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.&nbsp;</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).&nbsp;</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 &micro;m/s (p1), 200 &micro;m/s (p2), 100 &micro;m/s (p3) and 400 &micro;m/s (p4).&nbsp;</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>&nbsp;</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.&nbsp;</p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79&nbsp;</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&nbsp;Follain,&nbsp;Sujan&nbsp;Ghimire,&nbsp;Joanna W.&nbsp;Pylv&auml;n&auml;inen,&nbsp;Monika&nbsp;Vaitkevičiūtė,&nbsp;Diana&nbsp;Wurzinger,&nbsp;Camilo&nbsp;Guzm&aacute;n,&nbsp;James RW&nbsp;Conway,&nbsp;Michal&nbsp;Dibus,&nbsp;Sanna&nbsp;Oikari,&nbsp;Kirsi&nbsp;Rilla,&nbsp;Marko&nbsp;Salmi,&nbsp;Johanna&nbsp;Ivaska,&nbsp;Guillaume&nbsp;Jacquemet</div> <div>bioRxiv&nbsp;2024.09.30.615654;&nbsp;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>