A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells
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 form...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/5/1531 |
| _version_ | 1828364216880332800 |
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| author | Maria Colomba Comes Arianna Mencattini Davide Di Giuseppe Joanna Filippi Michele D’Orazio Paola Casti Francesca Corsi Lina Ghibelli Corrado Di Natale Eugenio Martinelli |
| author_facet | Maria Colomba Comes Arianna Mencattini Davide Di Giuseppe Joanna Filippi Michele D’Orazio Paola Casti Francesca Corsi Lina Ghibelli Corrado Di Natale Eugenio Martinelli |
| author_sort | Maria Colomba Comes |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:12:56Z |
| format | Article |
| id | doaj.art-c49d82c8b37344c6b1f156f312fa1934 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T05:12:56Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c49d82c8b37344c6b1f156f312fa19342022-12-22T02:10:29ZengMDPI AGSensors1424-82202020-03-01205153110.3390/s20051531s20051531A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer CellsMaria Colomba Comes0Arianna Mencattini1Davide Di Giuseppe2Joanna Filippi3Michele D’Orazio4Paola Casti5Francesca Corsi6Lina Ghibelli7Corrado Di Natale8Eugenio Martinelli9Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. of Chemical Science and Technologies, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Biology, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyDept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, ItalyCell 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.https://www.mdpi.com/1424-8220/20/5/1531camera sensorcell-motilitydrug effect on in-vitroprostate cancer cells |
| spellingShingle | Maria Colomba Comes Arianna Mencattini Davide Di Giuseppe Joanna Filippi Michele D’Orazio Paola Casti Francesca Corsi Lina Ghibelli Corrado Di Natale Eugenio Martinelli A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells Sensors camera sensor cell-motility drug effect on in-vitro prostate cancer cells |
| title | A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells |
| title_full | A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells |
| title_fullStr | A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells |
| title_full_unstemmed | A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells |
| title_short | A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells |
| title_sort | camera sensors based system to study drug effects on in vitro motility the case of pc 3 prostate cancer cells |
| topic | camera sensor cell-motility drug effect on in-vitro prostate cancer cells |
| url | https://www.mdpi.com/1424-8220/20/5/1531 |
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