Dept. Electronic Engineering, University of Rome Tor Vergata, 00133 Roma, Italy.
Dept. of Chemical Science and Technologies, University of Rome Tor Vergata, 00133 Roma, Italy.
Sensors (Basel). 2020 Mar 10;20(5):1531. doi: 10.3390/s20051531.
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.
细胞运动是细胞状态及其与周围环境相互作用的卓越结果。由于高分辨率相机传感器、延时显微镜设备以及用于视频和数据分析的专用软件工具的协同作用，现在可以检测到细胞运动。在这种情况下，我们提出了一种新的范例，即将单个细胞视为传感器的一种敏感元件，该元件利用相机作为换能器，将细胞的运动作为输出信号返回。通过这种方式，细胞运动使我们能够获取有关周围环境化学成分的信息。为了最佳地利用这些信息，在这项工作中，我们引入了一种新的设置，其中将细胞轨迹分为子轨迹，每个子轨迹都具有特定的运动类型。因此，我们将单细胞轨迹的所有子轨迹视为基于细胞运动的虚拟传感器阵列的信号。每个子轨迹的运动学被量化，并用于分类任务。为了研究所提出方法的潜力，我们将所获得的性能与使用单轨迹范例进行比较，目的是评估化疗对前列腺癌细胞的治疗效果。新的模式识别算法已应用于从子轨迹级别提取的描述符，并实施了特征选择（良好教师学习方法）以进行模型构建。实验结果表明，当考虑到进一步的聚类多数角色时，通过模拟某种传感器融合过程，性能会更高。所有这些结果都突显了所提出方法的强大优势，并直接预示着它在微流控芯片或器官上芯片应用中的使用，在这些应用中，可以使用延时显微镜图像对细胞运动分析进行大规模应用。
