Anomalous diffusion of single particles in cytoplasm

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

Dissipative systems fractionally coupled to a bath

Quantum diffusion is a major topic in condensed-matter physics, and the Caldeira-Leggett model has been one of the most successful approaches to study this phenomenon. Here, we generalize this model by coupling the bath to the system through a Liouville fractional derivative. The Liouville fractional Langevin equation is then derived in the classical regime, without imposing a non-Ohmic macroscopic spectral function for the bath. By investigating the short- and long-time behavior of the mean squared displacement, we show that this model is able to describe a large variety of anomalous diffusion. Indeed, we find ballistic, sub-ballistic, and super-ballistic behavior for short times, whereas for long times, we find saturation and sub- and super-diffusion.

Peto's "Paradox" and Six Degrees of Cancer Prevalence

Peto's paradox and the epidemiologic observation of the average six degrees of tumor prevalence are studied and hypothetically solved. A simple consideration, Petho's paradox challenges our intuitive understanding of cancer risk and prevalence. Our simple consideration is that the more a cell divides, the higher the chance of acquiring cancerous mutations, and so the larger or longer-lived organisms have more cells and undergo more cell divisions over their lifetime, expecting to have a higher risk of developing cancer. Paradoxically, it is not supported by the observations. The allometric scaling of species could answer the Peto paradox. Another paradoxical human epidemiology observation in six average mutations is necessary for cancer prevalence, despite the random expectations of the tumor causes. To solve this challenge, game theory could be applied. The inherited and random DNA mutations in the replication process nonlinearly drive cancer development. The statistical variance concept does not reasonably describe tumor development. Instead, the Darwinian natural selection principle is applied. The mutations in the healthy organism's cellular population can serve the species' evolutionary adaptation by the selective pressure of the circumstances. Still, some cells collect multiple uncorrected mutations, adapt to the extreme stress in the stromal environment, and develop subclinical phases of cancer in the individual. This process needs extensive subsequent DNA replications to heritage and collect additional mutations, which are only marginal alone. Still, together, they are preparing for the first stage of the precancerous condition. In the second stage, when one of the caretaker genes is accidentally mutated, the caused genetic instability prepares the cell to fight for its survival and avoid apoptosis. This can be described as a competitive game. In the third stage, the precancerous cell develops uncontrolled proliferation with the damaged gatekeeper gene and forces the new game strategy with binary cooperation with stromal cells for alimentation. In the fourth stage, the starving conditions cause a game change again, starting a cooperative game, where the malignant cells cooperate and force the cooperation of the stromal host, too. In the fifth stage, the resetting of homeostasis finishes the subclinical stage, and in the fifth stage, the clinical phase starts. The prevention of the development of mutated cells is more complex than averting exposure to mutagens from the environment throughout the organism's lifetime. Mutagenic exposure can increase the otherwise random imperfect DNA reproduction, increasing the likelihood of cancer development, but mutations exist. Toxic exposure is more challenging; it may select the tolerant cells on this particular toxic stress, so these mutations have more facility to avoid apoptosis in otherwise collected random mutational states.

RAF-like protein kinases mediate a deeply conserved, rapid auxin response

The plant-signaling molecule auxin triggers fast and slow cellular responses across land plants and algae. The nuclear auxin pathway mediates gene expression and controls growth and development in land plants, but this pathway is absent from algal sister groups. Several components of rapid responses have been identified in Arabidopsis, but it is unknown if these are part of a conserved mechanism. We recently identified a fast, proteome-wide phosphorylation response to auxin. Here, we show that this response occurs across 5 land plant and algal species and converges on a core group of shared targets. We found conserved rapid physiological responses to auxin in the same species and identified rapidly accelerated fibrosarcoma (RAF)-like protein kinases as central mediators of auxin-triggered phosphorylation across species. Genetic analysis connects this kinase to both auxin-triggered protein phosphorylation and rapid cellular response, thus identifying an ancient mechanism for fast auxin responses in the green lineage.

Information Transmission via Molecular Communication in Astrobiological Environments

The ubiquity of information transmission via molecular communication between cells is comprehensively documented on Earth; this phenomenon might even have played a vital role in the origin(s) and early evolution of life. Motivated by these considerations, a simple model for molecular communication entailing the diffusion of signaling molecules from transmitter to receiver is elucidated. The channel capacity C (maximal rate of information transmission) and an optimistic heuristic estimate of the actual information transmission rate ℐ are derived for this communication system; the two quantities, especially the latter, are demonstrated to be broadly consistent with laboratory experiments and more sophisticated theoretical models. The channel capacity exhibits a potentially weak dependence on environmental parameters, whereas the actual information transmission rate may scale with the intercellular distance d as ℐ ∝ d-4 and could vary substantially across settings. These two variables are roughly calculated for diverse astrobiological environments, ranging from Earth's upper oceans (C ∼ 3.1 × 103 bits/s; ℐ ∼ 4.7 × 10-2 bits/s) and deep sea hydrothermal vents (C ∼ 4.2 × 103 bits/s; ℐ ∼ 1.2 × 10-1 bits/s) to the hydrocarbon lakes and seas of Titan (C ∼ 3.8 × 103 bits/s; ℐ ∼ 2.6 × 10-1 bits/s).

Exploring non-equilibrium processes and spatio-temporal scaling laws in heated egg yolk using coherent X-rays

The soft-grainy microstructure of cooked egg yolk is the result of a series of out-of-equilibrium processes of its protein-lipid contents; however, it is unclear how egg yolk constituents contribute to these processes to create the desired microstructure. By employing X-ray photon correlation spectroscopy, we investigate the functional contribution of egg yolk constituents: proteins, low-density lipoproteins (LDLs), and yolk-granules to the development of grainy-gel microstructure and microscopic dynamics during cooking. We find that the viscosity of the heated egg yolk is solely determined by the degree of protein gelation, whereas the grainy-gel microstructure is controlled by the extent of LDL aggregation. Overall, protein denaturation-aggregation-gelation and LDL-aggregation follows Arrhenius-type time-temperature superposition (TTS), indicating an identical mechanism with a temperature-dependent reaction rate. However, above 75 °C TTS breaks down and temperature-independent gelation dynamics is observed, demonstrating that the temperature can no longer accelerate certain non-equilibrium processes above a threshold value.

Anomalous Colloidal Motion under Strong Confinement

Diffusion of biological macromolecules in the cytoplasm is a paradigm of colloidal diffusion in an environment characterized by a strong restriction of the accessible volume. This makes of the understanding of the physical rules governing colloidal diffusion under conditions mimicking the reduction in accessible volume occurring in the cell cytoplasm, a problem of a paramount importance. This work aims to study how the thermal motion of spherical colloidal beads in the inner cavity of giant unilamellar vesicles (GUVs) is modified by strong confinement conditions, and the viscoelastic character of the medium. Using single particle tracking, it is found that both the confinement and the environmental viscoelasticity lead to the emergence of anomalous motion pathways for colloidal microbeads encapsulated in the aqueous inner cavity of GUVs. This anomalous diffusion is strongly dependent on the ratio between the volume of the colloidal particle and that of the GUV under consideration as well as on the viscosity of the particle's liquid environment. Therefore, the results evidence that the reduction of the free volume accessible to colloidal motion pushes the diffusion far from a standard Brownian pathway as a result of the change in the hydrodynamic boundary conditions driving the particle motion.

Gramian angular fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

Anomalous diffusion is present at all scales, from atomic to large ones. Some exemplary systems are ultracold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, arthropods' free movement, and birds' migration patterns. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge [Muñoz-Gil et al., Nat. Commun. 12, 6253 (2021)2041-172310.1038/s41467-021-26320-w]. Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian angular fields (GAF) to encode one-dimensional trajectories as images (Gramian matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pretrained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime and infer the anomalous diffusion exponent α. Short raw trajectories of lengths between 10 and 50 are commonly encountered in single-particle tracking experiments and are the most difficult ones to characterize. We show that GAF images can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.

Size- and position-dependent cytoplasm viscoelasticity through hydrodynamic interactions with the cell surface

Many studies of cytoplasm rheology have focused on small components in the submicrometer scale. However, the cytoplasm also baths large organelles like nuclei, microtubule asters, or spindles that often take significant portions of cells and move across the cytoplasm to regulate cell division or polarization. Here, we translated passive components of sizes ranging from few up to ~50 percents of the cell diameter, through the vast cytoplasm of live sea urchin eggs, with calibrated magnetic forces. Creep and relaxation responses indicate that for objects larger than the micron size, the cytoplasm behaves as a Jeffreys material, viscoelastic at short timescales, and fluidizing at longer times. However, as component size approached that of cells, cytoplasm viscoelastic resistance increased in a nonmonotonic manner. Flow analysis and simulations suggest that this size-dependent viscoelasticity emerges from hydrodynamic interactions between the moving object and the static cell surface. This effect also yields to position-dependent viscoelasticity with objects initially closer to the cell surface being harder to displace. These findings suggest that the cytoplasm hydrodynamically couples large organelles to the cell surface to restrain their motion, with important implications for cell shape sensing and cellular organization.

Effect of crowding, compartmentalization and nanodomains on protein modification and redox signaling - current state and future challenges

Biological milieus are highly crowded and heterogeneous systems where organization of macromolecules within nanodomains (e.g. membraneless compartments) is vital to the regulation of metabolic processes. There is an increasing interest in understanding the effects that such packed environments have on different biochemical and biological processes. In this context, the redox biochemistry and redox signaling fields are moving towards investigating oxidative processes under conditions that exhibit these key features of biological systems in order to solve existing paradigms including those related to the generation and transmission of specific redox signals within and between cells in both normal physiology and under conditions of oxidative stress. This review outlines the effects that crowding, nanodomain formation and altered local viscosities can have on biochemical processes involving proteins, and then discusses some of the reactions and pathways involving proteins and oxidants that may, or are known to, be modulated by these factors. We postulate that knowledge of protein modification processes (e.g. kinetics, pathways and product formation) under conditions that mimic biological milieus, will provide a better understanding of the response of cells to endogenous and exogenous stressors, and their role in ageing, signaling, health and disease.

The Fokker-Planck equation of the superstatistical fractional Brownian motion with application to passive tracers inside cytoplasm

By collecting from literature data experimental evidence of anomalous diffusion of passive tracers inside cytoplasm, and in particular of subdiffusion of mRNA molecules inside live Escherichia coli cells, we obtain the probability density function of molecules' displacement and we derive the corresponding Fokker-Planck equation. Molecules' distribution emerges to be related to the Krätzel function and its Fokker-Planck equation to be a fractional diffusion equation in the Erdélyi-Kober sense. The irreducibility of the derived Fokker-Planck equation to those of other literature models is also discussed.

The Role of Diffusivity in Oil and Gas Industries: Fundamentals, Measurement, and Correlative Techniques

The existence of various native or nonnative species/fluids, along with having more than one phase in the subsurface and within the integrated production and injection systems, generates unique challenges as the pressure, temperature, composition and time (P-T-z and t) domains exhibit multi-scale characteristics. In such systems, fluid/component mixing, whether for natural reasons or man-made reasons, is one of the most complex aspects of the behavior of the system, as inherent compositions are partially or all due to these phenomena. Any time a gradient is introduced, these systems try to converge thermodynamically to an equilibrium state while being in the disequilibrium state at scale during the transitional process. These disequilibrium states create diffusive gradients, which, in the absence of flow, control the mixing processes leading to equilibrium at a certain time scale, which could also be a function of various time and length scales associated with the system. Therefore, it is crucial to understand these aspects, especially when technologies that need or utilize these concepts are under development. For example, as the technology of gas-injection-based enhanced oil recovery, CO2 sequestration and flooding have been developed, deployed and applied to several reservoirs/aquifers worldwide, performing research on mass-transfer mechanisms between gas, oil and aqueous phases became more important, especially in terms of optimal design considerations. It is well-known that in absence of direct frontal contact and convective mixing, diffusive mixing is one of most dominant mass-transfer mechanisms, which has an impact on the effectiveness of the oil recovery and gas injection processes. Therefore, in this work, we review the fundamentals of diffusive mixing processes in general terms and summarize the theoretical, experimental and empirical studies to estimate the diffusion coefficients at high pressure—temperature conditions at various time and length scales relevant to reservoir-fluid systems.

The sodium proton exchanger NHE9 regulates phagosome maturation and bactericidal activity in macrophages

Acidification of phagosomes is essential for the bactericidal activity of macrophages. Targeting machinery that regulates pH within the phagosomes is a prominent strategy employed by various pathogens that have emerged as major threats to public health. Nascent phagosomes acquire the machinery for pH regulation through a graded maturation process involving fusion with endolysosomes. In addition, meticulous coordination between proton pumping and leakage mechanisms is crucial for maintaining optimal pH within the phagosome. However, relative to mechanisms involved in acidifying the phagosome lumen, little is known about proton leakage pathways in this organelle. Sodium proton transporter NHE9 is a known proton leakage pathway located on the endosomes. As phagosomes acquire proteins through fusions with endosomes during maturation, NHE9 seemed a promising candidate for regulating proton fluxes on the phagosome. Here, using genetic and biophysical approaches, we show NHE9 is an important proton leakage pathway associated with the maturing phagosome. NHE9 is highly expressed in immune cells, specifically macrophages; however, NHE9 expression is strongly downregulated upon bacterial infection. We show that compensatory ectopic NHE9 expression hinders the directed motion of phagosomes along microtubules and promotes early detachment from the microtubule tracks. As a result, these phagosomes have shorter run lengths and are not successful in reaching the lysosome. In accordance with this observation, we demonstrate that NHE9 expression levels negatively correlate with bacterial survival. Together, our findings show that NHE9 regulates lumenal pH to affect phagosome maturation, and consequently, microbicidal activity in macrophages.

Effect of biphosphate salt on dipalmitoylphosphatidylcholine bilayer deformation by Tat polypeptide

Translocation of positively charged cell penetrating peptides (CPP) through cell membrane is important in drug delivery. Here we report all-atom molecular dynamics simulations to investigate how a biphosphate salt in a solvent affects the interaction of a CPP, HIV-1 Tat peptide with model dipalmitoylphosphatidylcholine (DPPC) lipid bilayer. Tat peptide has a large number of basic arginines and a couple of polar glutamines. We observe that in absence of salt, the basic residues of the polypeptide get localized in the vicinity of the membrane without altering the bilayer properties much; polypeptide induce local thinning of the bilayer membrane at the area of localization. In presence of biphosphate salt, the basic residues, dressed by the biphosphate ions, are repelled by the phosphate head groups of the lipid molecules. However, polar glutamine prefers to stay in the vicinity of the bilayer. This leads to larger local bilayer thickness at the contact point by the polar residue and non-uniform bilayer thickness profile. The thickness deformation of bilayer structure disappears upon mutating the polar residue, suggesting importance of the polar residue in bilayer deformation. Our studies point to control bilayer deformation by appropriate peptide sequence and solvent conditions.

Fractional Integrable Nonlinear Soliton Equations

Nonlinear integrable equations serve as a foundation for nonlinear dynamics, and fractional equations are well known in anomalous diffusion. We connect these two fields by presenting the discovery of a new class of integrable fractional nonlinear evolution equations describing dispersive transport in fractional media. These equations can be constructed from nonlinear integrable equations using a widely generalizable mathematical process utilizing completeness relations, dispersion relations, and inverse scattering transform techniques. As examples, this general method is used to characterize fractional extensions to two physically relevant, pervasive integrable nonlinear equations: the Korteweg-deVries and nonlinear Schrödinger equations. These equations are shown to predict superdispersive transport of nondissipative solitons in fractional media.

Plasmid DNA Undergoes Two Compaction Regimes under Macromolecular Crowding

The laser light scattering experiments were performed to explore the role of dextran (size (d): 2.6, 6.9, and 17.0 nm) in compacting the plasmids (pBS: 2.9 kbps; pCMV-Tag2B: 4.3 kbps; and pET28a: 5.3 kbps) in vitro in the volume fraction (ϕ) range 0.01 to 0.15 of the macromolecular crowder. Two compaction regimes were observed in terms of the radius of gyration (R_g) for plasmid-dextran combinations, wherein the plasmid diffusivity is governed by normal diffusion and subdiffusion, respectively. Generalized scaling, R_g ∼ ϕ^-1/(1+x), where x represents the conformational geometry of plasmids, is reported. The plasmid conformation depends on the crowder's size, with larger conformational changes observed in the presence of smaller crowders. The second virial coefficient (A₂) and translational diffusion coefficient (D_t) indicate that entropically driven depletion of crowders, excluded volume, and interplasmid repulsive interactions govern plasmids' conformational changes, validated herein from the scaling of D_t with molecular weight.

Mechanics of 3D Cell-Hydrogel Interactions: Experiments, Models, and Mechanisms

Hydrogels are highly water-swollen molecular networks that are ideal platforms to create tissue mimetics owing to their vast and tunable properties. As such, hydrogels are promising cell-delivery vehicles for applications in tissue engineering and have also emerged as an important base for ex vivo models to study healthy and pathophysiological events in a carefully controlled three-dimensional environment. Cells are readily encapsulated in hydrogels resulting in a plethora of biochemical and mechanical communication mechanisms, which recapitulates the natural cell and extracellular matrix interaction in tissues. These interactions are complex, with multiple events that are invariably coupled and spanning multiple length and time scales. To study and identify the underlying mechanisms involved, an integrated experimental and computational approach is ideally needed. This review discusses the state of our knowledge on cell-hydrogel interactions, with a focus on mechanics and transport, and in this context, highlights recent advancements in experiments, mathematical and computational modeling. The review begins with a background on the thermodynamics and physics fundamentals that govern hydrogel mechanics and transport. The review focuses on two main classes of hydrogels, described as semiflexible polymer networks that represent physically cross-linked fibrous hydrogels and flexible polymer networks representing the chemically cross-linked synthetic and natural hydrogels. In this review, we highlight five main cell-hydrogel interactions that involve key cellular functions related to communication, mechanosensing, migration, growth, and tissue deposition and elaboration. For each of these cellular functions, recent experiments and the most up to date modeling strategies are discussed and then followed by a summary of how to tune hydrogel properties to achieve a desired functional cellular outcome. We conclude with a summary linking these advancements and make the case for the need to integrate experiments and modeling to advance our fundamental understanding of cell-matrix interactions that will ultimately help identify new therapeutic approaches and enable successful tissue engineering.

Bounds for expected supremum of fractional Brownian motion with drift

We provide upper and lower bounds for the mean $\mathscr{M}(H)$ of $\sup_{t\geq 0} \{B_H(t) - t\}$ , with $B_H(\!\cdot\!)$ a zero-mean, variance-normalized version of fractional Brownian motion with Hurst parameter $H\in(0,1)$ . We find bounds in (semi-) closed form, distinguishing between $H\in(0,\frac{1}{2}]$ and $H\in[\frac{1}{2},1)$ , where in the former regime a numerical procedure is presented that drastically reduces the upper bound. For $H\in(0,\frac{1}{2}]$ , the ratio between the upper and lower bound is bounded, whereas for $H\in[\frac{1}{2},1)$ the derived upper and lower bound have a strongly similar shape. We also derive a new upper bound for the mean of $\sup_{t\in[0,1]} B_H(t)$ , $H\in(0,\frac{1}{2}]$ , which is tight around $H=\frac{1}{2}$ .

Subtle changes in crosslinking drive diverse anomalous transport characteristics in actin-microtubule networks

Anomalous diffusion in crowded and complex environments is widely studied due to its importance in intracellular transport, fluid rheology and materials engineering. Specifically, diffusion through the cytoskeleton, a network comprised of semiflexible actin filaments and rigid microtubules that interact both sterically and via crosslinking, plays a principal role in viral infection, vesicle transport and targeted drug delivery. Here, we elucidate the impact of crosslinking on particle diffusion in composites of actin and microtubules with actin-actin, microtubule-microtubule and actin-microtubule crosslinking. We analyze a suite of transport metrics by coupling single-particle tracking and differential dynamic microscopy. Using these complementary techniques, we find that particles display non-Gaussian and non-ergodic subdiffusion that is markedly enhanced by cytoskeletal crosslinking, which we attribute to suppressed microtubule mobility. However, the extent to which transport deviates from normal Brownian diffusion depends strongly on the crosslinking motif - with actin-microtubule crosslinking inducing the most pronounced anomalous characteristics. Our results reveal that subtle changes to actin-microtubule interactions can have complex impacts on particle diffusion in cytoskeleton composites, and suggest that a combination of reduced filament mobility and more variance in actin mobilities leads to more strongly anomalous particle transport.

A theoretical study on the effects of interdomain flexibility on drug encounter rate for coronavirus nucleocapsid-type proteins

To study the effects of the interdomain flexibility on the encounter rate of nucleocapsid-type protein with drug molecules, where two domains (NTD) are connected by a flexible linker and each NTD has a drug binding site, two-dimensional random walk simulation was carried out as a function of the interdomain flexibility and the drug concentration. NTDs represented as circles undergo random motions constrained by the interdomain flexibility while drug molecules are represented by lattice points. It was found that as the interdomain flexibility increases, the time interval between the drug bindings to the 1st and 2nd NTDs decreases, suggesting that the 2nd drug binding is accelerated. Furthermore, this effect was more significant at lower drug concentrations. These results suggest that the interdomain linker plays a key role in the drug binding process and thus emphasize the importance of characterization of their physicochemical properties to better evaluate the efficacy of potential drugs.

Computationally-efficient spatiotemporal correlation analysis super-resolves anomalous diffusion

Anomalous diffusion dynamics in confined nanoenvironments govern the macroscale properties and interactions of many biophysical and material systems. Currently, it is difficult to quantitatively link the nanoscale structure of porous media to anomalous diffusion within them. Fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) has been shown to extract nanoscale structure and Brownian diffusion dynamics within gels, liquid crystals, and polymers, but has limitations which hinder its wider application to more diverse, biophysically-relevant datasets. Here, we parallelize the least-squares curve fitting step on a GPU improving computation times by up to a factor of 40, implement anomalous diffusion and two-component Brownian diffusion models, and make fcsSOFI more accessible by packaging it in a user-friendly GUI. We apply fcsSOFI to simulations of the protein fibrinogen diffusing in polyacrylamide of varying matrix densities and super-resolve locations where slower, anomalous diffusion occurs within smaller, confined pores. The improvements to fcsSOFI in speed, scope, and usability will allow for the wider adoption of super-resolution correlation analysis to diverse research topics.

Enhanced Diffusion by Reversible Binding to Active Polymers

Cells are known to use reversible binding to active biopolymer networks to allow diffusive transport of particles in an otherwise impenetrable mesh. We here determine the motion of a particle that experiences random forces during binding and unbinding events while being constrained by attached polymers. Using Monte-Carlo simulations and a statistical mechanics model, we find that enhanced diffusion is possible with active polymers. However, this is possible only under optimum conditions that has to do with the relative length of the chains to that of the plate. For example, in systems where the plate is shorter than the chains, diffusion is maximum when many chains have the potential to bind but few remain bound at any one time. Interestingly, if the chains are shorter than the plate, we find that diffusion is maximized when more active chains remain transiently bound. The model provides insight into these findings by elucidating the mechanisms for binding-mediated diffusion in biology and design rules for macromolecular transport in transient synthetic polymers.

Electroviscous effect for a confined nanosphere in solution

A charged colloidal particle suspended in an electrolyte experiences electroviscous stresses arising from motion-driven electrohydrodynamic phenomena. Under certain conditions, the additional contribution from electroviscous drag forces to the total drag experienced by the moving particle can lead to measurable deviations of particle diffusion coefficients from values predicted by the well known Stokes-Einstein relation that describes diffusive behavior of small particles in an unbounded charge-free fluid. In this study, we investigate the role of electroviscous stresses on nanoparticle diffusion in confined geometries using both simulations and experiment. We compare our experimental measurements with the results of a numerically solved continuum model based on the Poisson-Nernst-Planck-Stokes system of equations and find good agreement between experiment and theory. Depending on the radius of the counterion species in solution and the degree of confinement, we find that the viscous drag on polystyrene nanoparticles can be augmented by approximately 10-25% compared to the values predicted by pure hydrodynamic models in the absence of free charge in the fluid. This enhancement corresponds approximately to a 5-10% increase compared to the electroviscous contribution for a charged particle in an unbounded fluid. Contrary to recent reports in the experimental literature, we find neither experimental nor theoretical evidence of an anomalously large enhancement of electroviscous forces on a confined charged nanoparticle in solution.

Power Law Behavior in Protein Desorption Kinetics Originating from Sequential Binding and Unbinding

The study of protein adsorption at the single molecule level has recently revealed that the adsorption is reversible, but with a long-tailed residence time distribution which can be approximated with a sum of exponential functions putatively related to distinct adsorption sites. Here it is proposed that the shape of the residence time distribution results from an adsorption process with sequential and reversible steps that contribute to overall binding strength resembling "zippering". In this model, the survival function of the residence time distribution of single proteins varies from an exponential distribution for a single adsorption step to a power law distribution with exponent -1/2 for a large number of adsorption steps. The adsorption of fluorescently labeled fibrinogen to glass surfaces is experimentally studied with single molecule imaging. The experimental residence time distribution can be readily fit by the proposed model. This demonstrates that the observed long residence times can arise from stepwise adsorption rather than rare but strong binding sites and provides guidance for the control of protein adsorption to biomaterials.

Simultaneous Single-Particle Tracking and Dynamic pH Sensing Reveal Lysosome-Targetable Mesoporous Silica Nanoparticle Pathways

Nanoparticle (NP)-based targeted drug delivery is intended to transport therapeutically active molecules to specific cells and particular intracellular compartments. However, there is limited knowledge regarding the complete route of NPs in this targeting scenario. In this study, simultaneously performing motion and dynamic pH sensing using single-particle tracking (SPT) leads to an alternative method of gaining insights into the mesoporous silica nanoparticle's (MSN) journey in targeting lysosome. Two different pH-sensitive dyes and a reference dye are incorporated into mesoporous silica nanoparticles (MSNs) via co-condensation to broaden the measurable pH range (pH 4-7.5) of the nanoprobe. The phosphonate, amine, and lysosomal sorting peptides (YQRLGC) are conjugated onto the MSN's surface to study intracellular nano-biointeractions of two oppositely charged and lysosome-targetable MSNs. The brightness and stability of these MSNs allow their movement and dynamic pH evolution during their journey to be simultaneously monitored in real time. Importantly, a multidimensional analysis of MSN's movement and local pH has revealed new model intracellular dynamic states and distributions of MSNs, previously inaccessible when using single parameters alone. A key result is that YQRLGC-conjugated MSNs took an alternative route to target lysosomes apart from the traditional one, which sped up to 4 h and enhanced their targeting efficiency (up to 32%). The findings enrich our understanding of the intracellular journey of MSNs. This study offers complementary information on correlating the surface design with the full pathway of nanoparticles to achieve targeted delivery of therapeutic payload.

Non-Gaussian subdiffusion of single-molecule tracers in a hydrated polymer network

Single molecule tracking experiments inside a hydrated polymer network have shown that the tracer motion is subdiffusive due to the viscoelastic environment inside the gel-like network. This property can be related to the negative autocorrelation of the instantaneous displacements at short times. Although the displacements of the individual tracers exhibit Gaussian statistics, the displacement distribution of all the trajectories combined from different spatial locations of the polymer network exhibits a non-Gaussian distribution. Here, we analyze many individual tracer trajectories to show that the central portion of the non-Gaussian distribution can be well approximated by an exponential distribution that spreads sublinearly with time. We explain all these features seen in the experiment by a generalized Langevin model for an overdamped particle with algebraically decaying correlations. We show that the degree of non-Gaussianity can change with the extent of heterogeneity, which is controlled in our model by the experimentally observed distributions of the motion parameters.

Anomalous intracellular transport phases depend on cytoskeletal network features

Intracellular transport in eukaryotic cells consists of phases of passive, diffusion-based transport and active, motor-driven transport along filaments that make up the cell's cytoskeleton. The interplay between superdiffusive transport along cytoskeletal filaments and the anomalous nature of subdiffusion in the bulk can lead to novel effects in transport behavior at the cellular scale. Here we develop a computational model of the process with cargo being ballistically transported along explicitly modeled cytoskeletal filament networks and passively transported in the cytoplasm by a subdiffusive continuous-time random walk (CTRW). We show that, over a physiologically relevant range of filament lengths and numbers, the network introduces a filament-length sensitive superdiffusive phase at early times which crosses over to a phase where the CTRW is dominant and produces subdiffusion at late times. We apply our approach to the problem of insulin secretion from cells and show that the superdiffusive phase introduced by the filament network manifests as a peak in the secretion at early times followed by an extended sustained release phase that is dominated by the CTRW process at late times. Our results are consistent with in vivo observations of insulin transport in healthy cells and shed light on the potential for the cell to tune functionally important transport phases by altering its cytoskeletal network.

Topology-dependent anomalous dynamics of ring and linear DNA are sensitive to cytoskeleton crosslinking

Cytoskeletal crowding plays a key role in the diffusion of DNA molecules through the cell, acting as a barrier to effective intracellular transport and conformational stability required for processes such as transfection, viral infection, and gene therapy. Here, we elucidate the transport properties and conformational dynamics of linear and ring DNA molecules diffusing through entangled and crosslinked composite networks of actin and microtubules. We couple single-molecule conformational tracking with differential dynamic microscopy to reveal that ring and linear DNA exhibit unexpectedly distinct transport properties that are influenced differently by cytoskeleton crosslinking. Ring DNA coils are swollen and undergo heterogeneous and biphasic subdiffusion that is hindered by crosslinking. Conversely, crosslinking actually facilitates the single-mode subdiffusion that compacted linear chains exhibit. Our collective results demonstrate that transient threading by cytoskeleton filaments plays a key role in the dynamics of ring DNA, whereas the mobility of the cytoskeleton dictates transport of linear DNA.

Filament Rigidity Vies with Mesh Size in Determining Anomalous Diffusion in Cytoskeleton

The diffusion of microscopic particles through the cell, important to processes such as viral infection, gene delivery, and vesicle transport, is largely controlled by the complex cytoskeletal network, comprised of semiflexible actin filaments and rigid microtubules, that pervades the cytoplasm. By varying the relative concentrations of actin and microtubules, the cytoskeleton can display a host of different structural and dynamic properties that, in turn, impact the diffusion of particles through the composite network. Here, we couple single-particle tracking with differential dynamic microscopy to characterize the transport of microsphere tracers diffusing through composite in vitro networks with varying ratios of actin and microtubules. We analyze multiple complementary metrics for anomalous transport to show that particles exhibit anomalous subdiffusion in all networks, which our data suggest arises from caging by networks. Further, subdiffusive characteristics are markedly more pronounced in actin-rich networks, which exhibit similarly more prominent viscoelastic properties compared to microtubule-rich composites. While the smaller mesh size of actin-rich composites compared to microtubule-rich composites plays an important role in these results, the rigidity of the filaments comprising the network also influences the anomalous characteristics that we observe. Our results suggest that as microtubules in our composites are replaced with actin filaments, the decreasing filament rigidity competes with increasing network connectivity to drive anomalous transport.

Damped White Noise Diffusion with Memory for Diffusing Microprobes in Ageing Fibrin Gels

From observations of colloidal tracer particles in fibrin undergoing gelation, we introduce an analytical framework that allows the determination of the probability density function for a stochastic process beyond fractional Brownian motion. Using passive microrheology via videomicroscopy, mean square displacements of tracer particles suspended in fibrin at different ageing times are obtained. The anomalous diffusion is then described by a damped white noise process with memory, with analytical results closely matching experimental plots of mean square displacements and probability density function. We further show that the white noise functional stochastic approach applied to passive microrheology reveals the existence of a gelation parameter μ which elucidates the dynamics of constrained tracer particles embedded in a time-dependent soft material. In addition, we found that microstructural heterogeneity of particle environments decreases as the ageing time increases. This study offers experimental insights on the ageing of fibrin gels while presenting a white noise functional stochastic approach that could be applied to other systems exhibiting non-Markovian diffusive behavior.

Approximations for reflected fractional Brownian motion

Fractional Brownian motion is a widely used stochastic process that is particularly suited to model anomalous diffusion. We focus on capturing the mean and variance of fractional Brownian motion reflected at level 0. As explicit expressions or numerical techniques are not available, we base our analysis on Monte Carlo simulation. Our main findings concern closed-form approximations of the mean and variance, with a near-perfect fit.

Bridging the spatiotemporal scales of macromolecular transport in crowded biomimetic systems

Crowding plays a key role in the transport and conformations of biological macromolecules. Gene therapy, viral infection, and transfection require DNA to traverse the crowded cytoplasm, including the cytoskeletal network of filamentous proteins. Given the complexity of cellular crowding, the dynamics of biological molecules can be highly dependent on the spatiotemporal scale probed. We present a powerful platform that spans molecular and cellular scales by coupling single-molecule conformational tracking (SMCT) and selective-plane illumination differential dynamic microscopy (SPIDDM). We elucidate the transport and conformational properties of large DNA, crowded by custom-designed networks of actin and microtubules, to link single-molecule conformations with ensemble DNA transport and cytoskeleton structure. We show that actin crowding leads to DNA compaction and suppression of fluctuations, combined with subdiffusion and heterogeneous transport, whereas microtubules have much more subdued impact across all scales. In composite networks of both filaments, scale-dependent effects emerge such that actin dictates ensemble DNA transport while microtubules influence single-molecule dynamics. We show that these intriguing results arise from a complex interplay between network rigidity, mesh size, filament concentration, and DNA size.

Controlling Anomalous Diffusion in Lipid Membranes

Diffusion in cell membranes is not just simple two-dimensional Brownian motion but typically depends on the timescale of the observation. The physical origins of this anomalous subdiffusion are unresolved, and model systems capable of quantitative and reproducible control of membrane diffusion have been recognized as a key experimental bottleneck. Here, we control anomalous diffusion using supported lipid bilayers containing lipids derivatized with polyethylene glycol (PEG) headgroups. Bilayers with specific excluded area fractions are formed by control of PEG lipid mole fraction. These bilayers exhibit a switch in diffusive behavior, becoming anomalous as bilayer continuity is disrupted. Using a combination of single-molecule fluorescence and interferometric imaging, we measure the anomalous behavior in this model over four orders of magnitude in time. Diffusion in these bilayers is well described by a power-law dependence of the mean-square displacement with observation time. Anomaleity in this system can be tailored by simply controlling the mole fraction of PEG lipid, producing bilayers with diffusion parameters similar to those observed for anomalous diffusion in biological membranes.

Urea-mediated anomalous diffusion in supported lipid bilayers

Diffusion in biological membranes is seldom simply Brownian motion; instead, the rate of diffusion is dependent on the time scale of observation and so is often described as anomalous. In order to help better understand this phenomenon, model systems are needed where the anomalous diffusion of the lipid bilayer can be tuned and quantified. We recently demonstrated one such model by controlling the excluded area fraction in supported lipid bilayers (SLBs) through the incorporation of lipids derivatized with polyethylene glycol. Here, we extend this work, using urea to induce anomalous diffusion in SLBs. By tuning incubation time and urea concentration, we produce bilayers that exhibit anomalous behaviour on the same scale as that observed in biological membranes.

Anomalous dynamics in tracer-particle motions in an electrohydrodynamically driven oil-in-oil system

We characterize the superdiffusive dynamics of tracer particles in an electrohydrodynamically driven emulsion of oil droplets in an immiscible oil medium, where the amplitude and frequency of an external electric field are the control parameters. In the weakly driven electrohydrodynamic regime, the droplets are trapped dielectrophoretically on a patterned electrode, and the driving is therefore spatially varying. We find excellent agreement with a 〈x^{2}〉∼t^{1.5} power law and find that this superdiffusive dynamics arises from an underlying displacement distribution that is distinctly non-Gaussian and exponential for small displacements and short times. While these results are comparable with a random-velocity field model, the tracer particle speeds are in fact spatially varying in two dimensions, arising from a spatially varying electrohydrodynamic driving force. This suggests that the important ingredient for the superdiffusive t^{1.5} behavior observed is a velocity field that is isotropic in the plane and spatially correlated. Finally, we can extract, from the superdiffusive dynamics, a experimental length scale that corresponds to the lateral range of the hydrodynamic flows. This experimental length scale is non zero only above a threshold ion mobility length.

Langevin equation in complex media and anomalous diffusion

The problem of biological motion is a very intriguing and topical issue. Many efforts are being focused on the development of novel modelling approaches for the description of anomalous diffusion in biological systems, such as the very complex and heterogeneous cell environment. Nevertheless, many questions are still open, such as the joint manifestation of statistical features in agreement with different models that can also be somewhat alternative to each other, e.g. continuous time random walk and fractional Brownian motion. To overcome these limitations, we propose a stochastic diffusion model with additive noise and linear friction force (linear Langevin equation), thus involving the explicit modelling of velocity dynamics. The complexity of the medium is parametrized via a population of intensity parameters (relaxation time and diffusivity of velocity), thus introducing an additional randomness, in addition to white noise, in the particle's dynamics. We prove that, for proper distributions of these parameters, we can get both Gaussian anomalous diffusion, fractional diffusion and its generalizations.

Non-specific interactions govern cytosolic diffusion of nanosized objects in mammalian cells

The diffusivity of macromolecules in the cytoplasm of eukaryotic cells varies over orders of magnitude and dictates the kinetics of cellular processes. However, a general description that associates the Brownian or anomalous nature of intracellular diffusion to the architectural and biochemical properties of the cytoplasm has not been achieved. Here we measure the mobility of individual fluorescent nanoparticles in living mammalian cells to obtain a comprehensive analysis of cytoplasmic diffusion. We identify a correlation between tracer size, its biochemical nature and its mobility. Inert particles with size equal or below 50 nm behave as Brownian particles diffusing in a medium of low viscosity with negligible effects of molecular crowding. Increasing the strength of non-specific interactions of the nanoparticles within the cytoplasm gradually reduces their mobility and leads to subdiffusive behaviour. These experimental observations and the transition from Brownian to subdiffusive motion can be captured in a minimal phenomenological model.

Crowding Induces Entropically-Driven Changes to DNA Dynamics That Depend on Crowder Structure and Ionic Conditions

Macromolecular crowding plays a principal role in a wide range of biological processes including gene expression, chromosomal compaction, and viral infection. However, the impact that crowding has on the dynamics of nucleic acids remains a topic of debate. To address this problem, we use single-molecule fluorescence microscopy and custom particle-tracking algorithms to investigate the impact of varying macromolecular crowding conditions on the transport and conformational dynamics of large DNA molecules. Specifically, we measure the mean-squared center-of-mass displacements, as well as the conformational size, shape, and fluctuations, of individual 115 kbp DNA molecules diffusing through various in vitro solutions of crowding polymers. We determine the role of crowder structure and concentration, as well as ionic conditions, on the diffusion and configurational dynamics of DNA. We find that branched, compact crowders (10 kDa PEG, 420 kDa Ficoll) drive DNA to compact, whereas linear, flexible crowders (10, 500 kDa dextran) cause DNA to elongate. Interestingly, the extent to which DNA mobility is reduced by increasing crowder concentrations appears largely insensitive to crowder structure (branched vs. linear), despite the highly different configurations DNA assumes in each case. We also characterize the role of ionic conditions on crowding-induced DNA dynamics. We show that both DNA diffusion and conformational size exhibit an emergent non-monotonic dependence on salt concentration that is not seen in the absence of crowders.

Do Skeletal Dynamics Mediate Sugar Uptake and Transport in Human Erythrocytes?

We explore, herein, the hypothesis that transport of molecules or ions into erythrocytes may be affected and directly stimulated by the dynamics of the spectrin/actin skeleton. Skeleton/actin motions are driven by thermal fluctuations that may be influenced by ATP hydrolysis as well as by structural alterations of the junctional complexes that connect the skeleton to the cell's lipid membrane. Specifically, we focus on the uptake of glucose into erythrocytes via glucose transporter 1 and on the kinetics of glucose disassociation at the endofacial side of glucose transporter 1. We argue that glucose disassociation is affected by both hydrodynamic forces induced by the actin/spectrin skeleton and by probable contact of the swinging 37-nm-long F-actin protofilament with glucose, an effect we dub the "stickball effect." Our hypothesis and results are interpreted within the framework of the kinetic measurements and compartmental kinetic models of Carruthers and co-workers; these experimental results and models describe glucose disassociation as the "slow step" (i.e., rate-limiting step) in the uptake process. Our hypothesis is further supported by direct simulations of skeleton-enhanced transport using our molecular-based models for the actin/spectrin skeleton as well as by experimental measurements of glucose uptake into cells subject to shear deformations, which demonstrate the hydrodynamic effects of advection. Our simulations have, in fact, previously demonstrated enhanced skeletal dynamics in cells in shear deformations, as they occur naturally within the skeleton, which is an effect also supported by experimental observations.

The nanofluidic confinement apparatus: studying confinement-dependent nanoparticle behavior and diffusion

The behavior of nanoparticles under nanofluidic confinement depends strongly on their distance to the confining walls; however, a measurement in which the gap distance is varied is challenging. Here, we present a versatile setup for investigating the behavior of nanoparticles as a function of the gap distance, which is controlled to the nanometer. The setup is designed as an open system that operates with a small amount of dispersion of ≈20 μL, permits the use of coated and patterned samples and allows high-numerical-aperture microscopy access. Using the tool, we measure the vertical position (termed height) and the lateral diffusion of 60 nm, charged, Au nanospheres as a function of confinement between a glass surface and a polymer surface. Interferometric scattering detection provides an effective particle illumination time of less than 30 μs, which results in lateral and vertical position detection accuracy ≈10 nm for diffusing particles. We found the height of the particles to be consistently above that of the gap center, corresponding to a higher charge on the polymer substrate. In terms of diffusion, we found a strong monotonic decay of the diffusion constant with decreasing gap distance. This result cannot be explained by hydrodynamic effects, including the asymmetric vertical position of the particles in the gap. Instead we attribute it to an electroviscous effect. For strong confinement of less than 120 nm gap distance, we detect the onset of subdiffusion, which can be correlated to the motion of the particles along high-gap-distance paths.

Viral highway to nucleus exposed by image correlation analyses

Parvoviral genome translocation from the plasma membrane into the nucleus is a coordinated multistep process mediated by capsid proteins. We used fast confocal microscopy line scan imaging combined with image correlation methods including auto-, pair- and cross-correlation, and number and brightness analysis, to study the parvovirus entry pathway at the single-particle level in living cells. Our results show that the endosome-associated movement of virus particles fluctuates from fast to slow. Fast transit of single cytoplasmic capsids to the nuclear envelope is followed by slow movement of capsids and fast diffusion of capsid fragments in the nucleoplasm. The unique combination of image analyses allowed us to follow the fate of intracellular single virus particles and their interactions with importin β revealing previously unknown dynamics of the entry pathway.

Subdiffusion via dynamical localization induced by thermal equilibrium fluctuations

We reveal the mechanism of subdiffusion which emerges in a straightforward, one dimensional classical nonequilibrium dynamics of a Brownian ratchet driven by both a time-periodic force and Gaussian white noise. In a tailored parameter set for which the deterministic counterpart is in a non-chaotic regime, subdiffusion is a long-living transient whose lifetime can be many, many orders of magnitude larger than characteristic time scales of the setup thus being amenable to experimental observations. As a reason for this subdiffusive behaviour in the coordinate space we identify thermal noise induced dynamical localization in the velocity (momentum) space. This novel idea is distinct from existing knowledge and has never been reported for any classical or quantum system. It suggests reconsideration of generally accepted opinion that subdiffusion is due to broad distributions or strong correlations which reflect disorder, trapping, viscoelasticity of the medium or geometrical constraints.

Membrane perturbation through novel cell-penetrating peptides influences intracellular accumulation of imatinib mesylate in CML cells

Chronic myeloid leukemia is a stem cell disease with the presence of Philadelphia chromosome generated through reciprocal translocation of chromosome 9 and 22. The use of first- and second-generation tyrosine kinase inhibitors has been successful to an extent. However, resistance against such drugs is an emerging problem. Apart from several drug-resistant mechanisms, drug influx/efflux ratio appears to be one of the key determinants of therapeutic outcomes. In addition, intracellular accumulation of drug critically depends on cell membrane fluidity and lipid raft dynamics. Previously, we reported two novel cell-penetrating peptides (CPPs), namely, cationic IR15 and anionic SR11 present in tryptic digest of Abrus agglutinin. Here, the potential of IR15 and SR11 to influence intracellular concentration of imatinib has been evaluated. Fluorescent correlation spectroscopy and lifetime imaging were employed to map membrane fluidity and lipid raft distribution following peptide-drug co-administration. Results show that IR15 and SR11 are the two CPPs which can modulate membrane fluidity and lipid raft distribution in K562 cells. Both IR15 and SR11 significantly reduce the viability of CML cells in the presence of imatinib by increasing the intracellular accumulation of the drug.

Self-averaging and weak ergodicity breaking of diffusion in heterogeneous media

Diffusion in natural and engineered media is quantified in terms of stochastic models for the heterogeneity-induced fluctuations of particle motion. However, fundamental properties such as ergodicity and self-averaging and their dependence on the disorder distribution are often not known. Here, we investigate these questions for diffusion in quenched disordered media characterized by spatially varying retardation properties, which account for particle retention due to physical or chemical interactions with the medium. We link self-averaging and ergodicity to the disorder sampling efficiency R_{n}, which quantifies the number of disorder realizations a noise ensemble may sample in a single disorder realization. Diffusion for disorder scenarios characterized by a finite mean transition time is ergodic and self-averaging for any dimension. The strength of the sample to sample fluctuations decreases with increasing spatial dimension. For an infinite mean transition time, particle motion is weakly ergodicity breaking in any dimension because single particles cannot sample the heterogeneity spectrum in finite time. However, even though the noise ensemble is not representative of the single-particle time statistics, subdiffusive motion in q≥2 dimensions is self-averaging, which means that the noise ensemble in a single realization samples a representative part of the heterogeneity spectrum.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.