Learning Cancer-Related Drug Efficacy Exploiting Consensus in Coordinated Motility Within Cell Clusters

Giuseppe, Davide Di; Corsi, Francesca; Mencattini, Arianna; Comes, Maria Colomba; Casti, Paola; Natale, Corrado Di; Ghibelli, Lina; Martinelli, Eugenio

doi:10.1109/tbme.2019.2897825

Cited by 24 publications

(41 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study overcomes the criticisms and the limitations highlighted in the preliminary work by Di Giuseppe et al [26] that was aimed at verifying the effectiveness of cell motility to discover block replication effects on cancer cells. In particular, we present here a novel way to analyze the information content of cell trajectories, considering the track as the concatenation of separate signals that come from a different motion kind.…”

Section: Introductionmentioning

confidence: 70%

“…First, unsupervised cell clustering is applied by automatically assigning each cell to a specific cluster in the video sequence. Cells are then localized and tracked by Cell-Hunter software [26][27][28], in order to automatically identify a set of individual cell trajectories within the video. Each trajectory is further segmented into different sub-tracks, each representing a different Cell Motion Sensor (CMS).…”

Section: A Sketch Of the Methodsmentioning

confidence: 99%

“…As a second step, we applied the software Cell Hunter [26][27][28] for Single Particle Tracking (SPT) [31] to identify cell trajectories in the video. An example of this application is represented in Figure 2C.…”

Section: Automatic Clustering and Trackingmentioning

confidence: 99%

“…As a proof of concept, we here applied the proposed system to the study of the effects of a chemotherapeutic drug (topoisomerase II inhibitor etoposide) at different concentrations on prostate cancer cells PC-3 cultured in a 35 mm Petri dish, and spontaneously grouped into clusters [26]. It will be demonstrated that the division in sub-tracks allows for improving the recognition performance of the drug effect on the cell motility patterns (i.e., paradigm "sensor array"), with respect to the analysis of the kinematics descriptors computed on the entire cell track (i.e., paradigm "single cell-based sensor"); furthermore, we will also prove that the analysis of classification results mediated over the descriptors extracted at cluster levels can be the optimal solution (i.e., in a new paradigm of "sensor fusion" strategy).…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the present algorithm proposes a twofold change of perspective in cell motility investigation. In the first place, the novel association cell track-sensor array plays a key role in the better discrimination of the drug effect respect to Di Giuseppe et al [26]. Secondly, the division of cell trajectories in sub-tracks is not restricted to consider cells on its own as in Montiel et al [22] or Dosset et al [23].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Comes

Mencattini

Giuseppe

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 70%

Section: A Sketch Of the Methodsmentioning

confidence: 99%

Section: Automatic Clustering and Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Comes

Mencattini

Giuseppe

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In‐Flow Identification of Single Cells

Filippi,

Casti,

Antonelli

et al. 2024

Small Methods

View full text Add to dashboard Cite

A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi‐frequency cell trajectories. Like in a “ping‐pong” match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so‐derived cell motions are characterized via time‐lapse microscopy, cell tracking, and state‐of‐the‐art machine learning algorithms, like the wavelet scattering transform (WST). As a cell‐electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug‐induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine‐like oxidized low‐density lipoprotein receptor‐1 (LOX‐1) transcript levels in human colorectal adenocarcinoma (DLD‐1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP‐based assays addressing cancer heterogeneity for precision medicine and pharmacological research.

show abstract

Integrating Machine Learning and Sensors for the Development of Organ-on-Chip Platforms for Medical Diagnosis

Mencattini

Giuseppe²,

Casti³

et al. 2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Learning Cancer-Related Drug Efficacy Exploiting Consensus in Coordinated Motility Within Cell Clusters

Cited by 24 publications

References 28 publications

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

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

Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In‐Flow Identification of Single Cells

Integrating Machine Learning and Sensors for the Development of Organ-on-Chip Platforms for Medical Diagnosis

Contact Info

Product

Resources

About