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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Mencattini A,Di Giuseppe D,Comes MC,Casti P,Corsi F,Bertani FR,Ghibelli L,Businaro L,Di Natale C,Parrini MC,Martinelli Edoi
10.1038/s41598-020-64246-3subject
Has Abstractpub_date
2020-05-06 00:00:00pages
7653issue
1issn
2045-2322pii
10.1038/s41598-020-64246-3journal_volume
10pub_type
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