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.

Tissue fusion over nonadhering surfaces.

Tissue fusion eliminates physical voids in a tissue to form a continuous structure and is central to many processes in development and repair. Fusion events in vivo, particularly in embryonic development, often involve the purse-string contraction of a pluricellular actomyosin cable at the free edge. However, in vitro, adhesion of the cells to their substrate favors a closure mechanism mediated by lamellipodial protrusions, which has prevented a systematic study of the purse-string mechanism. Here, we show that monolayers can cover well-controlled mesoscopic nonadherent areas much larger than a cell size by purse-string closure and that active epithelial fluctuations are required for this process. We have formulated a simple stochastic model that includes purse-string contractility, tissue fluctuations, and effective friction to qualitatively and quantitatively account for the dynamics of closure. Our data suggest that, in vivo, tissue fusion adapts to the local environment by coordinating lamellipodial protrusions and purse-string contractions.

How cells flow in the spreading of cellular aggregates.

Like liquid droplets, cellular aggregates, also called "living droplets," spread onto adhesive surfaces. When deposited onto fibronectin-coated glass or polyacrylamide gels, they adhere and spread by protruding a cellular monolayer (precursor film) that expands around the droplet. The dynamics of spreading results from a balance between the pulling forces exerted by the highly motile cells at the periphery of the film, and friction forces associated with two types of cellular flows: (i) permeation, corresponding to the entry of the cells from the aggregates into the film; and (ii) slippage as the film expands. We characterize these flow fields within a spreading aggregate by using fluorescent tracking of individual cells and particle imaging velocimetry of cell populations. We find that permeation is limited to a narrow ring of width ξ (approximately a few cells) at the edge of the aggregate and regulates the dynamics of spreading. Furthermore, we find that the subsequent spreading of the monolayer depends heavily on the substrate rigidity. On rigid substrates, the migration of the cells in the monolayer is similar to the flow of a viscous liquid. By contrast, as the substrate gets softer, the film under tension becomes unstable with nucleation and growth of holes, flows are irregular, and cohesion decreases. Our results demonstrate that the mechanical properties of the environment influence the balance of forces that modulate collective cell migration, and therefore have important implications for the spreading behavior of tissues in both early development and cancer.

Border forces and friction control epithelial closure dynamics.

We study the closure dynamics of a large number of well-controlled circular apertures within an epithelial monolayer, where the collective cell migration responsible for epithelization is triggered by the removal of a spatial constraint rather than by scratching. Based on experimental observations, we propose a physical model that takes into account border forces, friction with the substrate, and tissue rheology. Border protrusive activity drives epithelization despite the presence of a contractile actomyosin cable at the periphery of the wound. The closure dynamics is quantified by an epithelization coefficient, defined as the ratio of protrusive stress to tissue-substrate friction, that allows classification of different phenotypes. The same analysis demonstrates a distinct signature for human cells bearing the oncogenic RasV12 mutation, demonstrating the potential of the approach to quantitatively characterize metastatic transformations.