Quantitative assessment of the spatial crowding heterogeneity in cellular fluids

Controlling the Morphology of Poly(ethylene glycol)-b-poly(lactide) Self-Assemblies in Solution: Interplay of Homopolymer Additives and Kinetic Traps

This study investigates the effects of homopolymer additives and kinetic traps on the self-assembly of poly(ethylene glycol)-b-poly(lactide) (PEG-PLA) block copolymer (BCP) nanostructures in aqueous environments. By using non-adsorbing PEG homopolymers to kinetically trap PEG-PLA nanostructures, we demonstrate that varying the concentration and molecular weight of the added PEG induces a reversible micelle-to-vesicle transition. This transition is primarily driven by changes in the molecular geometry of the PEG-PLA BCPs due to excluded volume screening effects. Additionally, the reversible vesicle-to-micelle transition upon PEG's removal shows time and temperature dependency, highlighting the influence of the system's kinetic nature. Intermediate structures observed during the transition support a mechanism based on shifts in the molecular geometry of PEG-PLA. As a proof of concept, we show that PEG-PLA vesicles can act as thermoresponsive delivery systems, retaining dye at low temperatures (4 °C) and releasing it upon heating (37 °C). Overall, this work presents a novel approach to controlling BCP nanostructures' morphology, with implications for drug delivery and material science applications.

Exploring generic principles of compartmentalization in a developmental in vitro model

Self-organization of cells into higher-order structures is key for multicellular organisms, for example via repetitive replication of template-like founder cells or syncytial energids. Yet, very similar spatial arrangements of cell-like compartments ('protocells') are also seen in a minimal model system of Xenopus egg extracts in the absence of template structures and chromatin, with dynamic microtubule assemblies driving the self-organization process. Quantifying geometrical features over time, we show here that protocell patterns are highly organized with a spatial arrangement and coarsening dynamics similar to that of two-dimensional foams but without the long-range ordering expected for hexagonal patterns. These features remain invariant when enforcing smaller protocells by adding taxol, i.e. patterns are dominated by a single, microtubule-derived length scale. Comparing our data to generic models, we conclude that protocell patterns emerge by simultaneous formation of randomly assembling protocells that grow at a uniform rate towards a frustrated arrangement before fusion of adjacent protocells eventually drives coarsening. The similarity of protocell patterns to arrays of energids and cells in developing organisms, but also to epithelial monolayers, suggests generic mechanical cues to drive self-organized space compartmentalization.

Anomalous diffusion and nonergodicity for heterogeneous diffusion processes with fractional Gaussian noise

Heterogeneous diffusion processes (HDPs) feature a space-dependent diffusivity of the form D(x)=D_{0}|x|^{α}. Such processes yield anomalous diffusion and weak ergodicity breaking, the asymptotic disparity between ensemble and time averaged observables, such as the mean-squared displacement. Fractional Brownian motion (FBM) with its long-range correlated yet Gaussian increments gives rise to anomalous and ergodic diffusion. Here, we study a combined model of HDPs and FBM to describe the particle dynamics in complex systems with position-dependent diffusivity driven by fractional Gaussian noise. This type of motion is, inter alia, relevant for tracer-particle diffusion in biological cells or heterogeneous complex fluids. We show that the long-time scaling behavior predicted theoretically and by simulations for the ensemble- and time-averaged mean-squared displacements couple the scaling exponents α of HDPs and the Hurst exponent H of FBM in a characteristic way. Our analysis of the simulated data in terms of the rescaled variable y∼|x|^{1/(2/(2-α))}/t^{H} coupling particle position x and time t yields a simple, Gaussian probability density function (PDF), P_{HDP-FBM}(y)=e^{-y^{2}}/sqrt[π]. Its universal shape agrees well with theoretical predictions for both uni- and bimodal PDF distributions.