PDAC cells CD44 siRNA perfusion tracking dataset

This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds. Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective. 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. 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). Specifications Sample information 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). CD44 siRNA silencing of PDACs prior to perfusion Imaging specs Microscope: Nikon Eclipse Ti2-E, 20x objective Data Type: Brightfield microscopy images (16-bit) Image Size: 1024 x 1022 pixels (Pixel size: 650 nm) Recording speed 25 frames/s DL models: Cancer cells: https://doi.org/10.5281/zenodo.10572122 Neutrophils: https://doi.org/10.5281/zenodo.10572231 Mononucleated cells: https://doi.org/10.5281/zenodo.10572200 Model Training and predictions: Conducted using ZeroCostDL4Mic (https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki) Tracking parameters (TrackMate): Detection: label detector Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. Track filtering: min number of spots in the tracks 11.79 Tracking analysis Tracks were analyzed using a customized CellTracksColab notebook (https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab) Contents of the repository Analysis.zip As_TCsi1.zip dataset As_TCsi2.zip dataset As_TCsi3.zip dataset As_TCsiCtrl.zip dataset Mia_TCsi1.zip dataset Mia_TCsi2.zip dataset Mia_TCsi3.zip dataset Mia_TCsiCtrl.zip dataset Reference Biorxiv paper