Resolving Cytosolic Diffusive States in Bacteria by Single-Molecule Tracking

Abstract

11The trajectory of a single protein in the cytosol of a living cell contains information about 12 its molecular interactions in its native environment. However, it has remained challenging to 13 accurately resolve and characterize the diffusive states that can manifest in the cytosol using 14 analytical approaches based on simplifying assumptions. Here, we show that multiple intracellular 15 diffusive states can be successfully resolved if sufficient single-molecule trajectory information is 16 available to generate well-sampled distributions of experimental measurements and if 17 experimental biases are taken into account during data analysis. To address the inherent 18 experimental biases in camera-based and MINFLUX-based single-molecule tracking, we use an 19 empirical data analysis framework based on Monte Carlo simulations of confined Brownian 20 motion. This framework is general and adaptable to arbitrary cell geometries and data acquisition 21 parameters employed in 2D or 3D single-molecule tracking. We show that, in addition to 22 determining the diffusion coefficients and populations of prevalent diffusive states, the timescales 23 of diffusive state switching can be determined by stepwise increasing the time window of 24 averaging over subsequent single-molecule displacements. Time-averaged diffusion (TAD) 25 analysis of single-molecule tracking data may thus provide quantitative insights into binding and 26 unbinding reactions among rapidly diffusing molecules that are integral for cellular functions.27 28 29 73of cytosolic proteins in bacteria is strongly confined by the cell boundaries and molecular 74 displacements will, on average, be smaller than those expected for unconfined diffusion. 75Approaches assuming unconfined Brownian motion are therefore not suitable when tracking fast 76 diffusing molecules in the cytosol of bacterial cells. 77 Several approaches have been developed in recent years to extract the diffusion rates and 78 population fractions of different diffusive states that manifest for unbound molecules in confined 79 cellular environments. These approaches account for confinement effects by the cell boundaries 80 either (semi-)analytically (23-26) or numerically through Monte Carlo simulation of Brownian 81 diffusion trajectories (7, 13, 17, 27, 28). Here, we test and experimentally validate a numerical 82 analysis framework based on Monte Carlo simulations for both 2D and 3D single-molecule 83 tracking in bacterial cells ( Fig. 1). By explicitly accounting for confinement as well as 'motion-84 blur' of diffusing molecules inside small bacterial cells, we extract the unconfined diffusion 85 coefficients for two genetically encoded fluorescence proteins, eYFP and mEos3.2, in living Y. 86 enterocolitica cells. Using simulated 2D or 3D single-molecule tracking data of known diffusive 87 state composition, we quantify to what extent two or more simultaneously present diffusive states 88 can be resolved by numerical fitting of the displacement or apparent diffusion coefficient 89 d...

Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis

“…Using the estimated error for each individual localization can further improve the robustness of the analysis as has been demonstrated previously 53 . Another improvement which can be incorporated in our framework and has already been developed is to take the effect of particles moving out-of-focus, and the recovery of localizations depending on diffusion coefficients into account 38,50 .…”

“…Overall, the transition rates between bound and unbound polymerase found under both conditions are high compared to the frame rate (>1 transition per frame), which demonstrates the applicability of anaDDA to quantify very fast transition kinetics in vivo. Compared to other simulation-based frameworks for estimating transition rates [48][49][50] , anaDDA holds several advantages. First, the distributions of simulations are not exact as they are generated from a limited number of particles and therefore do not allow for using an MLE approach, which requires convergence based on exact probability even for small changes in the parameter space.…”