Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

Journal: Scientific reports
Published Date: May 6, 2020

Abstract

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style transfer. The originality of the method relies i) on the generation of atlas from the collection of single-cell trajectories in order to visually encode the multiple descriptors of cell motility, and ii) on the application of pre-trained Deep Learning Convolutional Neural Network architecture in order to extract relevant features to be used for classification tasks from this visual atlas. Validation tests were conducted on two different cell motility scenarios: 1) a 3D biomimetic gels of immune cells, co-cultured with breast cancer cells in organ-on-chip devices, upon treatment with an immunotherapy drug; 2) Petri dishes of clustered prostate cancer cells, upon treatment with a chemotherapy drug. For each scenario, single-cell trajectories are very accurately classified according to the presence or not of the drugs. This original approach demonstrates the existence of universal features in cell motility (a so called "motility style") which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories.

Authors

  • A Mencattini
    Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • D Di Giuseppe
    Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • M C Comes
    Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • P Casti
    Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • F Corsi
    Department of Chemical Science and Technologies, University of Rome Tor Vergata, Rome, Italy.
  • F R Bertani
    Institute for Photonics and Nanotechnology, Italian National Research Council, 00156, Rome, Italy.
  • L Ghibelli
    Department of Biology, University of Rome Tor Vergata, Rome, Italy.
  • L Businaro
    Institute for Photonics and Nanotechnology, Italian National Research Council, 00156, Rome, Italy.
  • C Di Natale
    Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
  • M C Parrini
    Institute Curie, Centre de Recherche, Paris Sciences et Lettres Research University, 75005, Paris, France.
  • E Martinelli
    Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico, 1 - 00133, Rome, Italy.

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