Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
111,247
datasets available to search
ShareScore release 0.9.0
Dataset results
111,247 results for “cells”
Three dimensional characterization of nickel coarsening in solid oxide cells via ex-situ ptychographic nano-tomography
<p>Three-dimensional dataset of a solid oxide cell (SOC) fuel electrode microstructure. Data were acquired using ptychographic X-ray computed tomography (PXCT). </p>
Noninvasive detection of macrophage activation with single-cell resolution through machine learning
<p>Data related to the article "Noninvasive detection of macrophage activation with<br> single-cell resolution through machine learning".</p> <p>The package contains 2 folders:<br> - RawData: This package contains raw data and examples of processing to extract the<br> variables employed to train and assess the models.<br> - Variables: This package contains the extracted data from the various experiments<br> showed in the article.</p>
Epigenome-wide analysis reveals specific DNA hypermethylation of T cells during human hematopoietic differentiation
<p>Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. In this study we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages.</p>
New Ideas for Brain Modelling 4-Figure 1. Example mapping of cells presented as an image.
<p>The author has used this structure before in Greer (2016) and it is a type of entropy classifier. It attempts to reduce the error overall and is not so concerned with minimising individual associations. The paper Greer (2017) describes a classifier that is conjecturally more visual in nature than other types and it also uses a complete linking method. Instead of several levels of feature refactoring, it is a 1-level impression only. With the image classifier, each cell stores a count of every other cell it gets associated with, when averaging this can determine what cells are most similar to the pixel in question. Figure is an example of the clustering technique. If the top LHS grid is the first image to be mapped, then for cell A1, the other black cells are recorded as shown, with a count of 1. The count would then be incremented each time a cell is recorded again, for example, after the second image, cell A3 would lose a count. The idea of linking everything this way has now been used 3 times.</p>
Lebelled cells at the wound
<p>The extremity of the tail of a transgenic zebrafish embryo was cut at 3 dpf.</p> <p>At 16 hpa, the wound was imaged using a MVX10 Olympus microscope, magnification 6.3 with a 2x objective. Red filter to image mCherry fluorescence. XC50 camera, 1s exposure time.</p>
High-throughput poly(A) length measurement of HeLa and NIH 3T3 cells using TAIL-seq with MiSeq
<p>This dataset contains the full raw data directory from Illumina MiSeq generated for Chang et al. (2014, DOI: 10.1016/j.molcel.2014.02.007). Please refer to the original paper and its supplementary materials for further details.</p>
Data: In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells
<p>This data set is composed of magnetic resonance images (MRI) that are supporting the article entitled <em>In vivo fate of free and encapsulated iron oxide nanoparticles after injection of labelled stem cells </em>by the same authors. Nanoparticle contrast agents are used to label stem cells and monitor their bio-distribution in pre-clinical models of disease. Due to the impact on the interpretation of imaging results, understanding the <em>in vivo</em> fate of the particles is important. The bio-distribution after intra-cardiac injection of labelled cells with superparamagnetic iron oxide nanoparticles was monitored longitudinally by MRI. </p>
Time-averaged simulation results and in vivo measurements to show the impact of red blood cells on the flow field in the cortical microvasculature
<p>The dataset contains 5 files. 4 of them are time-averaged results of blood flow simulations with discrete red blood cell (RBC) tracking in realistic microvascular networks. The 5th file contains median values of RBC velocity measurements at capillary bifurcations in the somatosensory cortex of the mouse.</p> <p>Further notes on the simulation results:<br> - The realistic microvascular networks are from the mouse parietal cortex and have first been published in Blinder et al., 2013, Nature Neuroscience (<a href="https://doi.org/10.1038/nn.3426">https://doi.org/10.1038/nn.3426</a>).<br> - The numerical model to simulate blood flow in realistic microvascular networks has been described in Schmid et al., 2017, PLOS Computational Biology (<a href="https://doi.org/10.1371/journal.pcbi.1005392">https://doi.org/10.1371/journal.pcbi.1005392</a>).<br> - MVN1 and MVN2 stands for microvascular network 1 and 2, respectively.<br> - wRBCs and wpPs stands for 'with red blood cells' and 'with passive particles'. These terms describe two different numerical models: The wRBC-model accounts for all RBC related flow phenomena. The wpP neglects the phase-separation and the Fahraeus-Lindqvist effect, i.e. RBCs and flow are decoupled. Further details are available from Schmid et al. (2019, <a href="https://doi.org/10.1371/journal.pcbi.1007231">https://doi.org/10.1371/journal.pcbi.1007231</a>)</p> <p><strong>File format: </strong>pickle (Python)</p> <p><strong>Files 1 - 4 </strong>(Time-averaged simulation results):<br> Filenames: MVN1_wpPs.tar.bz2, MVN2_wpPs.tar.bz2, MVN1_wRBCs.tar.bz2, MVN2_wRBCs.tar.bz2</p> <p>Each compressed folder contains two files:<br> <br> edgesDict.pkl: dictionary with edge/vessel related data: </p> <ul> <li>flow: Flow rate in vessel [um^3/ms]</li> <li>length: Vessel length [um] (Tortuosity is considered)</li> <li>htt: Tube hematocrit in vessel [-]</li> <li>diameter: Effective vessel diameter [um]</li> <li>connectivity: Vertex indices, e.g. start and end vertex of the corresponding vessel</li> </ul> <p>verticesDict.pkl: dictionary with vertex/bifurcation related data:</p> <ul> <li>index: Index of the current vertex </li> <li>coords: Coordinates to describe the position of the vertex [um]</li> <li>pressure: Pressure at the vertex [mmHg]</li> </ul> <p> </p> <p><strong>File 5</strong> (in vivo RBC velocity measurements):<br> Filename: measurementDict.pkl</p> <p>keys:</p> <ul> <li>divergent_d1: divergent bifurcation, RBC velocity measurement in daughter vessel 1</li> <li>divergent_d2: divergent bifurcation, RBC velocity measurement in daughter vessel 2</li> <li>convergent_m1: convergent bifurcation, RBC velocity measurement in mother vessel 1</li> <li>convergent_m2: convergent bifurcation, RBC velocity measurement in mother vessel 2</li> </ul> <p><br> Data structure: list of list,<br> e.g. daughter vessel 1:<br> [[bif.1 - measure.1, bif.1 - measure.2, bif.1 - measure.3], [bif.2 - measure.1, bif.2 - measure.2, bif.2 - measure.3],...]<br> bif.: bifurcation, measure.: measurement.<br> The order of bifurcations is the same for 'divergent_d1' and 'divergent_d2' (and for 'convergent_m1' and 'convergent_m2'). </p> <p> </p>
Dataset of Cervical Cell Images for the Study of Changes Associated with Malignancy in Conventional Pap Test
<p>This data set was approved by CEP, a human-research ethics committee {Comitê de Ética em Pesquisa de Campinas}, Brazil <em>(CAAE approval number: 71277217.6.0000.5404) </em></p> <p>Cervical cancer prevention campaign was carried out at the University of Campinas (Brazil), where 71 women were treated. For this study, six women were chosen: non-pre-menopausal, non-pregnant and between the ages of 25 and 40 years. Samples diagnosed with normal squamous lesions, atypia in squamous cells of undetermined significance (AC-US), low-grade squamous intraepithelial lesion LEI (changes associated with HPV infection or light dislocation (NIC 1)), and immature squamous metaplasia, were chosen. All samples were collected before treatment. The patients were anonymized and assigned a unique identifier. The criterion to limit the number of plates used in this study is related to the large amount of data to be processed and the computational cost necessary for this process.</p> <p>Raw data sets are available as CSV files. Each data was numbered to include the identification of the meta-data, along with the tag of the images following a sequence. For example, the data file<em> “ 01-CAP091868-MORF.csv ”</em> has the prefix 01 that refers to the sequential numbering of the files with the data from the digitized slide.</p> <p>The data set contains 102 digitized images of the human papilloma examination of 7 different patients. They were segmented 962</p>
Data for: "A high-throughput microscopy method for single-cell analysis of event-time correlations in nanoparticle-induced cell death"
<p>Data related to the publication Murschhauser <em>et al.</em>: <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.</p> <p>The format of the data is described below.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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:</p> <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> <p> </p>
Research data supporting "Engineering anisotropic muscle tissue using acoustic cell patterning"
<p>Raw research data supporting the publication:</p> <p>Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)</p>
MinION 1D² Reads From Mus musculus GL261 Cell Lines
<p>Called FASTQ and raw FAST5 MinION cDNA reads (1D²) from a murine GL261 neuroblastoma cell line, cultured at the Malaghan Institute of Medical Research, sequenced on a R9.5 flow cell in August 2017 using the LSK309 1D² kit for ligating ONT adapters to cDNA generated using strand-switching primers.</p> <p>The called reads for the entire sequencing run are available:</p> <ul> <li>called_reads_1Dsq_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- called reads from both/all runs (1D²-corrected fastq files only).</li> <li>called_reads_uncorrected_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- uncorrected reads from both/all runs.</li> <li>metadata_called_reads_Olivier_GL261_cDNA_2017-Aug-04.tar.gz -- metadata associated with all called sequences (e.g. sequencing_summary.txt)</li> </ul> <p>This dataset only includes a subset of the total reads as raw signal / FAST5 files:</p> <ul> <li>Actb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse beta-actin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000029580;r=5:142903234-142903654;t=ENSMUST00000100497">ENSMUST00000100497.10</a>].</li> <li>Ubb_GL261_cDNA_2017-Aug-04_1D2.tar -- reads from one run that mapped (in whole or in part) to a mouse ubiquitin transcript [<a href="http://asia.ensembl.org/Mus_musculus/Transcript/Summary?db=core;g=ENSMUSG00000019505;r=11:62551171-62553213;t=ENSMUST00000019649">ENSMUST00000019649.3</a>].</li> </ul>
Supplementary data to: Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma
<p>Supplemental Data for the patient described in the manuscript: "Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma". Summary of somatic variant calls from DNA whole exome, gene FPKM from RNA sequencing, and neoantigen predictions for high-affinity (ic<sub>50</sub> <500 nM) candidates.</p>
Cell specificity of human regulatory annotations and their genetic effects on gene expression
<p>Processed data for the manuscript: Cell specificty of regulatory annotations and their genetics effects on gene expression.</p> <p>The code to generate these data is included in the zip-file: regulatoryAnnotations_comparisons-master.zip</p> <p>and at GitHub: https://github.com/ParkerLab/regulatoryAnnotations_comparisons</p> <p>Individual dataset information:</p> <p>numberOfSegments.dat.gz: Number of segments in each annotation</p> <p>lengthDistributionOfSegments.dat.gz: Length distribution of segments in each annnotation</p> <p>genomeCoverage.dat.gz: Total genome coverage of regions in each annoation</p> <p>overlapFraction.dat.gz: Basepair level overlap between two pairs of annotations</p> <p>annotations_chromatinStateOverlap.dat.gz: Overlap of annotations with chromatin states</p> <p>information.fourcells_enhancer.dat.gz: Information content of the mean enhancer chromatin posterior probability for annotation segments across four cell types</p> <p>information.fourcells_promoter.dat.gz: Information content of the mean promoter chromatin posterior probability for annotation segments across four cell types</p> <p>gwas_enrichment_stats.txt.gz: Enrichment of GWAS SNPs in annotations</p> <p>GTEx_v7.ESI_medianTPM_min0.15.dat.gz : ESI for genes across 50 tissues</p> <p>gtexv7_lcleqtl.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL in annotations</p> <p>gtexv7_lcleqtl.binned_lclESI.enrichment.dat.gz: Enrichment of GTEx v7 LCL eQTL binned by lclESI in annotations</p> <p>K562.gtexv7_bloodeqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 blood eQTL effect sizes in K562 annotations </p> <p>GM12878.gtexv7_lcleqtl.fdr0.1.prune0.8.maf0.2.ld0.99.annotations.dat.gz: GTEx v7 LCL eQTL effect sizes in GM12878 annotations</p> <p>GM12878.dsqtl.prune0.8.maf0.2.ld0.99.annotations.dat.gz : GM12878 DNase QTL effect sizes in GM12878 annotations</p> <p>GM12878.allelicBiasResults.FracRef.downsampled30.annotations.withMAF.dat.gz: GM12878 ATAC-seq allelic bias effect sizes in GM12878 annotations </p> <p> <br> <br> <br> </p>
Metabolomic Profiling of Zinc Accumulating Prostate Cancer Cells: dataset of metabolomics and transctiptomics
<p>In this study, we focused on the metabolism of prostate cancer cells forced to accumulate zinc. Because levels of metabolites involved in Krebs and methionine cycle can participate in non-metabolic processes such as changes in gene expression, a panel of 371 genes connected with key steps of carcinogenesis was designed and expression levels of these genes were assessed to determine, which pathways are changed due to the long-term zinc treatment.</p> <p>As a model of prostate cancerogenesis, wild-type and zinc accumulating cell lines PNT1A, 22Rv1, and PC-3 were used. Metabolite profiles were examined using liquid chromatography triple quadrupole mass spectrometry. Quantification of total intracellular zinc was performed by atomic absorption spectrometry and gene expression investigated by cDNA microarray. </p> <p>For description of creation of zinc-resistant cell lines see Holubova et al, Metallomics 2014, DOI 10.1039/C4MT00065J</p> <p> </p> <p><strong>Description of dataset</strong></p> <p>Total 6 files are included:</p> <p><em>Krebs.metabolites.csv</em>: table of metabolomic data (in ppm) of wild type/untreated/zinc-resistant cells.</p> <p><em>Krebs.metabolites.medium.csv</em>: table of metabolomic data (in fold change compared to medium) in cultivation media of abovementioned cells.</p> <p><em>RNA_array_fold_p.csv</em>: processed results of microarray displayed as mean log2 fold change (resistant - WT) and p level</p> <p><em>RNA_array_raw_22Rv1.csv</em>, <em>RNA_array_raw_PC-3.csv, RNA_array_raw_PNT1A.csv</em> raw data from microarray reader for WT and resistant cells.</p>
Single-cell -omics datasets containing a trajectory
<p>Contains datasets to compare trajectory inference methods. See <a href="https://benchmark.dynverse.org">https://benchmark.dynverse.org</a></p> <p>These files can be opened in R using `readRDS` .</p> <p>Each file is a list containing at least:</p> <ul> <li>expression and counts: filtered log<sub>2</sub> normalised expression files and raw counts</li> <li>milestone_network, milestone_percentages and divergence_regions: the trajectory model</li> </ul>
Research raw data supporting "Activatable cell-biomaterial interfacing with photo-caged peptides"
<p>Raw data supporting the publication;</p> <p>Lin Y. et al., Activatable cell-biomaterial interfacing with photo-caged peptides, 2018, Chemical Science, DOI: 10.1039/c8sc04725a.</p>
Case report: Large-cell calcifying Sertoli cell tumor
<p><strong>Case history</strong></p> <p>21-year-old male with a painless mass in the right testicle and a prior history of cutaneous lesions.</p> <p> </p> <p><strong>Histologic findings</strong></p> <p>Figures show a proliferation composed of large, polygonal cells with ample, eosinophilic cytoplasm, round nuclei, evident nucleoli, with a trabecular, cord-like pattern of growth, associated with foci of dystrophic calcification and psammomatous bodies, within a myxoid loose stroma.</p> <p> </p> <p><strong>Discussion</strong></p> <p>Large-cell calcifying Sertoli cell tumor is an unusual morphological variant of Sertoli cell tumors composed of large polygonal Sertoli cells associated with calcification. Around one third of all cases appear in patients with the Carney or, less frequently, with the Peutz-Jeghers syndrome.</p> <p>Clinically, it presents as a small (<2 cm), painless testicular mass, which can be bilateral and multifocal in about 40% of the cases, especially in syndromic settings. In non-sporadic cases extratesticular manifestations are related to the associated syndrome, such as myxomas of the skin, soft-tissue, and heart and myxoid lesions of the breast, facial lentigines, cutaneous blue nevi, secondary Cushing syndrome due to pigmented adrenocortical nodular hyperplasia, pituitary somatotroph adenomas, and psammomatous melanotic schwannomas.</p> <p>Microscopically, solid, tubular, and cord-like growth patterns can be observed. Tumor cells are large polygonal with abundant eosinophilic, finely granular cytoplasm, vesicular nuclei with prominent nucleoli, within a myxohyaline stroma. Distinct calcifications characterized by large, laminated calcified nodule, psammoma bodies and focal ossification, and neutrophilic infiltration, can be also found.</p> <p>The presence of large tumor size (>4 cm), overt nuclear atypias, increased mitotic activity (>3 mitosis/10 HPF), necrosis, extratesticular extension, and vascular invasion are associated with clinical aggressive behavior. Malignant tumors appear in patients older than 25 years old and are usually not associated with syndromes.</p> <p>Tumor cells are positive for vimentin, S-100, EMA, melan-A, desmin, and inhibin, and focally positive for cytokeratins. The main differential diagnoses of large-cell calcifying Sertoli cell tumor include Sertoli cell and Leydig cell tumors.</p> <p>Most of the cases behave indolently, and surgical excision is the treatment of choice.</p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
Quantitative phase microscopy timelapse dataset of PNT1A, DU-145 and LNCaP cells with annotated caspase 3,7-dependent and independent cell death
<p>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:</p> <p>Vicar, T., Raudenska, M., Gumulec, J. <em>et al.</em> The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death. <em>Sci Rep</em> <strong>10, </strong>1566 (2020). <a href="https://doi.org/10.1038/s41598-020-58474-w">https://doi.org/10.1038/s41598-020-58474-w</a></p> <p>Correlative fluorescence microscopy is in a separate dataset <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a></p> <p>Code is available at <a href="https://github.com/tomasvicar/CellDeathDetect">https://github.com/tomasvicar/CellDeathDetect</a></p> <p><strong>Methods</strong></p> <p><em>Cell culture and cultured cell conditions</em><br> 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).</p> <p><em>Correlative time-lapse quantitative phase-fluorescence imaging</em></p> <p>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).</p> <p><strong>File description</strong></p> <p>There are three archives included for particular cell lines:</p> <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> <p>The archive includes of following files:</p> <ul> <li><strong>Tiff with time-lapse</strong> quantitative phase image (32-bit files 600x600px with values in pg/um2 with framerate 1 frame/3minutes with 1.59 px/um), named <em>QPI_cellline_treatment_FOV.tiff</em></li> <li><strong>Tiff file with segmentation</strong> mask for particular cells named <em>mask_cellline_treatment_FOV.tiff</em></li> <li><strong>xlsx table</strong> 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 <em>labels_cellline_treatment_FOV.xlsx</em></li> </ul> <p>file naming has following conventions:</p> <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> <p>e.g. QPI_DU145_st_4.tif, mask_DU145_st_4.tif, labels_DU145_st_4.xlsx</p> <p>Note that correlative fluorescence images are available at <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a></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.