Assessing the distribution of cancer stem cells in tumorspheres.

Cancer Stem Cells presumably drive tumor growth and resistance to conventional cancer treatments. From a previous computational model, we inferred that these cells are not uniformly distributed in the bulk of a tumorsphere. To confirm this result, we cultivated tumorspheres enriched in stem cells, and performed immunofluorescent detection of the stemness marker SOX2 using confocal microscopy. In this article, we present an image processing method that reconstructs the amount and location of the Cancer Stem Cells in the spheroids. Its advantage is the use of a statistical criterion to classify the cells in Stem and Differentiated, instead of setting an arbitrary threshold. Moreover, the analysis of the experimental images presented in this work agrees with the results from our computational models, thus enforcing the notion that the distribution of Cancer Stem Cells in a tumorsphere is non-homogeneous. Additionally, the method presented here provides a useful tool for analyzing any image in which different kinds of cells are stained with different markers.

High-resolution ethograms, accelerometer recordings, and behavioral time series of Japanese quail.

Although many small vertebrates are capable of performing high-speed behaviors, most studies continue to focus on low-resolution temporal scales (>>1 s). Herein, we present video-recordings, behavior time series, and the computer software for video-analysis of Japanese quail within social groups. Home-boxes were monitored using both top and side video-cameras. High-resolution ethograms were developed for analyses. Pairs of females were assigned as either controls or using one of two methods for attachment of an accelerometer (patch or backpack). Behavior was recorded during 1 h on the first 2-days, sampled at 1 s intervals (days 1 and 2). On day 8, an unfamiliar male was placed in the home-box and its behavior was recorded during the first 10 min, sampled every 1/15 s. Male accelerometer recordings were also obtained. Video-recordings and resulting detailed high-resolution behavioral time series are valuable for reuse in comparative studies regarding the temporal dynamics of behavior within social environments. In addition, they are necessary for the assessment of novel machine learning algorithms that could be used for deciphering the output of accelerometer recordings.

Understanding the influence of substrate when growing tumorspheres.

BACKGROUND: Cancer stem cells are important for the development of many solid tumors. These cells receive promoting and inhibitory signals that depend on the nature of their environment (their niche) and determine cell dynamics. Mechanical stresses are crucial to the initiation and interpretation of these signals. METHODS: A two-population mathematical model of tumorsphere growth is used to interpret the results of a series of experiments recently carried out in Tianjin, China, and extract information about the intraspecific and interspecific interactions between cancer stem cell and differentiated cancer cell populations. RESULTS: The model allows us to reconstruct the time evolution of the cancer stem cell fraction, which was not directly measured. We find that, in the presence of stem cell growth factors, the interspecific cooperation between cancer stem cells and differentiated cancer cells induces a positive feedback loop that determines growth, independently of substrate hardness. In a frustrated attempt to reconstitute the stem cell niche, the number of cancer stem cells increases continuously with a reproduction rate that is enhanced by a hard substrate. For growth on soft agar, intraspecific interactions are always inhibitory, but on hard agar the interactions between stem cells are collaborative while those between differentiated cells are strongly inhibitory. Evidence also suggests that a hard substrate brings about a large fraction of asymmetric stem cell divisions. In the absence of stem cell growth factors, the barrier to differentiation is broken and overall growth is faster, even if the stem cell number is conserved. CONCLUSIONS: Our interpretation of the experimental results validates the centrality of the concept of stem cell niche when tumor growth is fueled by cancer stem cells. Niche memory is found to be responsible for the characteristic population dynamics observed in tumorspheres. The model also shows why substratum stiffness has a deep influence on the behavior of cancer stem cells, stiffer substrates leading to a larger proportion of asymmetric doublings. A specific condition for the growth of the cancer stem cell number is also obtained.

Phase separation and emergence of collective motion in a one-dimensional system of active particles.

We study numerically a one-dimensional system of self-propelled particles, where the state of the particles is given by their moving direction (left or right), which is encoded by a spin-like variable, and their position. Particles interact by short-ranged, spring-like attractive forces and do not possess spin-spin interactions (i.e., velocity alignment). Newton's third law is broken in this model by assuming an asymmetric interaction range that is larger in the direction of the moving direction of the particle. We show that in this nonequilibrium system, due to the absence of the action-reaction symmetry, there exists an intimate link between phase separation and the formation of highly coherent, spatially localized, moving flocks (i.e., collective motion). More specifically, we prove the existence of two fundamentally different types of active phase separation, which we refer to as neutral phase separation (NPS) and polar phase separation. Furthermore, we indicate that NPS is subdivided in two classes with distinct critical exponents. These results are of key importance to understand that in active matter, there exist several phase-separation classes and that the emergence of polar, self-organized patterns (i.e., flocks) does not require the presence of a velocity alignment.

Large-Scale Patterns in a Minimal Cognitive Flocking Model: Incidental Leaders, Nematic Patterns, and Aggregates.

We study a minimal cognitive flocking model, which assumes that the moving entities navigate using the available instantaneous visual information exclusively. The model consists of active particles, with no memory, that interact by a short-ranged, position-based, attractive force, which acts inside a vision cone (VC), and lack velocity-velocity alignment. We show that this active system can exhibit-due to the VC that breaks Newton's third law-various complex, large-scale, self-organized patterns. Depending on parameter values, we observe the emergence of aggregates or millinglike patterns, the formation of moving-locally polar-files with particles at the front of these structures acting as effective leaders, and the self-organization of particles into macroscopic nematic structures leading to long-ranged nematic order. Combining simulations and nonlinear field equations, we show that position-based active models, as the one analyzed here, represent a new class of active systems fundamentally different from other active systems, including velocity-alignment-based flocking systems. The reported results are of prime importance in the study, interpretation, and modeling of collective motion patterns in living and nonliving active systems.

Evidence of a robust universality class in the critical behavior of self-propelled agents: metric versus topological interactions.

The nature of the interactions among self-propelled agents (SPA), i.e., topological versus metric or a combination of both types, is a relevant open question in the field of self-organization phenomena. We studied the critical behavior of a Vicsek-like system of SPA given by a group of agents moving at constant speed and interacting among themselves under the action of a topological rule: each agent aligns itself with the average direction of its seven nearest neighbors, independent of the distance, under the influence of some noise. Based on both stationary and dynamic measurements, we provide strong evidence that both types of interactions are manifestations of the same phenomenon, which defines a robust universality class. Also, the cluster size distribution evaluated at the critical point shows a power-law behavior, and the exponent corresponding to the topological model is in excellent agreement with that of the metric one, further reinforcing our claim. Furthermore, we found that with topological interactions the average distance of influence between agents undergoes large fluctuations that diverge at the critical noise, thus providing clues about a mechanism that could be implemented by the agents to change their moving strategy.