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.

Mencattini, A., Di Giuseppe, D., Comes, M.c., Casti, P., Corsi, F., Bertani, F.r., et al. (2020). Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments. SCIENTIFIC REPORTS, 10(1), 7653 [10.1038/s41598-020-64246-3].

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

Mencattini A.;Casti P.;Ghibelli L.;Di Natale C.;Martinelli E.
2020-01-01

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.
2020
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/07 - MISURE ELETTRICHE ED ELETTRONICHE
English
Algorithms
Antineoplastic Agents
Bioengineering
Cell Tracking
Humans
Molecular Imaging
Reproducibility of Results
Time-Lapse Imaging
Computational Biology
Drug Screening Assays, Antitumor
Machine Learning
Mencattini, A., Di Giuseppe, D., Comes, M.c., Casti, P., Corsi, F., Bertani, F.r., et al. (2020). Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments. SCIENTIFIC REPORTS, 10(1), 7653 [10.1038/s41598-020-64246-3].
Mencattini, A; Di Giuseppe, D; Comes, Mc; Casti, P; Corsi, F; Bertani, Fr; Ghibelli, L; Businaro, L; Di Natale, C; Parrini, Mc; Martinelli, E
Articolo su rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/289519
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