Studying cancer cell metastasis in the era of deep learning for microscopy

Leukocyte extravasation is a critical component of the innate immune response, while circulating tumor cell extravasation is a crucial step in metastasis formation. Despite their importance, leukocyte and tumor cell extravasation mechanisms remain incompletely understood. In this talk, we present a novel imaging framework that integrates microfluidics with high-speed, label-free imaging to study the arrest of pancreatic cancer cells (PDAC) on human endothelial layers under physiological flow conditions. To facilitate the analysis of our imaging data, we take advantage of a suite of open-source image analysis tools we developed, including ZeroCostDL4Mic and DL4MicEverywhere for deep learning-based labeling and segmentation, TrackMate v7 for enhanced cell tracking, and CellTracksColab for compiling and analyzing tracking data. Our findings reveal that PDAC cells exhibit variable adhesion strengths and sensitivities to flow, with some cells preferentially arresting at endothelial junctions similarly to leukocytes. This location provides PDAC cells access to the underlying basal extracellular matrix. PDAC cells also tend to cluster in regions with high expression of the CD44 receptor on endothelial cells. Both simulations and experimental data suggest that CD44-mediated attachment and localized flow disturbances enhance subsequent cell adherence. Targeting CD44 via siRNA, function-blocking antibodies, or degradation of its ligand hyaluronic acid (HA) almost completely abolishes PDAC cell attachment to endothelial monolayers. By combining advanced imaging strategies and developing image analysis software, we are advancing our understanding of cancer cell extravasation.

  • Keynote
  • Live-cell Imaging
  • Cancer
  • AI

Speaker

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