Resolving Cytosolic Diffusive States in Bacteria by Single-Molecule Tracking

Abstract

The trajectory of a single protein in the cytosol of a living cell contains information about its molecular interactions in its native environment. However, it has remained challenging to accurately resolve and characterize the diffusive states that can manifest in the cytosol using analytical approaches based on simplifying assumptions. Here, we show that multiple intracellular diffusive states can be successfully resolved if sufficient single-molecule trajectory information is available to generate wellsampled distributions of experimental measurements and if experimental biases are taken into account during data analysis. To address the inherent experimental biases in camera-based and MINFLUX-based single-molecule tracking, we use an empirical data analysis framework based on Monte Carlo simulations of confined Brownian motion. This framework is general and adaptable to arbitrary cell geometries and data acquisition parameters employed in two-dimensional or three-dimensional singlemolecule tracking. We show that, in addition to determining the diffusion coefficients and populations of prevalent diffusive states, the timescales of diffusive state switching can be determined by stepwise increasing the time window of averaging over subsequent single-molecule displacements. Time-averaged diffusion analysis of single-molecule tracking data may thus provide quantitative insights into binding and unbinding reactions among rapidly diffusing molecules that are integral for cellular functions.

Distinct cytosolic complexes containing the type III secretion system ATPase resolved by 3D single-molecule tracking in liveYersinia enterocolitica

“…Thus, a single-state, confined (within the bacterial cell) Brownian diffusion model accurately describes the measured 3D motion trajectories of eYFP in living Y. enterocolitica cells. These results are consistent with previous work by our laboratory 22 and others 25 and confirm that eYFP has negligible (non)specific interactions with cellular contents or with itself and is thus a suitable probe for measuring the diffusion coefficients for eYFP-labeled proteins.…”

“…We have previously reported the diffusive behaviors of free eYFP and eYFP-labeled Ye SctQ in Y. enterocolitica 19,22 . Here, we re-analyzed these data using a more complete library of apparent diffusion coefficient distributions (see Methods).…”

Metal-induced sensor mobilization turns on affinity to activate regulator for metal detoxification in live bacteria

“…3B). Under copper-depleted conditions, the fastest diffusion state has a diffusion constant D FD of 7.9 ± 0.6 μm 2 •s −1 , which we assigned as CusR mE freely diffusing (FD) in the cytoplasm (either as monomers or dimers), as these species are expected to exist and D FD is consistent with those for freely diffusing proteins in the bacterial cytoplasm (34,43,44). The slowest state has a diffusion constant D TB of 0.02 ± 0.01 μm 2 •s −1 , which we assigned as CusR mE tightly bound (TB) to the chromosome because CusR, when phosphorylated (i.e., activated), can bind tightly at cognate sites; D TB is also consistent with those for proteins that bind chromosome specifically (31,34,45,46), and its small magnitude mainly reflects chromosomal dynamics and experimental uncertainties in molecular localization.…”

Extracting transition rates in single-particle tracking using analytical diffusion distribution analysis

“…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.…”

“…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 .…”