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

• Published in: Scientific reports Vol. 12; no. 1; pp. 19066 - 11
• Main Authors: Chambost, Alexis J., Berabez, Nabila, Cochet-Escartin, Olivier, Ducray, François, Gabut, Mathieu, Isaac, Caroline, Martel, Sylvie, Idbaih, Ahmed, Rousseau, David, Meyronet, David, Monnier, Sylvain
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1564; 639/166/985; 639/705/1042; Algorithms; Cancer; Cellular Biology; Computer Science; Humanities and Social Sciences; Humans; Image Processing; Image Processing, Computer-Assisted - methods; Life Sciences; Machine Learning; multidisciplinary; Neoplasms; Neural Networks, Computer; Science; Science (multidisciplinary); Subcellular Processes
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-21822-z

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.