Probing Local Force Propagation in Tensed Fibrous Gels.

Fibrous hydrogels are a key component of soft animal tissues. They support cellular functions and facilitate efficient mechanical communication between cells. Due to their nonlinear mechanical properties, fibrous materials display non-trivial force propagation at the microscale, that is enhanced compared to that of linear-elastic materials. In the body, tissues are constantly subjected to external loads that tense or compress them, modifying their micro-mechanical properties into an anisotropic state. However, it is unknown how force propagation is modified by this isotropic-to-anisotropic transition. Here, force propagation in tensed fibrin hydrogels is directly measured. Local perturbations are induced by oscillating microspheres using optical tweezers. 1-point and 2-point microrheology are combined to simultaneously measure the shear modulus and force propagation. A mathematical framework to quantify anisotropic force propagation trends is suggested. Results show that force propagation becomes anisotropic in tensed gels, with, surprisingly, stronger response to perturbations perpendicular to the axis of tension. Importantly, external tension can also increase the range of force transmission. Possible implications and future directions for research are discussed. These results suggest a mechanism for favored directions of mechanical communication between cells in a tissue under external loads.

Measurements and characterization of the dynamics of tracer particles in an actin network.

The underlying physics governing the diffusion of a tracer particle in a viscoelastic material is a topic of some dispute. The long-term memory in the mechanical response of such materials should induce diffusive motion with a memory kernel, such as fractional Brownian motion (fBM). This is the reason that microrheology is able to provide the shear modulus of polymer networks. Surprisingly, the diffusion of a tracer particle in a network of a purified protein, actin, was found to conform to the continuous time random walk type (CTRW). We set out to resolve this discrepancy by studying the tracer particle diffusion using two different tracer particle sizes, in actin networks of different mesh sizes. We find that the ratio of tracer particle size to the characteristic length scale of a bio-polymer network plays a crucial role in determining the type of diffusion it performs. We find that the diffusion of the tracer particles has features of fBm when the particle is large compared to the mesh size, of normal diffusion when the particle is much smaller than the mesh size, and of the CTRW in between these two limits. Based on our findings, we propose and verify numerically a new model for the motion of the tracer in all regimes. Our model suggests that diffusion in actin networks consists of fBm of the tracer particle coupled with caging events with power-law distributed escape times.

Kinetics of actin networks formation measured by time resolved particle-tracking microrheology.

Actin is one of the most studied cytoskeleton proteins showing a very rich span of structures and functions. For example, adenosine triphosphate (ATP)-assisted polymerization of actin is used to push protrusions forward in a mechanism that enables cells to crawl on a substrate. In this process, the chemical energy released from the hydrolysis of ATP is what enables force generation. We study a minimal model system comprised of actin monomers in an excess of ATP concentration. In such a system polymerization proceeds in three stages: nucleation of actin filaments, elongation, and network formation. While the kinetics of filament growth was characterized previously, not much is known about the kinetics of network formation and the evolution of networks towards a steady-state structure. In particular, it is not clear how the non-equilibrium nature of this ATP-assisted polymerization manifests itself in the kinetics of self-assembly. Here, we use time-resolved microrheology to follow the kinetics of the three stages of self-assembly as a function of initial actin monomer concentration. Surprisingly, we find that at high enough initial monomer concentrations the effective elastic modulus of the forming actin networks overshoots and then relaxes with a -2/5 power law. We attribute the overshoot to the non-equilibrium nature of the polymerization and the relaxation to rearrangements of the network into a steady-state structure.

Real-Time In-Situ Monitoring of a Tunable Pentapeptide Gel-Crystal Transition.

Supramolecular gels often become destabilized by the transition of the gelator into a more stable crystalline phase, but often the long timescale and sporadic localization of the crystalline phase preclude a persistent observation of this process. We present a pentapeptide gel-crystal phase transition amenable for continuous visualization and quantification by common microscopic methods, allowing the extraction of kinetics and visualization of the dynamics of the transition. Using optical microscopy and microrheology, we show that the transition is a sporadic event in which gel dissolution is associated with microcrystalline growth that follows a sigmoidal rate profile. The two phases are based on ß-sheets of similar yet distinct configuration. We also demonstrate that the transition kinetics and crystal morphology can be modulated by extrinsic factors, including temperature, solvent composition, and mechanical perturbation. This work introduces an accessible model system and methodology for studying phase transitions in supramolecular gels.

Single-Particle Diffusion Characterization by Deep Learning.

Diffusion plays a crucial role in many biological processes including signaling, cellular organization, transport mechanisms, and more. Direct observation of molecular movement by single-particle-tracking experiments has contributed to a growing body of evidence that many cellular systems do not exhibit classical Brownian motion but rather anomalous diffusion. Despite this evidence, characterization of the physical process underlying anomalous diffusion remains a challenging problem for several reasons. First, different physical processes can exist simultaneously in a system. Second, commonly used tools for distinguishing between these processes are based on asymptotic behavior, which is experimentally inaccessible in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables because different transport modes can result in the same diffusion power-law α, which is typically obtained from the mean-square displacements (MSDs). The outstanding challenge in the field is to develop a method to extract an accurate assessment of the diffusion process using many short trajectories with a simple scheme that is applicable at the nonexpert level. Here, we use deep learning to infer the underlying process resulting in anomalous diffusion. We implement a neural network to classify single-particle trajectories by diffusion type: Brownian motion, fractional Brownian motion and continuous time random walk. Further, we demonstrate the applicability of our network architecture for estimating the Hurst exponent for fractional Brownian motion and the diffusion coefficient for Brownian motion on both simulated and experimental data. These networks achieve greater accuracy than time-averaged MSD analysis on simulated trajectories while only requiring as few as 25 steps. When tested on experimental data, both net and ensemble MSD analysis converge to similar values; however, the net needs only half the number of trajectories required for ensemble MSD to achieve the same confidence interval. Finally, we extract diffusion parameters from multiple extremely short trajectories (10 steps) using our approach.

Structural changes in nanoparticle-hydrogel composites at very low filler concentrations.

We study the enhancement of the stiffness of two families of hydrogels (polyacrylamide, PAAm, and polydimethylacrylamide, PDMA) due to the additions of very small amounts of silica nanofillers. It is well established that high concentrations of silica nanoparticles enhance the toughness of both hydrogel types, but significantly more for the PDMA based gels that adsorb readily to silica surfaces. In order to decouple the structural changes in the gels that stem either from polymerization kinetics or from the interactions between nanofillers and polymers, we use a photoinitiator for the polymerization of the composite gels that promotes the structural homogeneity of the hydrogels. We characterize both the mechanical and structural properties of the composite hydrogels as a function of nanofiller concentration, by calculating the single particle diffusion of inert polystyrene tracer particles of three different sizes. In agreement with previous experiments, we find that silica nanoparticles increase the stiffness of PAAm gels more than expected for passive fillers. Surprisingly, we find that a small addition of silica nanoparticles during gel polymerization to PDMA based hydrogels softens them. We attribute this effect to an increase of the average mesh size of the gel, allowing particles of 0.49 µm in diameter to diffuse normally through the gel, but restricting the motion of larger particles. A further increase in silica nanoparticle concentration results in the expected stiffening of the gel. PDMA based composites with a large mean pore size, as reported here, may find applications in particle separation and gentle fixation of microorganisms and cells.

Real-Time Imaging of the Azole Class of Antifungal Drugs in Live Candida Cells.

Azoles are the most commonly used class of antifungal drugs, yet where they localize within fungal cells and how they are imported remain poorly understood. Azole antifungals target lanosterol 14α-demethylase, a cytochrome P450, encoded by ERG11 in Candida albicans, the most prevalent fungal pathogen. We report the synthesis of fluorescent probes that permit visualization of antifungal azoles within live cells. Probe 1 is a dansyl dye-conjugated azole, and probe 2 is a Cy5-conjugated azole. Docking computations indicated that each of the probes can occupy the active site of the target cytochrome P450. Like the azole drug fluconazole, probe 1 is not effective against a mutant that lacks the target cytochrome P450. In contrast, the azole drug ketoconazole and probe 2 retained some antifungal activity against mutants lacking the target cytochrome P450, implying that both act against more than one target. Both fluorescent azole probes colocalized with the mitochondria, as determined by fluorescence microscopy with MitoTracker dye. Thus, these fluorescent probes are useful molecular tools that can lead to detailed information about the activity and localization of the important azole class of antifungal drugs.