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Dataset results
48 results for “Live cell imaging”
Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells
<p><strong>Introduction</strong></p> <p>This dataset is associated with our submission to Computers in Biology and Medicine, titled "Accurate Detection and Instance Segmentation of Unstained Living Adherent Cells in Differential Interference Contrast Images". The submission number for this manuscript is CIBM-D-23-09623R1.</p> <p><strong>Authors</strong>: Fei Pan, Yutong Wu, Kangning Cui, Shuxun Chen, Yanfang Li, Yaofang Liu, Adnan Shakoor, Han Zhao, Beijia Lu, Shaohua Zhi, Raymond Hon-Fu Chan, Dong Sun</p> <p><strong>Dataset Description</strong></p> <p>Our dataset comprises 520 differential interference contrast (DIC) images of 12,198 unstained HepG2 human liver cancer cells, each with a corresponding fluorescence image stained with calcein acetoxymethyl (AM), ensuring high-quality ground-truth annotations. Unique in addressing the multi-state nature of adherent cells commonly seen in wet labs, it includes both healthy and unhealthy cells in a single image, providing a valuable resource for studying multi-state cell detection and instance segmentation.<br>Citation</p> <p>We kindly request that researchers who use this dataset cite both our paper and this dataset. This will help acknowledge the work and facilitate further advancements in the field.</p> <p><br><strong>Please cite as follows:</strong></p> <p><strong>Paper:</strong><br>Pan, F., Wu, Y., Cui, K., Chen, S., Li, Y., Liu, Y., Shakoor, A., Zhao, H., Lu, B., Zhi, S., Chan, R. H.-F., & Sun, D. "Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images,” <em>Computers in Biology and Medicine</em>, vol. 182, p. 109151, Nov. 2024, doi: 10/g5p9d8.</p> <p><strong>Dataset:</strong><br>Pan, F., Chen, S., Li, Y., Shakoor, A., Zhao, H., & Sun, D. (2024). Differential interference contrast (DIC) image of unstained living HepG2 human liver cancer cells. Zenodo. </p> <p>Thank you for your interest and support in our work. We look forward to seeing the innovative research that this dataset will enable.</p> <p> </p> <p> </p>
Microscopy images and movies supporting the publication: "tRNA tracking for direct measurements of protein synthesis kinetics in live cells"
<p>This repository contains experimental and simulated microscopy movies and images supporting the publication: Volkov et al. (2018) tRNA tracking for direct measurements of protein synthesis kinetics in live cells. <em>Nat Chem Biol, </em>DOI: 10.1038/s41589-018-0063-y</p> <p>A detailed list of files and file organisation can be found in Repository_content.pdf.</p>
"CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data" datasets
<p>Datasets from the article:</p> <p><strong>CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data </strong></p> <p>by Franck Simon, Maria Colomba Comes, Tiziana Tocci, Louise Dupuis, Vincent Cabeli, Nikita Lagrange, Arianna Mencattini, Maria Carla Parrini, Eugenio Martinelli, Hervé Isambert.</p> <p> </p> <p>The <strong>original videos</strong> are uploaded as: "20161230.zip", "20170105.rar", "Video_2017_0517.zip".</p> <p><strong>Details </strong>for each <strong>experiment </strong>can be found in: "Experiments' details.zip".</p> <p>The <strong>ROIs </strong>(ROI: Region of Interest) are uploaded as .tif files in: "EXTRACTED ROIs.zip".</p> <p>The <strong>MATLAB data</strong> is uploaded in "MATLAB_DATA.rar" and includes: the cancer cells' trajectories (subfolder: "TUMOR TRAJECTORIES"), the immune cells' trajectories (subfolder: "IMMUNE TRAJECTORIES"), the ROIs videos as .mat files for the detection of cells (subfolder: "ROI MAT"), the ROIs further cropped for the extraction of shape descriptors (folder: "ROI_TU MAT"). The ROIs videos .mat files included in the last two subfolders are stopped after their apoptosis has been detected.</p>
BEHAV3D: A 3D live imaging platform for comprehensive analysis of engineered T cell behavior and tumor response
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Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms
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Data from: Live-cell single particle imaging reveals the role of RNA polymerase II in histone H2A.Z eviction
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Data from: Label-free imaging of intracellular structures in living mammalian cells via external apodization phase-contrast microscopy
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Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration
<p>Supporting Materials for the article:<br /> Long-term time-lapse live imaging reveals extensive cell migration during annelid regeneration</p> <p>Eduardo E. Zattara, Kate W. Turlington and Alexandra E. Bely</p> <p>BMC Developmental Biology, 2016</p> <p>Includes 11 movies and one compressed file with R code and data tables.</p>
Data to demonstrate : Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM
<p>Data to demonstrate :</p> <p>Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM - <a href="https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3290&id=545659">https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-3-3290&id=545659</a></p> <p>Trung Duc Nguyen, Yuan-I Chen, Anh-Thu Nguyen, Limin H. Chen, Siem Yonas, Mitchell Litvinov, Yujie He, Yu-An Kuo, Soonwoo Hong, H. Grady Rylander, and Hsin-Chih Yeh, "Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM," Opt. Express <strong>32</strong>, 3290-3307 (2024)</p>
Live-cell imaging data set of HaCaT FUCCI cells with BF/DIC and fluorescence
<p>This dataset contains time-lapse acquisitions of HaCaT FUCCI cells cultured in 8-well chambered coverslips with fluorobrite media. Cells were provided by Dr. Francesco Pasqualini and Dr. Moises di Sante (<a title="http://www.syntheticphysiologylab.com/" href="http://www.syntheticphysiologylab.com/" target="_blank" rel="noreferrer noopener">http://www.syntheticphysiologylab.com</a>), and videos acquired at the Henriques Lab (<a title="https://henriqueslab.org/" href="https://henriqueslab.org/" target="_blank" rel="noreferrer noopener">https://henriqueslab.org/</a>). </p> <p>HaCaT_FUCCI_BF-live set: Nikon Ti2, Channels BF (16 bit), FITC (12 bit sensitive) and Cy5 (12 bit sensitive). 20 positions, acquired in Nikon Ti2 with a 20x/0.8, every 30 minutes for 18 hours. </p> <p><span>HaCaT_FUCCI_DIC-live set: Nikon Ti2, Channels DIC, FITC and Cy5 (all 12 bit sensitive). 20 positions, acquired in Nikon Ti2 with a 20x/0.8, every 30 minutes for 18 hours. </span></p>
Implication of polymerase recycling for nascent transcript quantification by live cell imaging
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Conformational changes in insulin receptor illuminated by live-cell linear dichroism imaging and a FLIP biosensor
<p>Response of GP2(1)-eGFP-CD59, a FLIP biosensor co transfected with insulin receptor against anatgonists and agonists of IR was measured and qunatified using polarization-resolved fluorescence microscopy. Linear dichoirism values were measured by analyinzing cells using already published imageJ macros (Bondar, Rybakova et al. 2021). This data set contains both raw images obtained from polarization micrscopy and analyzed data.</p>
The raw images of Laser Confocal Microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells
<p>The <strong>original imaging data</strong> folder contains the raw images of Laser confocal microscopy experiments in the manuscript: Inert Pepper aptamer-mediated endogenous mRNA recognition and imaging in living cells. <a href="https://doi.org/10.1093/nar/gkac368">https://doi.org/10.1093/nar/gkac368</a> </p>
Live cell imaging reveals the trade-off between target search flexibility and efficiency for Cas9 and Cas12a
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Live cell imaging data of IRE1a-GFP foci formation
<p>Live cell imaging of XBP1 mRNA and IRE1a-GFP expressing cells.</p>
Click-free imaging of carbohydrate trafficking in live cells using an azido photothermal probe
<p>Raw imaging data and source data in the maniscript.</p>
CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies
<p><strong>Abstract:</strong></p> <p>We introduce CALIPERS, a Fluorescence Ubiquitin Cell Cycle Indicator enabling cell cycle-aware phenotyping based on multiplexed live-cell imaging. We validate CALIPERS in three- and four-color reporter lines (HaCaT) and human induced pluripotent stem cells (WTC-11), co-expressing genetically encoded structural and functional fluorescent sensors. To exemplify the broad applications of CALIPERS, we show cell cycle-aware phenotyping in live-cell imaging applications ranging from proliferation and migration to cardiac-specific maturation and drug testing.</p> <p>Code submission: <a href="https://github.com/alessandro-bertero/FUCCIplex">https://github.com/alessandro-bertero/FUCCIplex.git</a></p> <p><strong>Dataset description:</strong></p> <ol> <li>This dataset includes all data and the additional files to recreate the sc-RNAseq analysis in our manuscript. The Rmd files include the Seurat QC pipeline to preprocess the counts data from cellranger (<strong>QC_Seurat_gitgub_polished.RmD</strong>) and the monocle pipeline to re-create the clusters and pseudotime analysis (<strong>monocle_github_polished.Rmd</strong>).</li> <li><strong>Data obtained from Cellranger 7.2 pipeline</strong> are also available here including all the 3 files, usually placed in the path "outs/count/filtered_feature_bc_matrix" by cellrancer. This includes: <strong>barcodes.tsv.gz, features.tsv.gz and matrix.mtx.gz</strong> (alternatively the h5 file filtered_feature_bc_matrix.h5 can be loaded directly in Seurat v5).</li> <li>We also added few RData files to recreate our graphs and follow our pipeline smoothly. <strong>240111_seurat_before_clean.RData, 240111_seurat_clean.RData</strong> and <strong>240111_seurat_processed.RData</strong> are the files created by the codes in QC_Seurat_gitgub_polished.RmD pipeline. Then, <strong>240118_monocle_cds_clustered_proteincoding_PSEUDOTIME_clean_gene_info.RData</strong> is the file set created by the<strong> </strong>monocle_github_polished.Rmd pipeline.</li> </ol> <p>We also suggest to use this pipeline within a docker container by pulling our docker image with R studio:</p> <p><strong>docker pull hedgelab/rstudio-hedgelab:latest</strong></p>
Quantitative live-cell imaging of secretion activity reveals dynamic immune responses
GEO Series GSE253100. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
Live-animal imaging of native hematopoietic stem and progenitor cells
GEO Series GSE115908. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Live imaging in freely behaving mice reveals that adult neural stem cell activation is regulated by the day/night cycle and intracellular Ca2+ dynamic
GEO Series GSE161276. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
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.