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

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.

Post-transcriptional regulations of cancer stem cell homeostasis.

PURPOSE OF REVIEW: Although extensively studied for over a decade, gene expression programs established at the epigenetic and/or transcriptional levels do not fully characterize cancer stem cells (CSC). This review will highlight the latest advances regarding the functional relevance of different key post-transcriptional regulations and how they are coordinated to control CSC homeostasis. RECENT FINDINGS: In the past 2 years, several groups have identified master post-transcriptional regulators of CSC genetic programs, including RNA modifications, RNA-binding proteins, microRNAs and long noncoding RNAs. Of particular interest, these studies reveal that different post-transcriptional mechanisms are coordinated to control key signalling pathways and transcription factors to either support or suppress CSC homeostasis. SUMMARY: Deciphering molecular mechanisms coordinating plasticity, survival and tumourigenic capacities of CSCs in adult and paediatric cancers is essential to design new antitumour therapies. An entire field of research focusing on post-transcriptional gene expression regulation is currently emerging and will significantly improve our understanding of the complexity of the molecular circuitries driving CSC behaviours and of druggable CSC weaknesses.