Abstract
Cell motility varies according to intrinsic features and microenvironmental stimuli, being a signature of underlying biological phenomena. The heterogeneity in cell response, due to multilevel cell diversity especially relevant in cancer, poses a challenge in identifying the biological scenario from cell trajectories. We propose here a novel peer prediction strategy among cell trajectories, deciphering cell state (tumor vs. nontumor), tumor stage, and response to the anticancer drug etoposide, based on morphology and motility features, solving the strong heterogeneity of individual cell properties. The proposed approach first barcodes cell trajectories, then automatically selects the good ones for optimal model construction (good teacher and test sample selection), and finally extracts a collective response from the heterogeneous populations via cooperative learning approaches, discriminating with high accuracy prostate noncancer vs. cancer cells of high vs. low malignancy. Comparison with standard classification methods validates our approach, which therefore represents a promising tool for addressing clinically relevant issues in cancer diagnosis and therapy, e.g., detection of potentially metastatic cells and anticancer drug screening.
Highlights
The ability of cells to coordinately move is indispensable in many biological processes, such as tissue morphogenesis and repair, cancer progression, and invasion [1]
We present here a novel methodology to conduct massive analysis of cell motility in different in vitro–controlled conditions that combines time-lapse microscopy (TLM) and label-free imaging, with cell tracking, quantitative representation of trajectories, and novel machine learning (ML) strategies within peer prediction framework
The proposed approach can be divided into eight key steps: [1] cell localization and tracking, [2] automatic cell clustering identification, [3] cell morphology and motility feature extraction, [4] good teacher selection, [5] test samples selection, [6] dynamic feature selection (DFS), [7] classification model, and [8] cooperative learning
Summary
The ability of cells to coordinately move is indispensable in many biological processes, such as tissue morphogenesis and repair, cancer progression, and invasion (i.e., metastasis spreading) [1]. Heterogeneity in cell response represents a big limitation to identify the underlying biological scenario from cell motility; such heterogeneity allows extracting behavioral rules to finalize the automatic understanding, for example, of cell state (e.g., tumor vs nontumor), tumor stage (e.g., metastatic vs nonmetastatic), response to anticancer drugs, etc. To this purpose, label-free [3] fluorescence time-lapse microscopy (TLM) and special purpose video data analysis tools [4,5,6,7] are providing promising novel, nonmolecular, dynamic approaches
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