Spatial Crossover Between Far-From-Equilibrium and Near-Equilibrium Dynamics in Locally Driven Suspensions.

We examine the response of a quasi-two-dimensional colloidal suspension to a localized circular driving induced by optical tweezers. This approach allows us to resolve over 3 orders of magnitude in the Péclet number (Pe) and provide a direct observation of a sharp spatial crossover from far- to near-thermal-equilibrium regions of the suspension. In particular, particles migrate from high to low Pe regions and form strongly inhomogeneous steady-state density profiles with an emerging length scale that does not depend on the particle density and is set by Pe≈1. We show that the phenomenological two phase fluid constitutive model is in line with our results.

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.

Nanoparticle Mobility over a Surface as a Probe for Weak Transient Disordered Peptide-Peptide Interactions.

Weak interactions form the core basis of a vast number of biological processes, in particular, those involving intrinsically disordered proteins. Here, we establish a new technique capable of probing these weak interactions between synthetic unfolded polypeptides using a convenient yet efficient, quantitative method based on single particle tracking of peptide-coated gold nanoparticles over peptide-coated surfaces. We demonstrate that our technique is sensitive enough to observe the influence of a single amino acid mutation on the transient peptide-peptide interactions. Furthermore, the effects of buffer salinity, which are expected to alter weak electrostatic interactions, are also readily detected and examined in detail. The method presented here has the potential to evaluate, in a high-throughput manner, weak interactions for a wide range of disordered proteins, polypeptides, and other biomolecules.

Flow Arrest in the Plasma Membrane.

The arrangement of receptors in the plasma membrane strongly affects the ability of a cell to sense its environment both in terms of sensitivity and in terms of spatial resolution. The spatial and temporal arrangement of the receptors is affected in turn by the mechanical properties and the structure of the cell membrane. Here, we focus on characterizing the flow of the membrane in response to the motion of a protein embedded in it. We do so by measuring the correlated diffusion of extracellularly tagged transmembrane neurotrophin receptors TrkB and p75 on transfected neuronal cells. In accord with previous reports, we find that the motion of single receptors exhibits transient confinement to submicron domains. We confirm predictions based on hydrodynamics of fluid membranes, finding long-range correlations in the motion of the receptors in the plasma membrane. However, we discover that these correlations do not persist for long ranges, as predicted, but decay exponentially, with a typical decay length on the scale of the average confining domain size.

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.

Deep-penetration fluorescence imaging through dense yeast cells suspensions using Airy beams.

We propose, to the best of our knowledge, a new method to image fluorescent objects through turbid media based on Airy beam scanning. This is achieved by using the nondiffractive nature of Airy beams, namely their ability to maintain their shape while penetrating through a highly scattering medium. We show that our technique can image fluorescent objects immersed in turbid media with higher resolution and signal to noise than confocal imaging. As proof of principle, we demonstrate imaging of 1 µm sized fluorescent beads through a dense suspension of yeast cells with an attenuation coefficient of 51 cm-1 at a depth of 90 µm. Finally, we demonstrate that our technique can also provide the depth of the imaged object without any additional sectioning.

Optical trapping below the diffraction limit with a tunable beam waist using super-oscillating beams.

Super-oscillating beams can be used to create light spots whose size is below the diffraction limit with a side ring of high intensity adjacent to them. Optical traps made of the super-oscillating part of such beams exhibit superior localization of submicron beads compared to regular optical traps. Here we focus on the effect of the ratio of particle size to beam size on the localization and stiffness of optical traps made of super-oscillating beams. We find a nonmonotonic dependence of trapping stiffness on the ratio of particle size to beam size. Optimal trapping is achieved when the particle is larger than the beam waist of the super-oscillating feature but small enough not to overlap with the side ring.

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 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.

Non-diffracting beams for label-free imaging through turbid media.

We propose a new method to image through dynamically changing turbid media based on the scanning of non-diffractive laser beams. We use computer-generated holograms to create Airy beams and compare quantitatively the characteristics of their propagation in clear and turbid media. Imaging contrast is achieved by relative reflection of the scanned beams from the imaged surface. We implement our method to demonstrate experimentally our ability to image a chromium surface on a glass slide through 270 µm of highly scattering milk/water mixtures with a resolution of several microns.

Experimental Realization of an Information Machine with Tunable Temporal Correlations.

We experimentally realize a Maxwell's demon that converts information gained by measurements to work. Our setup is composed of a colloidal particle in a channel filled with a flowing fluid. A barrier made by light prevents the particle from being carried away by the flow. The colloidal particle then performs biased Brownian motion in the vicinity of the barrier. The particle's position is measured periodically. When the particle is found to be far enough from the barrier, feedback is applied by moving the barrier upstream while maintaining a given minimal distance from the particle. At steady state, the net effect of this measurement and feedback loop is to steer the particle upstream while applying very little direct work on it. This clean example of a Maxwell's demon is also naturally operated in a parameter regime where correlations between outcomes of consecutive measurements are important. Interestingly, we find a tradeoff between output power and efficiency. The efficiency is maximal at quasistatic operating conditions, whereas both the power output and rate of information gain are maximal for very frequent measurements.

Angular phase unwrapping of optically thick objects with a thin dimension.

We present a new phase unwrapping approach, which allows reconstruction of optically thick objects that are optically thin from at least one viewing angle, by considering the information stored in the object phase maps captured from consecutive angles. Our algorithm combines 1-D phase unwrapping in the angular dimension with conventional 2-D phase unwrapping, to achieve unwrapping of the object from the optically thick perspective. We thus obtain quantitative phase imaging of objects that were previously impossible to image in certain viewing angles. To demonstrate our approach, we present both numerical simulation and experimental results for quantitative phase imaging of biological cells.

Scale dependence of the mechanics of active gels with increasing motor concentration.

Actin is a protein that plays an essential role in maintaining the mechanical integrity of cells. In response to strong external stresses, it can assemble into large bundles, but it grows into a fine branched network to induce cell motion. In some cases, the self-organization of actin fibers and networks involves the action of bipolar filaments of the molecular motor myosin. Such self-organization processes mediated by large myosin bipolar filaments have been studied extensively in vitro. Here we create active gels, composed of single actin filaments and small myosin bipolar filaments. The active steady state in these gels persists long enough to enable the characterization of their mechanical properties using one and two point microrheology. We study the effect of myosin concentration on the mechanical properties of this model system for active matter, for two different motor assembly sizes. In contrast to previous studies of networks with large motor assemblies, we find that the fluctuations of tracer particles embedded in the network decrease in amplitude as motor concentration increases. Nonetheless, we show that myosin motors stiffen the actin networks, in accordance with bulk rheology measurements of networks containing larger motor assemblies. This implies that such stiffening is of universal nature and may be relevant to a wider range of cytoskeleton-based structures.

Tomographic phase microscopy with 180° rotation of live cells in suspension by holographic optical tweezers.

We present a new tomographic phase microscopy (TPM) approach that allows capturing the three-dimensional refractive index structure of single cells in suspension without labeling, using 180° rotation of the cells. This is obtained by integrating an external off-axis interferometer for wide-field wave front acquisition with holographic optical tweezers (HOTs) for trapping and micro-rotation of the suspended cells. In contrast to existing TPM approaches for cell imaging, our approach does not require anchoring the sample to a rotating stage, nor is it limited in angular range as is the illumination rotation approach. Thus, it allows noninvasive TPM of suspended live cells in a wide angular range. The proposed technique is experimentally demonstrated by capturing the three-dimensional refractive index map of yeast cells, while collecting interferometric projections at an angular range of 180° with 5° steps. The interferometric projections are processed by both the filtered back-projection method and the diffraction theory method. The experimental system is integrated with a spinning disk confocal fluorescent microscope for validation of the label-free TPM results.

Structure and dynamics of a layer of sedimented particles.

We investigate experimentally and theoretically thin layers of colloid particles held adjacent to a solid substrate by gravity. Epifluorescence, confocal, and holographic microscopy, combined with Monte Carlo and hydrodynamic simulations, are applied to infer the height distribution function of particles above the surface, and their diffusion coefficient parallel to it. As the particle area fraction is increased, the height distribution becomes bimodal, indicating the formation of a distinct second layer. In our theory, we treat the suspension as a series of weakly coupled quasi-two-dimensional layers in equilibrium with respect to particle exchange. We experimentally, numerically, and theoretically study the changing occupancies of the layers as the area fraction is increased. The decrease of the particle diffusion coefficient with concentration is found to be weakened by the layering. We demonstrate that particle polydispersity strongly affects the properties of the sedimented layer, because of particle size segregation due to gravity.

Extracting the dynamic correlation length of actin networks from microrheology experiments.

The mechanical properties of polymer gels based on cytoskeleton proteins (e.g. actin) have been studied extensively due to their significant role in biological cell motility and in maintaining the cell's structural integrity. Microrheology is the natural method of choice for such studies due to its economy in sample volume, its wide frequency range, and its spatial sensitivity. In microrheology, the thermal motion of tracer particles embedded in a complex fluid is used to extract the fluid's viscoelastic properties. Comparing the motion of a single particle to the correlated motion of particle pairs, it is possible to extract viscoelastic properties at different length scales. In a recent study, a crossover between intermediate and bulk response of complex fluids was discovered in microrheology measurements of reconstituted actin networks. This crossover length was related to structural and mechanical properties of the networks, such as their mesh size and dynamic correlation length. Here we capitalize on this result giving a detailed description of our analysis scheme, and demonstrating how this relation can be used to extract the dynamic correlation length of a polymer network. We further study the relation between the dynamic correlation length and the structure of the network, by introducing a new length scale, the average filament length, without altering the network's mesh size. Contrary to the prevailing assumption, that the dynamic correlation length is equivalent to the mesh size of the network, we find that the dynamic correlation length increases once the filament length is reduced below the crossover distance.

Programmable comprehensive controller for multi-color 3D confocal spinning-disk image scanning microscope.

This paper introduces a compact, portable, and highly accurate triggering control system for a 3D confocal spinning-disk image scanning microscope (CSD-ISM). Building upon on our previously published research, we expanded the hardware of the controller and synchronized it with a sub-micron translator which scans the object in the z-direction. As well as expanding the hardware, the software also was extended from previously published work similarly as it is stated for hardware while allowing full control over the 3D movement. We showed a clear and smooth 3D image made up of a collection of 2D images at different heights.

Ebola Virus Glycoprotein Strongly Binds to Membranes in the Absence of Receptor Engagement.

Ebola virus (EBOV) is an enveloped virus that must fuse with the host cell membrane in order to release its genome and initiate infection. This process requires the action of the EBOV envelope glycoprotein (GP), encoded by the virus, which resides in the viral envelope and consists of a receptor binding subunit, GP1, and a membrane fusion subunit, GP2. Despite extensive research, a mechanistic understanding of the viral fusion process is incomplete. To investigate GP-membrane association, a key step in the fusion process, we used two approaches: high-throughput measurements of single-particle diffusion and single-molecule measurements with optical tweezers. Using these methods, we show that the presence of the endosomal Niemann-Pick C1 (NPC1) receptor is not required for primed GP-membrane binding. In addition, we demonstrate this binding is very strong, likely attributed to the interaction between the GP fusion loop and the membrane's hydrophobic core. Our results also align with previously reported findings, emphasizing the significance of acidic pH in the protein-membrane interaction. Beyond Ebola virus research, our approach provides a powerful toolkit for studying other protein-membrane interactions, opening new avenues for a better understanding of protein-mediated membrane fusion events.

Accurate holographic imaging of colloidal particle pairs by Rayleigh-Sommerfeld reconstruction.

In-line holographic optical imaging has the unique capability of high speed imaging in three dimensions at rates limited only by the imaging rate of the camera used. In this technique the 3D data is recorded on the detector in a form of a hologram generated by diffraction between the scattered and unscattered light passing through the sample. For dilute samples of single particles or a small cluster of particles, this technique was shown to result in particle tracking with spatial positioning accuracy of a few nanometers. For dense suspension only approximate reconstruction were achieved with systematic axial positioning errors. We propose a scheme to extend accurate holographic microscopy to dense suspensions, by calibrating the Rayleigh-Sommerfeld reconstruction algorithm against Lorentz-Mie scattering theory. We perform this calibration both numerically and experimentally and define the parameter space in which accurate imaging is achieved, and in which numerical calibration holds. We demonstrate the validity of our approach by imaging two attached particles and measuring the distance between their centers with 36 nm accuracy. A difference of 50 nm in particle diameter is easily measured.