximum biomass as proxies of fitness. PCD-released materials from A. densus CCMA-UFSCar-3 significantly increased growth rates of two conspecific strains compared to healthy culture (HC) supernatants and improved the maximum biomass of all A. densus strains compared to related species. Although growth rates of non-A. densus with PCD supernatants were not statistically different from HC treatment, biomass gain was significantly reduced. Thus, the organic substances released by PCD, possibly nitrogenous compounds, could promote conspecific growth. These results support the argument that PCD may differentiate species or subtypes and increases inclusive fitness in this model unicellular chlorophyte. Further research, however, is needed to identify the responsible molecules and how they interact with cells to provide the PCD benefits.

opencc-zeroOct 2022View details →

zenodo40/100

Dataset of molecular structures of PNAS article " Ca2+ permeation through C-terminal cleaved, but not full-length human Pannexin1 hemichannels, mediates cell death"

The PMFWT_91_80.tar.gz file contains the WT molecular system described in the cited article. Briefly, the package contains the structure and topology files for AMBER software, along with configuration files to run Umbrella Sampling method and calculation of PMF of a Ca+2 ion traslocating the human pannexin channel (WT).  The TRUCWT_91_80.tar.gz file contains the truncated molecular system described in the cited article. Briefly, the package contains the structure and topology files for AMBER software, along with configuration files to run Umbrella Sampling method and calculation of PMF of a Ca+2 ion traslocating the truncated human pannexin channel as described in the article. Two NetCDF trajectories (*.nc) of a single PMF window are provided for each system.

opencc-by-4.0May 2024View details →

zenodo40/100

Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"

Data related to the publication Murschhauser et al.: <a href="https://doi.org/10.1038/s42003-019-0282-0">A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death</a>. It contains fluorescence time traces of single cells marked with cell-event markers and observed by time-lapse microscopy. The cells were treated with nanoparticles at different doses (NP25 and NP100), with staurosporine (sts) or were left untreated for control (ctrl). See the above-mentioned publication for more details. The format of the data is described below. The file <code>Data_A549.zip</code> contains data measured with A549 cells, and the file <code>Data_Huh7.zip</code> contains data measured with Huh7 cells. Both files have the same structure. Each file contains the directories <code>Raw</code> and <code>Fitted</code> as well as a checksum file. The <code>Raw</code> directory contains single-cell fluorescence time courses as obtained by time-lapse microscopy. The <code>Fitted</code> directory contains the results of fitting model functions as well as properties of identified events, such as event times. The checksum file contains SHA256 checksums of all files within these directories and can be used to check file integrity. Both directories contain measurement directories. Each measurement directory contains the data corresponding to one experiment. The name of the measurement directory is the measurement identifier. Each measurement directory contains condition directories. Each condition directory contains data corresponding to one condition measured in the measurement and is named after the condition. Each condition directory contains marker directories. They are named after the fluorescence markers measured and contain files with single-cell data corresponding to the respective markers. The names of those files consist of multiple parts separated by underscores. The first two parts identify a position of the microscope. Since pairs of markers were measured, each position is present in two marker directories. The third part is the measurement identifier. The other parts will be described below. The <code>Raw</code> directory contains only CSV files with the raw fluorescence time courses. The filenames contain no other parts and have the suffix “.txt”. The first row of each CSV file is the time (in units of 10 minutes), and the other rows are the fluorescence time courses of the cells observed at the corresponding position (in arbitrary units). Each file in the <code>Raw</code> directory corresponds to a group of files in the <code>Fitted</code> directory. The <code>Fitted</code> directory contains three types of CSV files. Their names have “ALL” as fourth part, a session identifier as sixth part and the suffix “.csv”. The fifth part indicates the type of file and is one of the following: <ul> <li>“PARAMS” indicates the estimated values for the model parameters. Each row stands for one cell and each column for a parameter of the model function fitted to the data. The model functions are published with the <a href="https://doi.org/10.5281/zenodo.1418465">fitting software</a>.</li> <li>“SIMULATED” indicates the fitted traces. The traces are calculated using the model functions and the estimated parameters. The format is the same as for the raw traces, but the time is in units of hours and has a higher resolution.</li> <li>“STATE” indicates additional information extracted from the fitted traces. Each row stands for a cell and each column for a property. The first column is the number of the cell. The second column is the event time found (in hours); non-finite values indicate that no event time was found. The third and fourth columns contain the absolute and relative amplitude of the trace, respectively. The fifth column is the logarithmic likelihood of the best fit. The sixth column indicates an algorithm used for postprocessing, and the seventh column indicates the trace slope at the event. See the fitting software for details.</li> </ul>

opencc-by-4.0Sep 2018View details →

zenodo40/100

Quantitative phase microscopy timelapse dataset of PNT1A, DU-145 and LNCaP cells with annotated caspase 3,7-dependent and independent cell death

Time-lapse dataset of prostatic cell lines (DU-145, PNT1A, LNCaP) exposed to cell death-inducing compounds (staurosporine, doxorubicin) and black phosphorus. The time-lapse dataset is annotated as follows: (1) cell masks and cell numbers, (2) by cell death type and timepoint of death in the attached xlsx file. This dataset is supplementary to the article: Vicar, T., Raudenska, M., Gumulec, J. et al. The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death. Sci Rep 10, 1566 (2020). <a href="https://doi.org/10.1038/s41598-020-58474-w">https://doi.org/10.1038/s41598-020-58474-w</a> Correlative fluorescence microscopy is in a separate dataset <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a> Code is available at <a href="https://github.com/tomasvicar/CellDeathDetect">https://github.com/tomasvicar/CellDeathDetect</a> Methods Cell culture and cultured cell conditions LNCaP cell line was established from a lymph node metastase of the hormone-refractory patient and contains a mutation in the AR gene. This mutation creates a promiscuous AR that can bind to different types of steroids. LNCaP cells are AR-positive, PSA-positive, PTEN-negative and harbor wild-type p53 {Skjoth, 2006 #150; Mitchell, 2000 #149}. PNT1A is immortalized non-tumorigenic epithelial cell line. PNT1A cells harbour wild-type p53. However, SV40 induced T-antigen expression inhibits the activity of p53. This cell line had lost the expression of androgen receptor (AR) and prostate-specific antigen (PSA) (Raudenska, 2019). DU-145 cell line is derived from the metastatic site in the brain and contains P223L and V274F mutations in p53. This cell line is PSA and AR-negative and androgen independent (Chappell, 2012). All cell lines used in this study were purchased from HPA Culture Collections (Salisbury, UK). and were cultured in RPMI-1640 medium with 10 % FBS. The medium was supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml). Cells were maintained at 37°C in a humidified (60%) incubator with 5% CO2 (Sanyo, Japan). Correlative time-lapse quantitative phase-fluorescence imaging QPI and fluorescence imaging were performed by using multimodal holographic microscope Q-PHASE (TESCAN, Brno, Czech Republic). To determine the amount of caspase-3/7 product accumulation, cells were loaded with 2 µM CellEventTM Caspase-3/7 Green Detection Reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol and visualized using FITC 488 nm filter. To detect the cells with a loss of plasma membrane integrity, cells were stained with 1 ug/ml propidium iodide (Sigma Aldrich Co., St. Louis, MO, USA) and visualized using TRITC 542 nm filter. Nuclear morphology and chromatin condensation were analyzed using Hoechst 33342 nuclear staining (ENZO, Lausen, Switzerland) and visualized using DAPI 461 nm filter. Cells were cultivated in Flow chambers μ-Slide I Lauer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions (37°C, humidified air (60%) with 5% CO2) during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). To image enough cells in one field of view, lens Nikon Plan 10/0.30 were chosen. For each cell line and each treatment, seven fields of view were observed with the frame rate 3 mins/frame for 24 or 48 h respectively. Holograms were captured by CCD camera (XIMEA MR4021 MC-VELETA), fluorescence images were captured using ANDOR Zyla 5.5 sCMOS camera. Complete quantitative phase image reconstruction and image processing were performed in Q-PHASE control software. Cell dry mass values were derived according to {Prescher, 2005 #177} and {Park, 2018 #178} from the phase (eq. (1)), where m is cell dry mass density (in pg/μm2), φ is detected phase (in rad), λ is wavelength in μm (0.65 μm in Q-PHASE), and α is specific refraction increment (≈0.18 μm3/pg). All values in the formula except the Phi are constant. Phi (Phase) is the value measured directly by the microscope. Integrated phase shift through a cell is proportional to its dry mass, which enables studying changes in cell mass distribution (Park et al., 2018). File description There are three archives included for particular cell lines: <ul> <li>QPI_annotated_timelapse_DU145.zip for DU-145 cells</li> <li>QPI_annotated_timelapse_PNT1A.zip for PNT1A cells</li> <li>QPI_annotated_timelapse_LNCaP.zip for LNCaP cells</li> </ul> The archive includes of following files: <ul> <li>Tiff with time-lapse quantitative phase image (32-bit files 600x600px with values in pg/um2 with framerate 1 frame/3minutes with 1.59 px/um), named QPI_cellline_treatment_FOV.tiff</li> <li>Tiff file with segmentation mask for particular cells named mask_cellline_treatment_FOV.tiff</li> <li>xlsx table with cell death type (1 for apoptosis, 2 for necrosis, 3 for ambiguous/surviving) and time of death for representative cell number from mask, named  labels_cellline_treatment_FOV.xlsx</li> </ul> file naming has following conventions: <ul> <li>cell names: DU145, PNT1A, LNCaP for particular cell line</li> <li>treatments: st, bp, do for staurosporine, black phosphorus and doxorubicin</li> <li>fields of view: 1 to 7</li> </ul> e.g. QPI_DU145_st_4.tif, mask_DU145_st_4.tif, labels_DU145_st_4.xlsx Note that correlative fluorescence images are available at <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a>

opencc-by-4.0Mar 2019View details →

dryad40/100

Acceptable loss: Fitness consequences of salinity-induced cell death in a halotolerant microalga

Environmentally induced reductions in fitness components (survival, fecundity) are generally considered as passive, maladaptive responses to stress. However, there is also mounting evidence for active, programmed forms of environmentally induced cell death in unicellular organisms. While conceptual work has questioned how such programmed cell death (PCD) might be maintained by natural selection, few experimental studies have investigated how PCD influences genetic differences in longer-term fitness across environments. Here, we tracked the population dynamics of two closely related strains of the halotolerant microalga Dunaliella salina, following transfers across salinities. We showed that after a salinity rise, only one of these strains displayed a massive population decline (-69% in one hour), largely attenuated by exposure to a PCD inhibitor. However, this decline was followed by a rapid demographic rebound, characterized by faster growth than the non-declining strain, such that sharper decline was correlated with faster subsequent growth across experiments and conditions. Strikingly, the decline was more pronounced in conditions more favourable to growth (more light, more nutrients, less competition), further suggesting that it was not simply passive. We explored several hypotheses that could explain this decline-rebound pattern, which suggests that successive stresses could select for higher environmentally induced death in this system.

opencc-zeroJan 2023View details →

zenodo40/100

Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks

This repository contains training, validation, and testing of fluorescence image data sets with their label for spheroid cell death classification.  These data are intended to be used in the paper "Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks" currently submitted

opencc-by-4.0Aug 2023View details →

dryad40/100