Hyaluronidase treatment perfusion tracking dataset

Description

This dataset contains tracking results of different combinations of Hyaluronidase-treated AsPC1 and MiaPaca cells perfused on Hyaluronidase-treated endothelial monolayer 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, and 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).   Spesifications 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).  Hyaluronidase treatment PDACs or HUVECs 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 dataset As_ctrl.zip As_HUdigestion.zip As_TCdigestion.zip Mia_ctrl.zip Mia_HUdig.zip Mia_TCdig.zip   Reference Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers 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 bioRxiv 2024.09.30.615654; doi: https://doi.org/10.1101/2024.09.30.615654
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Year of publication

2024

Type of data

Authors

Zenodo - Publisher

Gautier Follain - Creator

Guillaume Jacquemet Orcid -palvelun logo - Creator

Joanna Pylvänäinen Orcid -palvelun logo - Creator

Johanna Ivaska - Creator

Sujan Ghimire Orcid -palvelun logo - Creator

Project

Other information

Fields of science

Biochemistry, cell and molecular biology

Language

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

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