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.
Highlights
The advent of sophisticated camera sensors integrated into time-lapse microscopy (TLM) devices coupled with modern software tools for video and data analysis, allowed increasing capabilities to “see in the deep” [1].Sensors 2020, 20, 1531; doi:10.3390/s20051531 www.mdpi.com/journal/sensorsThanks to these facilities, the cellular microenvironment and related biological mechanisms can be investigated online during the phenomena evolution, letting research to understand the dynamics of the processes involved for a deeper understanding of the biological mechanism
Starting from the hypothesis that each single motion kind carried a different representation of the information of the cell, and of its interaction with the environment [19,20], the most innovative aspect in this work is that we considered each single sub-track as a sort of sensor (Cell Motion Sensor, CMS)
The present study aims at providing a preliminary investigation of the effect of chemotherapy on cancer cell motility during initial time steps after treatment
Summary
Thanks to these facilities, the cellular microenvironment and related biological mechanisms can be investigated online during the phenomena evolution, letting research to understand the dynamics of the processes involved for a deeper understanding of the biological mechanism. The online analysis allowed by such sensors permits us to follow and analyze cellular movement in the environment, and to further extract quantitative kinematics descriptors from the cell trajectories, giving to cell motility a crucial role. The individual cells can be considered themselves as a sort of sensitive element of a sensor that uses the camera as a transducer, and provides the movement of the cell as an output signal that is able to retrieve information about the chemical composition of the close environment. Cell-based sensors have been largely used in cellular physiological parameter detection, human olfactory mimicking systems [2,3,4,5,6], the treatment effect analysis, an environmental toxicity test, and immunotherapy efficacy [7,8,9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
