Automated interpretation of time-lapse quantitative phase image by machine learning to study cellular dynamics during epithelial-mesenchymal transition.

Autor:	Strbkova L; Brno Univ. of Technology, Czech Republic.; CEITEC- Central European Institute of Technology, Czech Republic., Carson BB; Uppsala Univ., Sweden., Vincent T; Uppsala Univ., Sweden.; NYU School of Medicine, United States., Vesely P; Brno Univ. of Technology, Czech Republic., Chmelik R; Brno Univ. of Technology, Czech Republic.
Jazyk:	angličtina
Zdroj:	Journal of biomedical optics [J Biomed Opt] 2020 Aug; Vol. 25 (8).
DOI:	10.1117/1.JBO.25.8.086502
Abstrakt:	Significance: Machine learning is increasingly being applied to the classification of microscopic data. In order to detect some complex and dynamic cellular processes, time-resolved live-cell imaging might be necessary. Incorporating the temporal information into the classification process may allow for a better and more specific classification. Aim: We propose a methodology for cell classification based on the time-lapse quantitative phase images (QPIs) gained by digital holographic microscopy (DHM) with the goal of increasing performance of classification of dynamic cellular processes. Approach: The methodology was demonstrated by studying epithelial-mesenchymal transition (EMT) which entails major and distinct time-dependent morphological changes. The time-lapse QPIs of EMT were obtained over a 48-h period and specific novel features representing the dynamic cell behavior were extracted. The two distinct end-state phenotypes were classified by several supervised machine learning algorithms and the results were compared with the classification performed on single-time-point images. Results: In comparison to the single-time-point approach, our data suggest the incorporation of temporal information into the classification of cell phenotypes during EMT improves performance by nearly 9% in terms of accuracy, and further indicate the potential of DHM to monitor cellular morphological changes. Conclusions: Proposed approach based on the time-lapse images gained by DHM could improve the monitoring of live cell behavior in an automated fashion and could be further developed into a tool for high-throughput automated analysis of unique cell behavior.
Databáze:	MEDLINE
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