Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry

Abstract

Resolving distinct biochemical interaction states when analyzing the trajectories of diffusing proteins in live cells on an individual basis remains challenging because of the limited statistics provided by the relatively short trajectories available experimentally. Here, we introduce a novel, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors which collectively result from distinct biochemical interactions. We validate the performance of pEM in silico and demonstrate that pEM is capable of uncovering the proper number of underlying diffusive states with an accurate characterization of their diffusion properties. We then apply pEM to experimental protein trajectories of Rho GTPases, an integral regulator of cytoskeletal dynamics and cellular homeostasis, in vivo via single particle tracking photo-activated localization microcopy. Remarkably, pEM uncovers 6 distinct diffusive states conserved across various Rho GTPase family members. The variability across family members in the propensities for each diffusive state reveals non-redundant roles in the activation states of RhoA and RhoC. In a resting cell, our results support a model where RhoA is constantly cycling between activation states, with an imbalance of rates favoring an inactive state. RhoC, on the other hand, remains predominantly inactive.

Systems-level approach to uncovering diffusive states and their transitions from single-particle trajectories

“…Importantly, pEMv2 is rooted in physical principles of stochastic processes. Applying non-physical clustering methods to the same data, such as k-means clustering, lead to poor characterization of the underlying diffusive states [17].…”

“…Here, we extend the original pEM framework [17] to now include non-normal modes of diffusion, i.e. we lift the short-time-diffusion approximation.…”

“…However, as described in detail in Ref. [17] and summarized in the Methods (Sec. IV), suitably perturbing the likelihood surface, namely pEM, is a computationally efficient means to reach the global maximum likelihood.…”

“…[17]. The traditional approach for analyzing the diffusive properties of individual particle trajectories is by fitting each trajectory's time-averaged mean * simon.mochrie@yale.edu square displacement (taMSD) to a corresponding diffusion model [18].…”

Arg188 drives RhoC membrane binding

“…6,7 Koo et al recently analyzed the location of the fluorescent reporter, mEos2, when fused to either full-length RhoA, fulllength RhoC, or the HV domain of either RhoA or RhoC. 8 The authors found a similar number of diffusion states for mEos2 when attached to either full-length GTPase or its corresponding HV domain, suggesting the HV domain alone dictates much of the diffusibility of RhoA and RhoC within cells, potentially through its intermolecular interactions. Moreover, the HV domain chimeras of RhoA and RhoC were found to be dissimilar.…”