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111 results for “Cell Segmentation”
Nucleus and cell segmentations for data in the mudRapp-seq paper
<p>Segmentation masks for images published with the paper describing </p> <p>"<em>Multiple direct RNA padlock probing in combination with in-situ sequencing (mudRapp-seq)</em>":</p> <blockquote> <p>Ahmad S, Gribling-Burrer AS, Schaust J, Fischer SC, Ambil UB, Ankenbrand MJ, Smyth RP. <em>Visualizing the transcription and replication of influenza A viral RNAs in cells by multiple direct RNA padlock probing and in-situ sequencing (mudRapp-seq)</em> (in review)</p> </blockquote> <p>Raw images are published in the <a href="https://www.ebi.ac.uk/bioimage-archive/">Bioimage Archive</a> (identifier pending). To use these masks, run the data formatting code in the accompanying code repository to get the raw data in the correct structure and extract this zip archive into the repository root (the folder structure in the archive matches the folder structure of the repository).</p> <p>Filenames in `analysis/segmentation` contain a hint about how they were created:</p> <ul> <li>cp: direct segmentation with a cellpose model (<a href="https://github.com/BioMeDS/mudRapp-seq/blob/main/models/cellpose/nuclei">nuclei</a>, <a href="https://github.com/BioMeDS/mudRapp-seq/blob/main/models/cellpose/cells">cells</a>)</li> <li>cpws: cell segmentation through watershed with nucleus masks as seeds</li> <li>cpmc: manually corrected cellpose segmentations</li> </ul> <p>Besides the final segmentation masks, the training data are included in `data/training` and the models in `models/cellpose`.</p> <p>Changes:</p> <ul> <li>v1.1 training data and models added</li> </ul>
Deep learning segmentation projects of FIB-SEM dataset of U2-OS cell
<p>This submission includes ground truth datasets that were used to segment the nuclear envelope (NE), mitochondria, endoplasmic reticulum (ER) and Golgi from a human bone osteosarcoma epithelial cell (U2-OS) imaged using focused-ion beam scanning electron microscopy (FIB-SEM).</p><p>The full FIB-SEM dataset is deposited to EMPIAR (<a href="https://www.ebi.ac.uk/empiar">https://www.ebi.ac.uk/empiar</a>, EMPIAR-11746). </p>
Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'
<p><strong>Self-supervised machine learning code and data for segmenting live cell imagery (Matlab)</strong></p> <p><em>Running the Code</em></p> <p>SSL_Demo_2.m : main program for self-supervised machine learning segmentation</p> <p>SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m)</p> <p>This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background.</p> <p>It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure.</p> <p>Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run.</p> <p><em>Code Methodology</em></p> <p>The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required.</p> <p>Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination.</p> <p>The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy.</p> <p>Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions.</p> <p>The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing): </p> <p><a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a></p> <p>This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.</p>
CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images
<p>This is the dataset from the preprint "Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images" by Raufeisen et al. (2024). It contains 6,683 annotations (3,991 nuclei and 2,607 whole cells) of head and neck squamous cell carcinoma cells in hematoxylin and eosin stained histological images. The annotations are in COCO format and distributed over 83 PNG images. Cyto R-CNN was trained on this dataset and compared with other state-of-the-art methods. The CytoNuke dataset is released under the CC BY 4.0 license.</p> <p>The histological images are from the CPTAC dataset:<br>National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC). (2018). The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC) (Version 15) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2018.UW45NH81</p> <p>Funding: Behrus Puladi was funded by the Medical Faculty of RWTH Aachen University as part of the Clinician Scientist Program. We acknowledge FWF enFaced 2.0 [KLI 1044, https://enfaced2.ikim.nrw/] and KITE (Plattform für KI-Translation Essen) from the REACT-EU initiative [https://kite.ikim.nrw/, EFRE-0801977]. Fabian Hörst, Jianning Li, Jens Kleesiek and Jan Egger received funding from the Cancer Research Center Cologne Essen (CCCE).</p>
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç6.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, ventral view; B, segments 4 and 5, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; pac, pachycyclus; pf, pectinate fringe; rss, rounded sensory spot.
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç5.Ec hinoderes ohtsukai sp. nov., holotype, male (ZIHU 3976), Nomarski photomicrographs. A, Segments 1 and 2, dorsal view; B, segment 4, dorsal view. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; mds, middorsal spine; pac, pachycyclus; pf, pectinate fringe; ps, perforation site; rss, rounded sensory spot.
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç7.Ec hinoderes ohtsukai sp. nov., paratype, female (ZIHU 3980), Nomarski photomicrographs. A, Segments 5 and 6, ventral view; B, segments 8 and 9, ventral view. Abbreviations: dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; lvt, lateroventral tubule; si, sieve plate; sp, sternal plate; tp, tergal plate.
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç3.Ec hinoderes ohtsukai sp. nov., scanning electron micrographs. A, B, Paratype, female (ZIHU 3983); C–E, paratype, male (ZIHU 3982). A, General habitus, lateral view; B, neck and segments 1–4, lateral view; C, enlargement of segment 7, lateral view; D, enlargement of segment 9, lateral view; E, enlargement of segments 10 and 11, lateroventral view. Abbreviations: ch, cuticular hair; dss, droplet-shaped sensory spot; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; pf, pectinate fringe; po, pore; ps1, penile spine 1; ps2, penile spine 2; ps3, penile spine 3; rss, rounded sensory spot; si, sieve plate; ss, sensory spot.
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç2.Ec hinoderes ohtsukai sp. nov., camera lucida drawings. A, B, Holotype, male (ZIHU 3976), entire animal, dorsal and ventral view, respectively; C, D, allotype, female (ZIHU 3977), segments 9–11, dorsal and ventral view, respectively. Abbreviations: dss, droplet-shaped sensory spot; gco1, glandular cell outlet type I; gco2, modi ed glandular cell outlet type II; ldt, laterodorsal tubule; lts, lateral terminal spine; lvt, lateroventral tubule; mds, middorsal spine; ne, neck; ps, penile spine; rss, rounded sensory spot; si, sieve plate.
Cell Instance Segmentation Dataset
<p>This project contains the data for the paper:</p> <p>A. Bouyssoux, R. Fezzani and J. -C. Olivo-Marin, "<em>Cell Instance Segmentation Using Z-Stacks In Digital Cytopathology,</em>" 2022 IEEE International Symposium on Biomedical Imaging (ISBI), 2022.</p> <p>The code associated with this project is available at: <a href="https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation">https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation</a></p> <p>A new large Cell Instance Segmentation Dataset (CISD) is introduced. It comprises 3911 samples containing at least two touching or overlapping urothelial cells. Cell instances were manually annotated by trained cytotechnicians. All samples are extracted from 30 digital cytology slides stained with nine variations of Papanicolaou staining. The cytology slides are prepared from urine samples from healthy patients, using a Hologic ThinPrep®5000 processor, and routinely stained with the Agilent Dako CoverStainer®. The slides are finally digitized using a Hamamatsu NanoZoomer®S360 with 21 focal planes and centered on the best focus plane determined by the scanner autofocus.</p> <p>Note that cell instances with a bounding box width/height smaller than 10% of the sample width/height, as well as red blood cells and neutrophil cells were automatically filtered out because under-represented in the available data.</p> <p>Each sample is considered in three different manners in the CISD, allowing experimentation with different methods<br> for handling Z-stack data and comparison with simple 2D acquisition:</p> <ul> <li>Center slice: the best focus plane only as determined by the scanner, which is equivalent to a 2D slide acquisition.</li> <li>Extended Depth of Field (EDF): the 21 planes merged in an image where all textured parts appear in focus.</li> <li>Raw Z-stack: the volume composed of the 21 focal planes.</li> </ul> <p>Once extracted, the dataset folder contains three subfolders, one for each of the three types of samples described here above, containing the images and stack of images. A JSON file contains the instance masks, encoded in RLE format.</p>
Text-fig. 6. Scanning electron micrographs of seeds of ericalean affinity (a–d) and seeds of uncertain affinity (e–i) from Zliv-Řídká Blana locality. a: Protovisnea sp. 1, rounded seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3177; b: Protovisnea sp. 2, angular seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3179; c, d: Eurya crassitesta, one seed split into two parts, no. NM-F 3211, c – surface cells of the seed coat are palisade, d – cross-section of the seed; e: Nympheaceae sp. 1, seed, no. NM-F 3636; f: Nympheaceae sp. 2, seed, no. NM-F 4634; g: Klikovispermum sp.1, seeds with irregular outline and smooth outer surface, no. NM-F 3203; h: Klikovispermum malechii, seed with an orange-segment shape, no. NM-F 3299; i: Taxon 35, seed, no. NM-F 3236. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 6. Scanning electron micrographs of seeds of ericalean affinity (a–d) and seeds of uncertain affinity (e–i) from Zliv-Řídká Blana locality. a: Protovisnea sp. 1, rounded seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3177; b: Protovisnea sp. 2, angular seed with the narrow elongate seed cavity flanked by two bulging regions of larger cells, no. NM-F 3179; c, d: Eurya crassitesta, one seed split into two parts, no. NM-F 3211, c – surface cells of the seed coat are palisade, d – cross-section of the seed; e: Nympheaceae sp. 1, seed, no. NM-F 3636; f: Nympheaceae sp. 2, seed, no. NM-F 4634; g: Klikovispermum sp.1, seeds with irregular outline and smooth outer surface, no. NM-F 3203; h: Klikovispermum malechii, seed with an orange-segment shape, no. NM-F 3299; i: Taxon 35, seed, no. NM-F 3236.
MSVermet/Cell-Nuclei-Detection-And-Segmentation-CENet-UNet: Cell nuclei detection and segmentation
<p>Fully automated nuclei detection and segmentation with CE-Net and U-Net on the PanNuke dataset. Repository includes code and dataset.</p>
Tracking Data I/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><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><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></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 [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<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>
Outputs from new methods for 3D+time cell image segmentation and tracking
<p>Segmentation and tracking of 3D+time microscopy images of cell nuclei within the zebrafish pectoral fin.</p> <p>The file named 7_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named 7_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for seven cells (colored black) moving in time, and the file _tracking_of_7_cell_in_70_frames.mp4 has the tracking of these seven cells. </p> <p>Additionally, the file named group_of_cells_moving_in_70_frames_orig.avi is a 70-frame video of a group of cells moving in time, the file named group_of_cells_moving_in_70_frames.avi has the result of 4D segmentation, using our new segmentation methods, for the group of cells (colored black) moving in time and the file _tracking_of_group_of_cell_in_70_frames.gif has the tracking of this group of cells. </p>
Dual-modality imaging of immunofluorescence and imaging mass cytometry for high-resolution whole slide imaging with accurate single-cell segmentation
<p>Imaging mass cytometry (IMC) is a powerful multiplexed tissue imaging technology that allows simultaneous detection of more than 30 makers on a single slide. It has been increasingly used for single-cell based spatial phenotyping in a wide range of samples. However, it only acquires a small, rectangle field of view (FOV) with a low image resolution that hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dementional IMC on the same tissue slide. Our computational pipeline uses the whole slide image (WSI) of IF as spatial reference, integrates small FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images and demonstrated the advantage of the dual-modality imaging strategy.</p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Lysosome training dataset for live cell anatomy segmentation
Open the record for dataset details and reuse information.
ER training dataset for live cell anatomy segmentation
Open the record for dataset details and reuse information.
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
KerNet, 3D fluorescence and segmentation datasets of keratin networks from MDCK, HaCaT, and RPE cells
<p><span>Mechanobiology requires precise quantitative spatial information on processes taking place in specific 3D microenvironments. Connecting microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps is particular challenging for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. Here we present raw data and processed segmented data of fluorescence-tagged keratin intermediate filaments from MDCK, HaCaT and RPE epithelial cells. The 30 datasets contain unprocessed airyscan superresolution 3D image data and numerical network data of the 3D arrangement of the keratin intermediate filament cytoskeleton. Information is provided on network organization at the subcellular level, including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. The datasets contain data in exchange format for 3d software packages. Comparison of the resulting network parameters helps to identify similarities and differences of keratin network organization in epithelial cell types. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested. </span></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.