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122 results for “CD44”
"The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" ("Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro"); NCN Miniatura 2022/06/X/NZ3/00848
<p>Results from Screening for "The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" the project <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) funded by Polish <strong>National Science Centre (NCN)</strong></p> <p>Wyniki skriningu w projekcie "Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro", <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) finansowanym przez <strong>Narodowe Centrum Nauki (NCN)</strong></p>
Dataset related to article "The soluble glycoprotein NMB (GPNMB) produced by macrophages induces cancer stemness and metastasis via CD44 and IL-33"
<p>This record contains data related to article “The soluble glycoprotein NMB (GPNMB) produced by macrophages induces cancer stemness and metastasis via CD44 and IL-33"</p> <p> </p> <p>Abstract</p> <p>In cancer, myeloid cells have tumor-supporting roles. We reported that the protein GPNMB (glycoprotein nonmetastatic B) was profoundly upregulated in macrophages interacting with tumor cells. Here, using mouse tumor models, we show that macrophage-derived soluble GPNMB increases tumor growth and metastasis in Gpnmb-mutant mice (DBA/2J). GPNMB triggers in the cancer cells the formation of self-renewing spheroids, which are characterized by the expression of cancer stem cell markers, prolonged cell survival and increased tumor-forming ability. Through the CD44 receptor, GPNMB mechanistically activates tumor cells to express the cytokine IL-33 and its receptor IL-1R1L. We also determined that recombinant IL-33 binding to IL-1R1L is sufficient to induce tumor spheroid formation with features of cancer stem cells. Overall, our results reveal a new paracrine axis, GPNMB and IL-33, which is activated during the cross talk of macrophages with tumor cells and eventually promotes cancer cell survival, the expansion of cancer stem cells and the acquisition of a metastatic phenotype.</p> <p> </p>
CD44: A Target Enabling Package
<p>CD44 is a transmembrane receptor that signals through binding hyaluronic acid (HA) with its ectodomain. The intracellular C-terminal tail associates with a variety of factors, including Moesin (MSN), an F-actin binding protein. Through MSN and HA binding, CD44 can link the extracellular matrix to the cytoskeleton. Investigation of the Alzheimer’s Disease (AD) brain proteome, along with weighted co-expression network analysis, revealed a module enriched with proteins involved in inflammation. CD44 (along with MSN) is a key driver of this module, increasing in abundance in asymptomatic and symptomatic AD patients. The aim of the TEP is to produce reagents to test the hypothesis that inhibiting CD44 ectodomain binding to HA may reduce CD44 signalling and inflammation in the brain, limiting neuronal damage in AD patients.</p>
CD44 / CCP4 tutorial dataset
<p>A rotation data set from a protein crystal of selenomethionine-containing CD44, plus the amino acid sequence of CD44. This can be used for training diffraction data processing and phasing. The data set was collected at the ESRF beamline ID14-4 on the 3rd May 2002.</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Crystallographic Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of crystallographic mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Umbrella Sampling Data, Parallel Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "Free Energy" simulations of parallel mode in Ref. [1]. </p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>'pullx' ('and pullx2' files, which contain data from the last 80ns) are used to constuct the free energy profile. Command for building the free energy profile is included in 'wham.sh'</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Upright Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "E-field weak, upright mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc.</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Parallel Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "E-field weak, parallel mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc.</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Weak E-field Simulations, Crystallographic Mode
<p>Simulation files (Gromacs 4.6.7 format) for the "E-field weak, crystallographic mode" simulations in Ref. [1]. There are 20 replicas marked with "_1" , "_2", etc.</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Unbound Simulations, Set 2
<p>Simulation files (Gromacs 4.6.7 format) for the "Unbound" simulations in Ref. [1]. There are two replicas marked with "_4" and "_5".</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Unbound Simulations, Set 1
<p>Simulation files (Gromacs 4.6.7 format) for the "Unbound" simulations in Ref. [1]. There are three replicas marked with "_1" , "_2", and "_3".</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Clustering Simulations
<p>Simulation files (Gromacs 4.6.7 format) for the "Clustering" simulations in Ref. [1]. There are two replicas marked with "_1" and "_2".</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Strong E-field Simulations
<p>Simulation files (Gromacs 4.6.7 format) for the "E-field strong" simulations in Ref. [1]. There are four different systems ("Crystallographic A-form", "Crystallographic B-form", "Parallel", "Upright") with 20 replica simulations in each (marked with "_1" etc.).</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Seeding Simulations
<p>Simulation files (Gromacs 4.6.7 format) for the "Seeding" simulations in Ref. [1].</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Atomistic Fingerprint of Hyaluronan-CD44 Binding: Gathering Simulations
<p>Simulation files (Gromacs 4.6.7 format) for the "Gathering" simulations in Ref. [1].</p> <p>Files include:</p> <p>-trajectories (.xtc) that are saved every 100ps <br> -initial structures (.gro), <br> -run input files (.tpr)<br> -checkpoint files (.cpt)<br> -simulation parameter files (.mdp)<br> -system topology file (.top)<br> -topology files included in the system topology file (.itp)</p> <p>[1] Vuorio J. et al., Atomistic Fingerprint of Hyaluronan-CD44 Binding, PLOS Comp. Biol., 2017. (Submitted)</p>
Molecular dynamics of LYVE-1 and CD44 in complex with hyaluronan
<p>Input and output data for MD of mouse/human LYVE-1 and mouse CD44, both, as apo proteins (PDB codes 8ORX, 8OS2, 2JCP, respectively) and in complex with hyaluronan hexasaccharide (HA6; PDB codes 8OX3, 8OXD, 2JCR, respectively). AMBER parm7 topology (.top), restart (.rst), minimisation and MD inputs (.tin, .in), binary NETCDF trajectories (.netcdf) and volumetric maps (.dx) are shared.</p> <p>LYVE-1/CD44 input structures in their apo forms or with HA6 bound were immersed in an octahedral box of TIP3P water molecules and 150 mM NaCl was added. Hydrogen mass repartitioning to 3Da enabled us to use a time step of 4 fs. A stepwise relaxation protocol using sander.MPI of AMBER20 was followed by 1 µs MD production run using pmemd.cuda of AMBER20. Trajectories were first analysed for structural stability using RMSD metrics by use of cpptraj of AMBER20. Due to the high flexibility of the systems, we analysed only portions of 500 ns length of residues 29 to 138, 24 to 133 and 25 to 134 for mLYVE-1/hLYVE-1/CD44, respectively. The following hydrogen-bonding criteria were used: 3.6 Å cutoff for acceptor‧‧‧donor distance and 120-180º range for acceptor‧‧‧H-donor angle. Bridging water molecule occupancies were calculated by summing up binary, ternary and quaternary interactions (the cutoff for each was set to a minimum of 10 %).</p>
Stardist_MiaPaCa2_from_CD44
<p>This repository contains a StarDist deep learning model designed for segmenting MiaPaCa2 cells from the CD44 channel in fluorescence microscopy images. The model is capable of accurately segmenting individual MiaPaCa2 cells while excluding HUVECs. Trained on a small dataset, the model achieved an Intersection over Union (IoU) score of 0.884 and an F1 Score of 0.950, indicating high precision in cell segmentation.</p> <h3>Specifications</h3> <ul> <li> <p>Model: StarDist for segmenting MiaPaCa2 cells from the CD44 fluorescence channel</p> </li> <li> <p>Training Dataset:</p> </li> <ul> <li> <p>Number of Images: 8 paired fluorescence microscopy images and label masks</p> </li> <li> <p>Microscope: Spinning disk confocal microscope (3i CSU-W1) with a 20x objective, NA 0.8</p> </li> <li> <p>Data Type: Fluorescence microscopy images of the CD44 channel, obtained after immunofluorescence staining with primary and secondary antibodies and manually segmented masks</p> </li> <li> <p>File Format: TIFF (.tif)</p> </li> <ul> <li> <p>Fluorescence Images: 16-bit</p> </li> <li> <p>Masks: 8-bit</p> </li> </ul> <li> <p>Image Size: 920 x 920 pixels (Pixel size: 0.6337 x 0.6337 µm²)</p> </li> </ul> <li> <p>Model Capabilities:</p> </li> <ul> <li> <p>Segment MiaPaCa2 Cells: Accurately detects individual MiaPaCa2 cells while ignoring HUVECs</p> </li> <li> <p>Measure CD44 Intensity: Allows for the measurement of CD44 intensity around MiaPaCa2 cells, specifically from the CD44 channel</p> </li> </ul> <li> <p>Performance:</p> </li> <ul> <li> <p>Average IoU: 0.884</p> </li> <li> <p>Average F1 Score: 0.950</p> </li> </ul> <li> <p>Model Training: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
CD44 Blocking Antibody perfusion tracking dataset
<p>This dataset contains tracking results of different combinations of CD44 antibody-blocked AsPC1 and MiaPaca cells perfused on CD44 antibody-blocked endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3> Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 antibody blocking of PDACs, HUVECs, or both prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis_AsPC1.zip dataset</p> </li> <li> <p>Analysis_Miapaca.zip dataset</p> </li> <li> <p>As_blockboth.zip dataset</p> </li> <li> <p>As_ctrlblock.zip dataset</p> </li> <li> <p>As_HUblock.zip dataset</p> </li> <li> <p>As_TCblock.zip dataset</p> </li> <li> <p>Mia_blockboth.zip</p> </li> <li> <p>Mia_ctrlblock.zip</p> </li> <li> <p>Mia_HUblock.zip</p> </li> <li> <p>Mia_TCblock.zip</p> </li> </ul> <p><strong> </strong></p> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div> <p> </p>
HUVEC CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of AsPC1 and MiaPaca cells perfused on CD44 siRNA-silenced endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of the HUVEC monolayer</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_HUsi1.zip dataset</p> </li> <li> <p>As_HUsi2.zip dataset</p> </li> <li> <p>As_HUsi3.zip dataset</p> </li> <li> <p>As_HUsiCtrl.zip dataset</p> </li> <li> <p>Mia_HUsi1.zip dataset</p> </li> <li> <p>Mia_HUsi2.zip dataset</p> </li> <li> <p>Mia_HUsi3.zip dataset</p> </li> <li> <p>Mia_HUsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
PDAC cells CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). <strong> </strong></p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of PDACs prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3><strong> </strong>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_TCsi1.zip dataset</p> </li> <li> <p>As_TCsi2.zip dataset</p> </li> <li> <p>As_TCsi3.zip dataset</p> </li> <li> <p>As_TCsiCtrl.zip dataset</p> </li> <li> <p>Mia_TCsi1.zip dataset</p> </li> <li> <p>Mia_TCsi2.zip dataset</p> </li> <li> <p>Mia_TCsi3.zip dataset</p> </li> <li> <p>Mia_TCsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
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.