A comparison of step-detection methods: how well can you do?

Many biological machines function in discrete steps, and detection of such steps can provide insight into the machines' dynamics. It is therefore crucial to develop an automated method to detect steps, and determine how its success is impaired by the significant noise usually present. A number of step detection methods have been used in previous studies, but their robustness and relative success rate have not been evaluated. Here, we compare the performance of four step detection methods on artificial benchmark data (simulating different data acquisition and stepping rates, as well as varying amounts of Gaussian noise). For each of the methods we investigate how to optimize performance both via parameter selection and via prefiltering of the data. While our analysis reveals that many of the tested methods have similar performance when optimized, we find that the method based on a chi-squared optimization procedure is simplest to optimize, and has excellent temporal resolution. Finally, we apply these step detection methods to the question of observed step sizes for cargoes moved by multiple kinesin motors in vitro. We conclude there is strong evidence for sub-8-nm steps of the cargo's center of mass in our multiple motor records.

Multiple-motor based transport and its regulation by Tau.

Motor-based intracellular transport and its regulation are crucial to the functioning of a cell. Disruption of transport is linked to Alzheimer's and other neurodegenerative diseases. However, many fundamental aspects of transport are poorly understood. An important issue is how cells achieve and regulate efficient long-distance transport. Mounting evidence suggests that many in vivo cargoes are transported along microtubules by more than one motor, but we do not know how multiple motors work together or can be regulated. Here we first show that multiple kinesin motors, working in conjunction, can achieve very long distance transport and apply significantly larger forces without the need of additional factors. We then demonstrate in vitro that the important microtubule-associated protein, tau, regulates the number of engaged kinesin motors per cargo via its local concentration on microtubules. This function of tau provides a previously unappreciated mechanism to regulate transport. By reducing motor reattachment rates, tau affects cargo travel distance, motive force, and cargo dispersal. We also show that different isoforms of tau, at concentrations similar to those in cells, have dramatically different potency. These results provide a well defined mechanism for how altered tau isoform levels could impair transport and thereby lead to neurodegeneration without the need of any other pathway.

Tracking single particles: a user-friendly quantitative evaluation.

As our knowledge of biological processes advances, we are increasingly aware that cells actively position sub-cellular organelles and other constituents to control a wide range of biological processes. Many studies quantify the position and motion of, for example, fluorescently labeled proteins, protein aggregates, mRNA particles or virus particles. Both differential interference contrast (DIC) and fluorescence microscopy can visualize vesicles, nuclei or other small organelles moving inside cells. While such studies are increasingly important, there has been no complete analysis of the different tracking methods in use, especially from the practical point of view. Here we investigate these methods and clarify how well different algorithms work and also which factors play a role in assessing how accurately the position of an object can be determined. Specifically, we consider how ultimate performance is affected by magnification, by camera type (analog versus digital), by recording medium (VHS and SVHS tape versus direct tracking from camera), by image compression, by type of imaging used (fluorescence versus DIC images) and by a variety of sources of noise. We show that most methods are capable of nanometer scale accuracy under realistic conditions; tracking accuracy decreases with increasing noise. Surprisingly, accuracy is found to be insensitive to the numerical aperture, but, as expected, it scales with magnification, with higher magnification yielding improved accuracy (within limits of signal-to-noise). When noise is present at reasonable levels, the effect of image compression is in most cases small. Finally, we provide a free, robust implementation of a tracking algorithm that is easily downloaded and installed.

Cytoplasmic dynein functions as a gear in response to load.

Cytoskeletal molecular motors belonging to the kinesin and dynein families transport cargos (for example, messenger RNA, endosomes, virus) on polymerized linear structures called microtubules in the cell. These 'nanomachines' use energy obtained from ATP hydrolysis to generate force, and move in a step-like manner on microtubules. Dynein has a complex and fundamentally different structure from other motor families. Thus, understanding dynein's force generation can yield new insight into the architecture and function of nanomachines. Here, we use an optical trap to quantify motion of polystyrene beads driven along microtubules by single cytoplasmic dynein motors. Under no load, dynein moves predominantly with a mixture of 24-nm and 32-nm steps. When moving against load applied by an optical trap, dynein can decrease step size to 8 nm and produce force up to 1.1 pN. This correlation between step size and force production is consistent with a molecular gear mechanism. The ability to take smaller but more powerful strokes under load--that is, to shift gears--depends on the availability of ATP. We propose a model whereby the gear is downshifted through load-induced binding of ATP at secondary sites in the dynein head.