Biology subjects

Roichman, Y.

Publications and source records attributed to Roichman, Y..

3 recordsLinked to original sources

bioRxiv2019

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

ABSRACTTracking 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 such 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.

biophysics↗

bioRxiv2019

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 to distinguish between these processes are based on asymptotic behavior, which is inaccessible experimentally in most cases. Finally, an accurate analysis of the diffusion model requires the calculation of many observables, since different transport modes can result in the same diffusion power-law , that is obtained from the commonly used mean-squared-displacement (MSD) in its various forms. 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 non-expert level.\n\nHere, we use deep learning to infer the underlying process resulting in anomalous diffusion. We implement a neural network to classify single particle trajectories according to diffusion type - Brownian motion, fractional Brownian motion (FBM) and Continuous Time Random Walk (CTRW). We further use the net to estimate the Hurst exponent for FBM, and the diffusion coefficient for Brownian motion, demonstrating its applicability on simulated and experimental data. The networks outperform time averaged MSD analysis on simulated trajectories while requiring as few as 25 time-steps. Furthermore, when tested on experimental data, both network and ensemble MSD analysis converge to similar values, with the network requiring half the trajectories required for ensemble MSD. Finally, we use the nets to extract diffusion parameters from multiple extremely short trajectories (10 steps).

biophysics↗

bioRxiv2019

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 sub-micron 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.

biophysics↗