Precise Particle Tracking Against a Complicated Background: Polynomial Fitting with Gaussian Weight

We present a new particle tracking software algorithm designed to accurately track the motion of low-contrast particles against a background with large variations in light levels. The method is based on a polynomial fit of the intensity around each feature point, weighted by a Gaussian function of the distance from the centre, and is especially suitable for tracking endogeneous particles in the cell, imaged with bright field, phase contrast or fluorescence optical microscopy. Furthermore, the method can simultaneously track particles of all different sizes, and allows significant freedom in their shape. The algorithm is evaluated using the quantitative measures of accuracy and precision of previous authors, using simulated images at variable signal-to-noise ratios. To these we add a new test of the error due to a non-uniform background. Finally the tracking of particles in real cell images is demonstrated. The method is made freely available for non-commencial use as a software package with a graphical user-inferface, which can be run within the Matlab programming environment

Direct conversion of rheological compliance measurements into storage and loss moduli

We remove the need for Laplace/inverse-Laplace transformations of experimental data, by presenting a direct and straightforward mathematical procedure for obtaining frequency-dependent storage and loss moduli ($G'(\omega)$ and $G"(\omega)$ respectively), from time-dependent experimental measurements. The procedure is applicable to ordinary rheological creep (stress-step) measurements, as well as all microrheological techniques, whether they access a Brownian mean-square displacement, or a forced compliance. Data can be substituted directly into our simple formula, thus eliminating traditional fitting and smoothing procedures that disguise relevant experimental noise.Comment: 4 page

Extreme heterogeneity in the microrheology of lamellar surfactant gels analyzed with neural networks

The heterogeneity of the viscoelasticity of a lamellar gel network based on cetyl-trimethylammonium chloride (CTAC) and ceto-stearyl alcohol was studied using particle tracking microrheology. A recurrent neural network (RNN) architecture was used for estimating the Hurst exponent, $H$, on small sections of tracks of probe spheres moving with fractional Brownian motion. Thus dynamic segmentation of tracks via neural networks was used in microrheology for the first time and it is significantly more accurate than using mean square displacements. An ensemble of 414 particles produces a mean squared displacement (MSD) that is subdiffusive in time, $t$, with a power law of the form $t^{0.74\pm0.02}$, indicating power law viscoelasticity. RNN analysis of the probability distributions of $H$, combined with detailed analysis of the time-averaged MSDs of individual tracks, revealed diverse diffusion processes belied by the simple scaling of the ensemble MSD, such as caging phenomena, which give rise to the complex viscoelasticity of lamellar gels.Comment: 15 pages without references (17 with references), 13 figure

Memory effects and L\'evy walk dynamics in intracellular transport of cargoes

We demonstrate the phenomenon of cumulative inertia in intracellular transport involving multiple motor proteins in human epithelial cells by measuring the empirical survival probability of cargoes on the microtubule and their detachment rates. We found the longer a cargo moves along a microtubule, the less likely it detaches from it. As a result, the movement of cargoes is non-Markovian and involves a memory. We observe memory effects on the scale of up to 2 seconds. We provide a theoretical link between the measured detachment rate and the super-diffusive Levy walk-like cargo movement.Comment: 9 pages, 6 figure

Network organisation and the dynamics of tubules in the endoplasmic reticulum

From Springer Nature via Jisc Publications RouterHistory: received 2021-04-26, accepted 2021-06-27, registration 2021-07-19, pub-electronic 2021-08-10, online 2021-08-10, collection 2021-12Publication status: PublishedFunder: Biotechnology and Biological Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000268; Grant(s): BB/H017828/1Funder: Wellcome Trust; doi: http://dx.doi.org/10.13039/100010269; Grant(s): 215189/Z/19/ZFunder: Engineering and Physical Sciences Research Council; doi: http://dx.doi.org/10.13039/501100000266Abstract: The endoplasmic reticulum (ER) is a eukaryotic subcellular organelle composed of tubules and sheet-like areas of membrane connected at junctions. The tubule network is highly dynamic and undergoes rapid and continual rearrangement. There are currently few tools to evaluate network organisation and dynamics. We quantified ER network organisation in Vero and MRC5 cells, and developed an analysis workflow for dynamics of established tubules in live cells. The persistence length, tubule length, junction coordination number and angles of the network were quantified. Hallmarks of imbalances in ER tension, indications of interactions with microtubules and other subcellular organelles, and active dynamics were observed. Clear differences in dynamic behaviour were observed for established tubules at different positions within the cell using itemset mining. We found that tubules with activity-driven fluctuations were more likely to be located away from the cell periphery and a population of peripheral tubules with no signs of active motion was found

Intracellular microrheology of motile Amoeba proteus

The motility of motile Amoeba proteus was examined using the technique of passive particle tracking microrheology, with the aid of newly-developed particle tracking software, a fast digital camera and an optical microscope. We tracked large numbers of endogeneous particles in the amoebae, which displayed subdiffusive motion at short time scales, corresponding to thermal motion in a viscoelastic medium, and superdiffusive motion at long time scales due to the convection of the cytoplasm. Subdiffusive motion was characterised by a rheological scaling exponent of 3/4 in the cortex, indicative of the semiflexible dynamics of the actin fibres. We observed shear-thinning in the flowing endoplasm, where exponents increased with increasing flow rate; i.e. the endoplasm became more fluid-like. The rheology of the cortex is found to be isotropic, reflecting an isotropic actin gel. A clear difference was seen between cortical and endoplasmic layers in terms of both viscoelasticity and flow velocity, where the profile of the latter is close to a Poiseuille flow for a Newtonian fluid

The First Passage Probability of Intracellular Particle Trafficking

The first passage probability (FPP), of trafficked intracellular particles reaching a displacement L, in a given time t or inverse velocity S = t/L, can be calculated robustly from measured particle tracks, and gives a measure of particle movement in which different types of motion, e.g. diffusion, ballistic motion, and transient run-rest motion, can readily be distinguished in a single graph, and compared with mathematical models. The FPP is attractive in that it offers a means of reducing the data in the measured tracks, without making assumptions about the mechanism of motion: for example, it does not employ smoothing, segementation or arbitrary thresholds to discriminate between different types of motion in a particle track. Taking experimental data from tracked endocytic vesicles, and calculating the FPP, we see how three molecular treatments affect the trafficking. We show the FPP can quantify complicated movement which is neither completely random nor completely deterministic, making it highly applicable to trafficked particles in cell biology.Comment: Article: 13 pages, 8 figure

Dual self-assembly of supramolecular peptide nanotubes to provide stabilisation in water

Self-assembling peptides have the ability to spontaneously aggregate into large ordered structures. The reversibility of the peptide hydrogen bonded supramolecular assembly make them tunable to a host of different applications, although it leaves them highly dynamic and prone to disassembly at the low concentration needed for biological applications. Here we demonstrate that a secondary hydrophobic interaction, near the peptide core, can stabilise the highly dynamic peptide bonds, without losing the vital solubility of the systems in aqueous conditions. This hierarchical self-assembly process can be used to stabilise a range of different β-sheet hydrogen bonded architectures

Local Analysis of Heterogeneous Intracellular Transport: Slow and Fast Moving Endosomes

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-23, pub-electronic 2021-07-27Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; Grant(s): EP/V008641/1Funder: Wellcome Trust; Grant(s): Grant No. 215189/Z/19/ZFunder: Basque Government; Grant(s): BERC 2018–2021 programsFunder: Spanish Ministry of Economy and Competitiveness MINECO; Grant(s): BCAM Severo Ochoa excellence accreditation SEV-2017-0718Trajectories of endosomes inside living eukaryotic cells are highly heterogeneous in space and time and diffuse anomalously due to a combination of viscoelasticity, caging, aggregation and active transport. Some of the trajectories display switching between persistent and anti-persistent motion, while others jiggle around in one position for the whole measurement time. By splitting the ensemble of endosome trajectories into slow moving subdiffusive and fast moving superdiffusive endosomes, we analyzed them separately. The mean squared displacements and velocity auto-correlation functions confirm the effectiveness of the splitting methods. Applying the local analysis, we show that both ensembles are characterized by a spectrum of local anomalous exponents and local generalized diffusion coefficients. Slow and fast endosomes have exponential distributions of local anomalous exponents and power law distributions of generalized diffusion coefficients. This suggests that heterogeneous fractional Brownian motion is an appropriate model for both fast and slow moving endosomes. This article is part of a Special Issue entitled: “Recent Advances In Single-Particle Tracking: Experiment and Analysis” edited by Janusz Szwabiński and Aleksander Weron