Article

Recording single motor proteins in the cytoplasm of mammalian cells.

Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, Michigan, USA.
Methods in enzymology (Impact Factor: 1.9). 01/2010; 475:81-107. DOI: 10.1016/S0076-6879(10)75004-7
Source: PubMed

ABSTRACT Biomolecular motors are central to the function and regulation of all cellular transport systems. The molecular mechanisms by which motors generate force and motion along cytoskeletal filaments have been mostly studied in vitro using a variety of approaches, including several single-molecule techniques. While such studies have revealed significant insights into the chemomechanical transduction mechanisms of motors, important questions remain unanswered as to how motors work in cells. To understand how motor activity is regulated and how motors orchestrate the transport of specific cargoes to the proper subcellular domain requires analysis of motor function in vivo. Many transport processes in cells are believed to be powered by single or very few motor molecules, which makes it essential to track, in real time and with nanometer resolution, individual motors and their associated cargoes and tracks. Here we summarize, contrast, and compare recent methodological advances, many relying on advanced fluorescent labeling, genetic tagging, and imaging techniques, that lay the foundation for groundbreaking approaches and discoveries. In addition, to illustrate the impact and capabilities for these methods, we highlight novel biological findings where appropriate.

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    ABSTRACT: Molecular motors are responsible of transporting a wide variety of cargos in the cytoplasm. Current efforts are oriented to characterize the biophysical properties of motors in cells with the aim of elucidating the mechanisms of these nanomachines in the complex cellular environment. In this study, we present an algorithm designed to extract motor step sizes and dwell times between steps from trajectories of motors or cargoes driven by motors in cells. The algorithm is based on finding patterns in the trajectory compatible with the behavior expected for a motor step, i.e., a region of confined motion followed by a jump in the position to another region of confined motion with similar characteristics to the previous one. We show that this algorithm allows the analysis of 2D trajectories even if they present complex motion patterns such as active transport interspersed with diffusion and does not require the assumption of a given step size or dwell period. The confidence on the step detection can be easily obtained and allows the evaluation of the confidence of the dwell and step size distributions. To illustrate the possible applications of this algorithm, we analyzed trajectories of myosin-V driven organelles in living cells.
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