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Journal Articles Scientific Reports Year : 2022

Machine learning-based detection of label-free cancer stem-like cell fate

Abstract

The detection of cancer stem-like cells (CSCs) is mainly based on molecular markers or functional tests giving a posteriori results. Therefore label-free and real-time detection of single CSCs remains a difficult challenge. The recent development of microfluidics has made it possible to perform high-throughput single cell imaging under controlled conditions and geometries. Such a throughput requires adapted image analysis pipelines while providing the necessary amount of data for the development of machine-learning algorithms. In this paper, we provide a data-driven study to assess the complexity of brightfield time-lapses to monitor the fate of isolated cancer stem-like cells in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. We show that with our experimental system and on two different primary cell lines our optimized deep learning based algorithm outperforms classical computer vision and shallow learning-based algorithms in terms of accuracy while being faster than cutting-edge convolutional neural network (CNNs). With this study, we show that tailoring our deep learning-based algorithm to the image analysis problem yields better results than pre-trained models. As a result, such a rapid and accurate CNN is compatible with the rise of high-throughput data generation and opens the door to on-the-fly CSC fate analysis.
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Dates and versions

hal-03880836 , version 1 (15-02-2023)

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Alexis Chambost, Nabila Berabez, Olivier Cochet-Escartin, François Ducray, Mathieu Gabut, et al.. Machine learning-based detection of label-free cancer stem-like cell fate. Scientific Reports, 2022, 12 (1), pp.19066. ⟨10.1038/s41598-022-21822-z⟩. ⟨hal-03880836⟩
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