Project description:Cell shape is fundamental in biology. The average cell shape can influence crucial biological functions, such as cell fate and division orientation. But cell-to-cell shape variability is often regarded as noise. In contrast, recent works reveal that shape variability in diverse epithelial monolayers follows a nearly universal distribution. However, the origin and implications of this universality remain unclear. Here, assuming contractility and adhesion are crucial for cell shape, characterized via aspect ratio (r), we develop a mean-field analytical theory for shape variability. We find that all the system-specific details combine into a single parameter α that governs the probability distribution function (PDF) of r; this leads to a universal relation between the standard deviation and the average of r. The PDF for the scaled r is not strictly but nearly universal. In addition, we obtain the scaled area distribution, described by the parameter μ. Information of α and μ together can distinguish the effects of changing physical conditions, such as maturation, on different system properties. We have verified the theory via simulations of two distinct models of epithelial monolayers and with existing experiments on diverse systems. We demonstrate that in a confluent monolayer, average shape determines both the shape variability and dynamics. Our results imply that cell shape distribution is inevitable, where a single parameter describes both statics and dynamics and provides a framework to analyze and compare diverse epithelial systems. In contrast to existing theories, our work shows that the universal properties are consequences of a mathematical property and should be valid in general, even in the fluid regime.
Project description:Epithelial cell monolayers show remarkable displacement and velocity correlations over distances of ten or more cell sizes that are reminiscent of supercooled liquids and active nematics. We show that many observed features can be described within the framework of dense active matter, and argue that persistent uncoordinated cell motility coupled to the collective elastic modes of the cell sheet is sufficient to produce swirl-like correlations. We obtain this result using both continuum active linear elasticity and a normal modes formalism, and validate analytical predictions with numerical simulations of two agent-based cell models, soft elastic particles and the self-propelled Voronoi model together with in-vitro experiments of confluent corneal epithelial cell sheets. Simulations and normal mode analysis perfectly match when tissue-level reorganisation occurs on times longer than the persistence time of cell motility. Our analytical model quantitatively matches measured velocity correlation functions over more than a decade with a single fitting parameter.
Project description:Collective cell migration is a highly regulated process involved in wound healing, cancer metastasis, and morphogenesis. Mechanical interactions among cells provide an important regulatory mechanism to coordinate such collective motion. Using a self-propelled Voronoi (SPV) model that links cell mechanics to cell shape and cell motility, we formulate a generalized mechanical inference method to obtain the spatiotemporal distribution of cellular stresses from measured traction forces in motile tissues and show that such traction-based stresses match those calculated from instantaneous cell shapes. We additionally use stress information to characterize the rheological properties of the tissue. We identify a motility-induced swim stress that adds to the interaction stress to determine the global contractility or extensibility of epithelia. We further show that the temporal correlation of the interaction shear stress determines an effective viscosity of the tissue that diverges at the liquid-solid transition, suggesting the possibility of extracting rheological information directly from traction data.
Project description:When mammalian cells form confluent monolayers completely filling a plane, these apparently random "tilings" show regularity in the statistics of cell areas for various types of epithelial and endothelial cells. The observed distributions are reproduced by a model which accounts for cell growth and division, with the latter treated stochastically both in terms of the sizes of the dividing cells as well as the sizes of the "newborn" ones--remarkably, the modeled and experimental distributions fit well when all free parameters are estimated directly from experiments.
Project description:Granular materials transition between unjammed (deformable) and jammed (rigid) states when adjusting their packing density. Here, we report on experiments demonstrating that the same kind of phase transition can be alternatively achieved through temperature-controlled particle shape change. Using a confined system of randomly-packed rod-like particles made of shape memory alloy (SMA), we exploit that shape recovery of these bent rods with rising temperature at a constant packing density leads to a jammed state. The responsible physical processes are elucidated with numerical simulations based on the Discrete Element Method. As an exemplary application of the uncovered mechanism, we engineer a smart clamp that can actively grip or release an object through the thermo-induced jamming or unjamming of the granular material, and robustly so under cyclic temperature changes. In the jammed state, its load-bearing capability surpasses the total SMA weight by a tunable margin, up to over 800-fold. The clamping design paves the way towards a new kind of functional devices based on the thermo-responsive jamming of shape memory granular materials.
Project description:We studied the influence of embedded dipole moments in self-assembled monolayers (SAMs) formed on template stripped Au surfaces with liquid eutectic Ga-In alloy as a top electrode. We designed three molecules based on a p-terphenyl structure in which the central aromatic ring is either phenyl or a dipole-inducing pyrimidyl in one of two different orientations. All three form well defined SAMs with similar thickness, packing density and tilt angle, with dipole moments embedded in the SAM, isolated from either interface. The magnitude of the current density is dominated by the tunneling distance and is not affected by the presence of dipole moments; however, transition voltages (VT) show a clear linear correlation with the shift in the work function of Au induced by the collective action of the embedded dipoles. This observation demonstrates that VT can be manipulated synthetically, without altering either the interfaces or electrodes and that trends in VT can be related to experimental observables on the SAMs before installing the top contact. Calculated projected density of states of the SAMs on Au surfaces that relate HOMO-derived states to VT further show that energy level alignment within an assembled junction can be predicted and adjusted by embedding dipoles in a SAM without altering any other properties of the junction. We therefore suggest that trends in VT can be used analogously to β in systems for which length-dependence is physically or experimentally inaccessible.
Project description:In natural settings, microbes tend to grow in dense populations [1-4] where they need to push against their surroundings to accommodate space for new cells. The associated contact forces play a critical role in a variety of population-level processes, including biofilm formation [5-7], the colonization of porous media [8, 9], and the invasion of biological tissues [10-12]. Although mechanical forces have been characterized at the single cell level [13-16], it remains elusive how collective pushing forces result from the combination of single cell forces. Here, we reveal a collective mechanism of confinement, which we call self-driven jamming, that promotes the build-up of large mechanical pressures in microbial populations. Microfluidic experiments on budding yeast populations in space-limited environments show that self-driven jamming arises from the gradual formation and sudden collapse of force chains driven by microbial proliferation, extending the framework of driven granular matter [17-20]. The resulting contact pressures can become large enough to slow down cell growth, to delay the cell cycle in the G1 phase, and to strain or even destroy the microenvironment through crack propagation. Our results suggest that self-driven jamming and build-up of large mechanical pressures is a natural tendency of microbes growing in confined spaces, contributing to microbial pathogenesis and biofouling [21-26].
Project description:Anisotropic tissue structures provide guidance for navigating neurons in vitro and in vivo. Here we optimized the generation of comparable anisotropic monolayers of astrocytes, endothelial cells, and Schwann cells as a first step toward determining which properties of anisotropic cells are sufficient for nerve guidance. The statistical experimental design method Design of Experiments and the experimental analysis method Response Surface Methodology were applied to improve efficiency and utility. Factors investigated included dimensions of microcontact printed protein patterns, cell density, and culture duration. Protein patterning spacing had the strongest influence. When cells initially aligned at borders and proliferated to fill in spaces, space between stripes was most effective when it was comparable to cell size. Maximizing the area of adhesive molecule coverage was also important for confluence of these types of cells. When cells adhered and aligned over the width of a stripe and broadened to fill spaces, space width about half the cell width was most effective. These findings suggest that if the mechanism of alignment, alignment at borders or over the width of the stripe, is predetermined and the cell size determined, the optimal size of the micropatterning for aligned monolayers of other cell types can be predicted. This study also demonstrates the effective use of DOE and RSM to probe cellular responses to various and multiple factors toward determination of optimal conditions for a desired cellular response.
Project description:The design and generation of molecules capable of mimicking the binding and/or functional sites of proteins represents a promising strategy for the exploration and modulation of protein function through controlled interference with the underlying molecular interactions. Synthetic peptides have proven an excellent type of molecule for the mimicry of protein sites because such peptides can be generated as exact copies of protein fragments, as well as in diverse chemical modifications, which includes the incorporation of a large range of non-proteinogenic amino acids as well as the modification of the peptide backbone. Apart from extending the chemical and structural diversity presented by peptides, such modifications also increase the proteolytic stability of the molecules, enhancing their utility for biological applications. This article reviews recent advances by this and other laboratories in the use of synthetic protein mimics to modulate protein function, as well as to provide building blocks for synthetic biology.
Project description:Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses requires experimental tools to measure these cues. In the context of large scale tissues, this can be achieved by segmenting individual cells to extract their shapes and deformations which in turn inform on their mechanical environment. Historically, this has been done by segmentation methods which are well known to be time consuming and error prone. In this context however, one doesn't necessarily require a cell-level description and a coarse-grained approach can be more efficient while using tools different from segmentation. The advent of machine learning and deep neural networks has revolutionized the field of image analysis in recent years, including in biomedical research. With the democratization of these techniques, more and more researchers are trying to apply them to their own biological systems. In this paper, we tackle a problem of cell shape measurement thanks to a large annotated dataset. We develop simple Convolutional Neural Networks (CNNs) which we thoroughly optimize in terms of architecture and complexity to question construction rules usually applied. We find that increasing the complexity of the networks rapidly no longer yields improvements in performance and that the number of kernels in each convolutional layer is the most important parameter to achieve good results. In addition, we compare our step-by-step approach with transfer learning and find that our simple, optimized CNNs give better predictions, are faster in training and analysis and don't require more technical knowledge to be implemented. Overall, we offer a roadmap to develop optimized models and argue that we should limit the complexity of such models. We conclude by illustrating this strategy on a similar problem and dataset.