Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDiscovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments
Anno di pubblicazione2020
Formato-
Autore/iMencattini, A.; Di Giuseppe, D.; Comes, M. C.; Casti, P.; Corsi, F.; Bertani, F. R.; Ghibelli, L.; Businaro, L.; Di Natale, C.; Parrini, M. C.; Martinelli, E.
Affiliazioni autoriUniv Roma Tor Vergata; Univ Roma Tor Vergata; Italian Natl Res Council; Univ Roma Tor Vergata; Paris Sci & Lettres Res Univ
Autori CNR e affiliazioni
  • FRANCESCA ROMANA BERTANI
  • LUCA BUSINARO
Lingua/e
  • inglese
AbstractWe 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.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da-
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Pagine totali11
RivistaScientific reports (Nature Publishing Group)
Attiva dal 2011
Editore: Nature Publishing Group - London
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 2045-2322
Titolo chiave: Scientific reports (Nature Publishing Group)
Titolo proprio: Scientific reports (Nature Publishing Group)
Numero volume della rivista10
Fascicolo della rivista1
DOI10.1038/s41598-020-64246-3
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000534024000020)
Parole chiaveDeep Learning, Neural Network, Image analysis, time lapse microscopy, cell motility
Link (URL, URI)https://www.nature.com/articles/s41598-020-64246-3
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFN — Istituto di fotonica e nanotecnologie
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
Allegati
Mencattini_2020_Sci rep (documento privato )
Descrizione: pdf del lavoro
Tipo documento: application/pdf