A Camera Sensors-Based System to Study Drug Effects on In Vitro Motility: The Case of PC-3 Prostate Cancer Cells

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.

Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia

“…RBCs are localized through the Circular Hough Transform, an algorithm specifically designed to localize almost circular object in a given scene 34 . This algorithm has already demonstrated to be efficient in localizing cancer cells and immune cells in previous work 35 – 37 . A ROI of fixed size is extracted around each localized cell (Fig.…”

Deciphering Cancer Cell Behavior From Motility and Shape Features: Peer Prediction and Dynamic Selection to Support Cancer Diagnosis and Therapy

“…The method is focused on the use of a previously validated cell tracking tool, Cell-Hunter, which has been tested in prostate cancer cell automatic tracking (12,19), immune-cancer cell crosstalk studies (16), and recently in red blood cell plasticity analysis (20). The software automatically locates cells with a radius within a given range provided by the user and tracks them providing a predetermined maximum displacement allowed.…”

“…Cells move according to the cluster they belong, promoting different roles according to the cell stage, age, drug absorption, etc. The automatic identification of the clusters each cell belongs to is performed through image analysis algorithms involving image binarization and morphological operators (12). The technique is based on the localization of individual cells by performing the segmentation of circular objects using the Circular Hough Transform (CHT) (21) set according to the mean estimated radius of cells involved.…”

Combining microfluidic spleen-like filtering unit with machine learning algorithms to characterize rare hereditary hemolytic anemia

“…26 This algorithm has already demonstrated to be e cient in localizing cancer cells and immune cells in previous work. [27][28][29] A ROI of xed size is extracted around each localized cell ( Figure 1D), frame by frame, by cropping the frame in a square region around the center of the cell. In this way, we collected two sets of ROIs.…”