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141 results for “Cell tracking”
Labeling and tracking of immune cells in ex vivo human skin
<p>Example imaging datasets linked to the publication 'Labeling and tracking of immune cells in ex vivo human skin' (doi: 10.1038/s41596-020-00435-8).</p> <p>Additional information filenames:<br> - m = ex vivo murine skin<br> - h = ex vivo human skin<br> - mCD8= anti-murine CD8-AF594 nanobody staining (red)<br> - hCD8= anti-human CD8-AF594 nanobody staining (red)<br> - Hoechst= Hoechst 33342 staining (grey)<br> - CD1a= anti-hCD1a-AF488 staining (green)<br> - CD103= anti-hCD103-AF488 staining (green)<br> - SHG = second harmonic generation (cyan)</p> <p>Imaris x64 v9.2.0.</p>
Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"
<p>Data and software for "Metal Pad Sensing: exploiting the electrical double layer to improve resistance-based microfluidic cell tracking, with applications to label-free mechanophenotyping"</p>
Tracking Glioblastoma-astrocytoma cells imaged in brightfield with TrackMate-Cellpose
<p>Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells in brightfield, using a custom cellpose model (included in the dataset).</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
Tracking breast cancer cells migrating collectively and imaged in fluorescence with TrackMate-Cellpose
<p>Breast cancer cells migrating collectively.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells.</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking
<p>Human fibrosarcoma HT1080WT (ATCC) cells at low cell densities embedded in 3D collagen type I matrices [1]. The time-lapse videos were recorded every 2 minutes for 16.7 hours and covered a field of view of 1002 pixels × 1004 pixels with a pixel size of 0.802 μm/pixel The videos were pre-processed to correct frame-to-frame drift artifacts, resulting in a final size of 983 pixels × 985 pixels pixels.</p> <p><em>Hasini Jayatilaka, Anjil Giri, Michelle Karl, Ivie Aifuwa, Nicholaus J Trenton, Jude M Phillip, Shyam Khatau, and Denis Wirtz. EB1 and cytoplasmic dynein mediate protrusion dynamics for efficient 3-dimensional cell migration. FASEB J., 32(3):1207–1221, 2018. ISSN 0892-6638. doi: 10.1096/fj.201700444RR.</em></p> <p>Further information about how to use this data is given in <a href="http://github.com/esgomezm/microscopy-dl-suite-tf">https://github.com/esgomezm/microscopy-dl-suite-tf</a></p> <p><strong>This dataset is provided together with the following preprint and if you use it, we would like to kindly ask you to cite it properly:</strong></p> <p><a href="https://arxiv.org/abs/2112.08817">Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek, Thomas Brox, Denis Wirtz, Arrate Muñoz-Barrutia, *Search for temporal cell segmentation robustness in phase-contrast microscopy videos*, arXiv 2021 (arXiv:2112.08817)</a></p>
Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)
<p>This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software.</p> <p>Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.</p>
Sequence tracks of Ago2 Neural stem cells and differentiated neurons from single-cells- Related to Fig. 5
<p>Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Ago2 transcript which was then sent off for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</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>
Single-particle tracking data for "CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning"
<p>This dataset contains all the raw SPT data reported in "­­­­CTCF sites display cell cycle dependent dynamics in factor binding and nucleosome positioning" in the form of SPT trajectories. The SPT trajectories are provided in two different formats for convenience: a CSV format and a Matlab format. Both formats are readable by Spot-On: https://spoton.berkeley.edu/</p> <p>The SPT data contains "fast tracking" spaSPT data and this data was analyzed using the Matlab version of Spot-On which can be found and downloaded at: https://gitlab.com/tjian-darzacq-lab/spot-on-matlab</p> <p> </p> <p>Full details about the Matlab and CSV formats are provided in the ReadMe files in the associated zip files.</p> <p>Please see the associated manuscript for a detailed description of how the data was acquired and analyzed.</p>
Tracking Data II/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><strong>PLEASE NOTE: this repository contains only the folder compare_postprocessing_synth_bm </strong></p> <p><strong>All other datasets and files are provided in 10.5281/zenodo.5227595 due to size restrictions.</strong></p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p> </p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [will be stored in 10.5281/zenodo.5227610 due to size restrictions]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p> <p> </p> <p> </p>
Tracking with TrackMate using mask images of cell migration
<p>Tutorial dataset used to show how to use mask images for tracking with TrackMate.</p> <p>Two movies are provided, one small and one large to play with.</p> <p>For more information, check here: https://imagej.net/plugins/trackmate/trackmate-mask-detector</p> <p> </p>
STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images
<p>The datasets consist of time-lapse images underlying the research article "STrack: A tool to Simply Track bacterial cells in microscopy time-lapse images". Please visit the STrack github page for instructions on how to install and use STrack to track cells in images containing segmented cell masks: https://github.com/Helena-todd/STrack</p> <p>The data was generated at the Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland, by Tania Miguel Trabajo. The datasets are organised in four folders, one per bacterial species (<em>Pseudomonas putida, Pseudomonas veronii, Rahnella and Lysobacter</em>), that each contain 5 time-lapse datasets. Each of the 20 folders is organised in two subfolders, containing:</p> <p>- the raw, phase contrast, timelapse images (taken with a Nikon ECLIPSE Ti Series inverted microscope coupled with a Hamamatsu C11440 22CU camera and a Nikon CFI Plan Apo Lambda 100X Oil objective)</p> <p>- the manually segmented masks (manually generated using the QuPath open-source software for bioimage analysis )</p> <p>Dowload and unzip to view the contents.</p>
Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
<p>Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. Here, we defined gene expression and cell motion states using single cell RNA sequencing data and in vivo timelapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we utilized the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arises from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.</p>
Single cell FUCCI tracking data
<p>These datasets contain single cell FUCCI tracking data for the data analysis R scripts in the FUCCI_analysis folder in the heldring-E2S-modeling GitHub repository available via the persistent link https://doi.org/10.5281/zenodo.8143692.</p>
Drosophila Cell Tracking
<p>This 3D+t dataset shows an excerpt of a developing fruit fly (Drosophila melanogaster) embryo over 100 time steps during gastrulation. The movie has been recorded using a light sheet microscope. We provide dense manual annotations for cell lineages for all ~800 cells per frame.</p> <p><a href="../api/records/8032735/draft/files/benchmark-dataset-abstract-2.png/content" target="_blank" rel="noopener">Preview Image</a></p> <p>The movie of this developing <em>Drosophila melanogaster</em> embryo has been recorded by the <a href="https://www.embl.de/research/units/cbb/hufnagel/">Hufnagel group, EMBL Heidelberg, Germany</a>, with the light sheet microscope described in (<a href="http://www.nature.com/nmeth/journal/v9/n7/abs/nmeth.2064.html">Krzic et al., 2012</a>).</p> <p>The challenging task is to automatically segment and track all cells over all time steps to reconstruct full cell lineages.</p> <p>To acquire manual cell tracking annotations, we first segmented this dataset using <a href="http://ilastik.org/">ilastik</a> and refined the result with a seeded watershed. On average, this yielded ~800 cells per frame, which we then tracked manually over all 100 time steps using the <a href="http://ilastik.org/documentation/tracking/tracking.html#sec_manual">Manual Tracking workflow in ilastik</a>. We here provide the raw data, our segmentation and our manual annotations for benchmarking purposes.</p> <p> </p>
Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
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Cell tracking data from: Automated timelapse data segmentation reveals in vivo cell state dynamics
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Grid cells accurately track movement during path integration-based navigation despite switching reference frames
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4D light sheet imaging, computational reconstruction, and cell tracking in mouse embryos -- example data (raw .czi and fused .klb light sheet images of mouse E7.5)
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Data from: In vivo tracking of dendritic cell using MRI reporter gene, ferritin
The noninvasive imaging of dendritic cells (DCs) migrated into lymph nodes (LNs) can provide helpful information on designing DCs-based immunotherapeutic strategies. This study is to investigate the influence of transduction of human ferritin heavy chain (FTH) and green fluorescence protein (GFP) genes on inherent properties of DCs, and the feasibility of FTH as a magnetic resonance imaging (MRI) reporter gene to track DCs migration into LNs. FTH-DCs were established by the introduction of FTH and GFP genes into the DC cell line (DC2.4) using lentivirus. The changes in the rate of MRI signal decay (R2*) resulting from FTH transduction were analyzed in cell phantoms as well as popliteal LN of mice after subcutaneous injection of those cells into hind limb foot pad by using a multiple gradient echo sequence on a 9.4 T MR scanner. The transduction of FTH and GFP did not influence the proliferation and migration abilities of DCs. The expression of co-stimulatory molecules (CD40, CD80 and CD86) in FTH-DCs was similar to that of DCs. FTH-DCs exhibited increased iron storage capacity, and displayed a significantly higher transverse relaxation rate (R2*) as compared to DCs in phantom. LNs with FTH-DCs exhibited negative contrast, leading to a high R2* in both in vivo and ex vivo T2*-weighted images compared to DCs. On histological analysis FTH-DCs migrated to the subcapsular sinus and the T cell zone of LN, where they highly expressed CD25 to bind and stimulate T cells. Our study addresses the feasibility of FTH as an MRI reporter gene to track DCs migration into LNs without alteration of their inherent properties. This study suggests that FTH-based MRI could be a useful technique to longitudinally monitor DCs and evaluate the therapeutic efficacy of DC-based vaccines.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.