Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.

Comes, M.c., Mencattini, A., Di Giuseppe, D., Filippi, J., D'Orazio, M., Casti, P., et al. (2020). A camera sensors-based system to study drug effects on in vitro motility: The case of PC-3 prostate cancer cells. SENSORS, 20(5), 1531 [10.3390/s20051531].

A camera sensors-based system to study drug effects on in vitro motility: The case of PC-3 prostate cancer cells

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

Abstract

Cell motility is the brilliant result of cell status and its interaction with close environments. Its detection is now possible, thanks to the synergy of high-resolution camera sensors, time-lapse microscopy devices, and dedicated software tools for video and data analysis. In this scenario, we formulated a novel paradigm in which we considered the individual cells as a sort of sensitive element of a sensor, which exploits the camera as a transducer returning the movement of the cell as an output signal. In this way, cell movement allows us to retrieve information about the chemical composition of the close environment. To optimally exploit this information, in this work, we introduce a new setting, in which a cell trajectory is divided into sub-tracks, each one characterized by a specific motion kind. Hence, we considered all the sub-tracks of the single-cell trajectory as the signals of a virtual array of cell motility-based sensors. The kinematics of each sub-track is quantified and used for a classification task. To investigate the potential of the proposed approach, we have compared the achieved performances with those obtained by using a single-trajectory paradigm with the scope to evaluate the chemotherapy treatment effects on prostate cancer cells. Novel pattern recognition algorithms have been applied to the descriptors extracted at a sub-track level by implementing features, as well as samples selection (a good teacher learning approach) for model construction. The experimental results have put in evidence that the performances are higher when a further cluster majority role has been considered, by emulating a sort of sensor fusion procedure. All of these results highlighted the high strength of the proposed approach, and straightforwardly prefigure its use in lab-on-chip or organ-on-chip applications, where the cell motility analysis can be massively applied using time-lapse microscopy images.
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/07
English
Camera sensor
Cell-motility
Drug effect on in-vitro
Prostate cancer cells
Algorithms
Antineoplastic Agents
Biomechanical Phenomena
Cell Movement
Cluster Analysis
Humans
Image Processing, Computer-Assisted
Machine Learning
Male
Microscopy
Models, Statistical
Normal Distribution
PC-3 Cells
Pattern Recognition, Automated
Prostate
Prostatic Neoplasms
Software
Video Recording
Drug Screening Assays, Antitumor
Comes, M.c., Mencattini, A., Di Giuseppe, D., Filippi, J., D'Orazio, M., Casti, P., et al. (2020). A camera sensors-based system to study drug effects on in vitro motility: The case of PC-3 prostate cancer cells. SENSORS, 20(5), 1531 [10.3390/s20051531].
Comes, Mc; Mencattini, A; Di Giuseppe, D; Filippi, J; D'Orazio, M; Casti, P; Corsi, F; Ghibelli, L; Di Natale, C; Martinelli, E
Articolo su rivista
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/289527
Citazioni
  • ???jsp.display-item.citation.pmc??? 3
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 3
social impact