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.

The Type VI Secretion System (T6SS) inhibits growth of neighboring bacterial cells through a contact-mediated mechanism. We describe a detailed characterization of the protein localization dynamics in the T6SS. It has been proposed that Type VI secretion process is driven by a conformational-change induced contraction of the T6SS sheath. However, although the contraction of an optically resolvable TssBC sheath and the subsequent localization of ClpV are observed in, coordinated assembly and disassembly of TssB and ClpV are observed without TssB contraction in These dynamics are inconsistent with the proposed contraction sheath model. Motivated by the phenomenon of dynamic instability, we propose a new model in which ATP hydrolysis, rather than conformational change, generates the force for secretion. The Type VI Secretion System (T6SS) is widely conserved among Gram-negative bacteria and is a central determinant of bacterial fitness in polymicrobial communities. The secretion system targets bacteria and secretes effectors that inhibit the growth of neighboring cells using a contact-mediated-delivery system. Despite significant homology to the previously characterized T6SS, our analysis reveals that effector secretion is driven by a distinct force-generation mechanism in The presence of two distinct force-generation mechanisms in T6SS represents an example of the evolutionary diversification of force-generation mechanisms.

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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...

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