New developments in the microrheology of complex fluids are considered. Firstly the requirements for a simple modern particle tracking microrheology experiment are introduced, the error analysis methods associated with it and the mathematical techniques required to calculate the linear viscoelasticity. Progress in microrheology instrumentation is then described with respect to detectors, light sources, colloidal probes, magnetic tweezers, optical tweezers, diffusing wave spectroscopy, optical coherence tomography, fluorescence correlation spectroscopy, elastic- and quasi-elastic scattering techniques, 3D tracking, single molecule methods, modern microscopy methods and microfluidics. New theoretical techniques are also reviewed such as Bayesian analysis, oversampling, inversion techniques, alternative statistical tools for tracks (angular correlations, first passage probabilities, the kurtosis, motor protein step segmentation etc), issues in micro/macro rheological agreement and two particle methodologies. Applications where microrheology has begun to make some impact are also considered including semi-flexible polymers, gels, microorganism biofilms, intracellular methods, high frequency viscoelasticity, comb polymers, active motile fluids, blood clots, colloids, granular materials, polymers, liquid crystals and foods. Two large emergent areas of microrheology, non-linear microrheology and surface microrheology are also discussed.

The rheological characteristics of gastric and duodenal mucin solutions, the building blocks of the mucus layer that covers the epithelia of the two organs, were investigated using particle tracking microrheology. We used biochemically well characterized purified porcine mucins (MUC5AC and MUC2) as models for human mucins, to probe their viscoelasticity as a function of mucin concentration and pH. Furthermore, we used both reducing (dithiothreitol, DTT) and chaotropic agents (guanidinium chloride and urea) to probe the mesoscopic forces that mediate the integrity of the polymer network. At neutral pH both gastric and duodenal mucins formed self‐assembled semi‐dilute networks above a certain critical mucin concentration (c*) with the viscosity (η) scaling as for MUC5AC and for MUC2, where c is the mucin concentration. Above an even higher mucin concentration threshold (c_e, the entanglement concentration) reptation occurs and there is a dramatic increase in the viscosity scaling, for MUC5AC and for MUC2. The dynamics of the self‐assembled comb polymers is examined in terms of a scaling model for flexible polyelectrolyte combs. Both duodenum and gastric mucin are found to be pH switchable gels, gelation occurring at low pHs. There is a hundred‐fold increase in the elastic shear modulus once the pH is decreased. The addition of DTT, guanidinium chloride and urea disassembles both the semi‐dilute and gel structures causing a large increase in the compliance (decrease in their shear moduli). Addition of the polyphenol EGCG has a reverse effect on mucin viscoelasticity, that is, it triggers a sol–gel transition in semi‐dilute mucin solutions at neutral pH. © 2013 The Authors. Biopolymers published by Wiley Periodicals, Inc. Biopolymers 101: 366–377, 2014.

Intracellular transport is predominantly heterogeneous in both time and space, exhibiting varying non-Brownian behavior. Characterization of this movement through averaging methods over an ensemble of trajectories or over the course of a single trajectory often fails to capture this heterogeneity. Here, we developed a deep learning feedforward neural network trained on fractional Brownian motion, providing a novel, accurate and efficient method for resolving heterogeneous behavior of intracellular transport in space and time. The neural network requires significantly fewer data points compared to established methods. This enables robust estimation of Hurst exponents for very short time series data, making possible direct, dynamic segmentation and analysis of experimental tracks of rapidly moving cellular structures such as endosomes and lysosomes. By using this analysis, fractional Brownian motion with a stochastic Hurst exponent was used to interpret, for the first time, anomalous intracellular dynamics, revealing unexpected differences in behavior between closely related endocytic organelles.