title: 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 microscopy. 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.
authors.affiliation: Department of Physics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Peter K
authors.initials: PK
authors.lastname: Koo
authors.affiliation: Department of Biological Sciences, University of Delaware, Newark, Delaware, United States of America.
authors.forename: Matthew
authors.initials: M
authors.lastname: Weitzman
authors.affiliation: Delaware Biotechnology Institute, Bioimaging Center, Newark, Delaware, United States of America.
authors.forename: Chandran R
authors.initials: CR
authors.lastname: Sabanaygam
authors.affiliation: Department of Biological Sciences, University of Delaware, Newark, Delaware, United States of America.
authors.forename: Kenneth L
authors.initials: KL
authors.lastname: van Golen
authors.affiliation: Department of Biological Sciences, University of Delaware, Newark, Delaware, United States of America; Department of Applied Physics, Yale University, New Haven, Connecticut, United States of America.
authors.forename: Simon G J
authors.initials: SG
authors.lastname: Mochrie
mesh_terms: D003198:Computer Simulation; D004058:Diffusion; D000069550:Machine Learning; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D057054:Molecular Imaging / Q000379:methods*; D013347:Subcellular Fractions / Q000737:chemistry*; D020741:rho GTP-Binding Proteins / Q000737:chemistry*
chemical_list: D020741:rho GTP-Binding Proteins