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.

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.

Portable low-cost and highly accurate programmable triggering controller for confocal spinning disk image scanning microscope.

This work presents a very low-cost and highly accurate alternative way of implementing triggering control for a confocal spinning-disk image scanning microscope (CSD-ISM). Instead of using the previously reported hig-cost field programmable gate array (FPGA), that connects to a computer by an internal PCIe bus, we implemented the controller using a simple, low-cost real-time digital signal controller (DSC) development kit that connects to a computer via an external USB channel. The overall time resolution of the controller is better than 10 nanoseconds. The DSC programming is implemented using the free software included in the development kit and no additional commercial software is required. The operation of the CSD-ISM is performed by a 32-bit 150 MHz processor using the software environment Micro-Manager, a popular open-source platform for microscopy. Since software is used to calculate all the necessary times, the output signal is almost limitless.

Diffusion with partial resetting.

Inspired by many examples in nature, stochastic resetting of random processes has been studied extensively in the past decade. In particular, various models of stochastic particle motion were considered where, upon resetting, the particle is returned to its initial position. Here we generalize the model of diffusion with resetting to account for situations where a particle is returned only a fraction of its distance to the origin, e.g., half way. We show that this model always attains a steady-state distribution which can be written as an infinite sum of independent, but not identical, Laplace random variables. As a result, we find that the steady-state transitions from the known Laplace form which is obtained in the limit of full resetting to a Gaussian form, which is obtained close to the limit of no resetting. A similar transition is shown to be displayed by drift diffusion whose steady state can also be expressed as an infinite sum of independent random variables. Finally, we extend our analysis to capture the temporal evolution of drift diffusion with partial resetting, providing a bottom-up probabilistic construction that yields a closed-form solution for the time-dependent distribution of this process in Fourier-Laplace space. Possible extensions and applications of diffusion with partial resetting are discussed.

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.

Correction: Live cell single molecule tracking and localization microscopy of bioorthogonally labeled plasma membrane proteins.

Correction for 'Live cell single molecule tracking and localization microscopy of bioorthogonally labeled plasma membrane proteins' by Andres I. König et al., Nanoscale, 2020, 12, 3236-3248, DOI: 10.1039/C9NR08594G.

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.

Experimental Realization of Diffusion with Stochastic Resetting.

Stochastic resetting is prevalent in natural and man-made systems, giving rise to a long series of nonequilibrium phenomena. Diffusion with stochastic resetting serves as a paradigmatic model to study these phenomena, but the lack of a well-controlled platform by which this process can be studied experimentally has been a major impediment to research in the field. Here, we report the experimental realization of colloidal particle diffusion and resetting via holographic optical tweezers. We provide the first experimental corroboration of central theoretical results and go on to measure the energetic cost of resetting in steady-state and first-passage scenarios. In both cases, we show that this cost cannot be made arbitrarily small because of fundamental constraints on realistic resetting protocols. The methods developed herein open the door to future experimental study of resetting phenomena beyond diffusion.

Live cell single molecule tracking and localization microscopy of bioorthogonally labeled plasma membrane proteins.

Tracking the localization and mobility of individual proteins in live cells is key for understanding how they mediate their function. Such information can be obtained from single molecule imaging techniques including as Single Particle Tracking (SPT) and Single Molecule Localization Microscopy (SMLM). Genetic code expansion (GCE) combined with bioorthogonal chemistry offers an elegant approach for direct labeling of proteins with fluorescent dyes, holding great potential for improving protein labeling in single molecule applications. Here we calibrated conditions for performing SPT and live-SMLM of bioorthogonally labeled plasma membrane proteins in live mammalian cells. Using SPT, the diffusion of bioorthogonally labeled EGF receptor and the prototypical Shaker voltage-activated potassium channel (Kv) was measured and characterized. Applying live-SMLM to bioorthogonally labeled Shaker Kv channels enabled visualizing the plasma membrane distribution of the channel over time with ∼30 nm accuracy. Finally, by competitive labeling with two Fl-dyes, SPT and live-SMLM were performed in a single cell and both the density and dynamics of the EGF receptor were measured at single molecule resolution in subregions of the cell. We conclude that GCE and bioorthogonal chemistry is a highly suitable, flexible approach for protein labeling in quantitative single molecule applications that outperforms current protein live-cell labeling approaches.

Auxiliary Optomechanical Tools for 3D Cell Manipulation.

Advances in laser and optoelectronic technologies have brought the general concept of optomechanical manipulation to the level of standard biophysical tools, paving the way towards controlled experiments and measurements of tiny mechanical forces. Recent developments in direct laser writing (DLW) have enabled the realization of new types of micron-scale optomechanical tools, capable of performing designated functions. Here we further develop the concept of DLW-fabricated optomechanically-driven tools and demonstrate full-3D manipulation capabilities over biological objects. In particular, we resolved the long-standing problem of out-of-plane rotation in a pure liquid, which was demonstrated on a living cell, clamped between a pair of forks, designed for efficient manipulation with holographic optical tweezers. The demonstrated concept paves the way for the realization of flexible tools for performing on-demand functions over biological objects, such as cell tomography and surgery to name just few.

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.

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.

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.

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.

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.

Fluorescent Tracking of the Endoplasmic Reticulum in Live Pathogenic Fungal Cells.

In fungal cells, the endoplasmic reticulum (ER) harbors several of the enzymes involved in the biosynthesis of ergosterol, an essential membrane component, making this organelle the site of action of antifungal azole drugs, used as a first-line treatment for fungal infections. This highlights the need for specific fluorescent labeling of this organelle in cells of pathogenic fungi. Here we report on the development and evaluation of a collection of fluorescent ER trackers in a panel of Candida, considered the most frequently encountered pathogen in fungal infections. These trackers enabled imaging of the ER in live fungal cells. Organelle specificity was associated with the expression of the target enzyme of antifungal azoles that resides in the ER; specific ER labeling was not observed in mutant cells lacking this enzyme. Labeling of live Candida cells with a combination of a mitotracker and one of the novel fungal ER trackers revealed sites of contact between the ER and mitochondria. These fungal ER trackers therefore offer unique molecular tools for the study of the ER and its interactions with other organelles in live cells of pathogenic fungi.

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.