Summary To ensure equal chromosome segregation during mitosis, the macromolecular kinetochore must remain attached to depolymerizing microtubules, which drive chromosome movements. How kinetochores associate with depolymerizing microtubules, which undergo dramatic structural changes forming curved protofilaments, has yet to be defined in vertebrates. Here, we demonstrate that the conserved kinetochore-localized Ska1 complex tracks with depolymerizing microtubule ends and associates with both the microtubule lattice and curved protofilaments. In contrast, the Ndc80 complex, a central player in the kinetochore-microtubule interface, binds only to the straight microtubule lattice and lacks tracking activity. We demonstrate that the Ska1 complex imparts its tracking capability to the Ndc80 complex. Finally, we present a structure of the Ska1 microtubule binding domain that reveals its interaction with microtubules and its regulation by Aurora B. This work defines an integrated kinetochore-microtubule interface formed by the Ska1 and Ndc80 complexes that associates with depolymerizing microtubules, potentially by interacting with curved microtubule protofilaments.

Live-cell imaging and particle tracking provide rich information on mechanisms of intracellular transport. however, trajectory analysis procedures to infer complex transport dynamics involving stochastic switching between active transport and diffusive motion are lacking. We applied bayesian model selection to hidden markov modeling to infer transient transport states from trajectories of mrna-protein complexes in live mouse hippocampal neurons and metaphase kinetochores in dividing human cells. the software is available at http://hmm-bayes.org/.

Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.

Messenger RNA localization is important for cell motility by local protein translation. However, while single mRNAs can be imaged and their movements tracked in single cells, it has not yet been possible to determine whether these mRNAs are actively translating. Therefore, we imaged single β-actin mRNAs tagged with MS2 stem loops colocalizing with labeled ribosomes to determine when polysomes formed. A dataset of tracking information consisting of thousands of trajectories per cell demonstrated that mRNAs co-moving with ribosomes have significantly different diffusion properties from non-translating mRNAs that were exposed to translation inhibitors. These data indicate that ribosome load changes mRNA movement and therefore highly translating mRNAs move slower. Importantly, β-actin mRNA near focal adhesions exhibited sub-diffusive corralled movement characteristic of increased translation. This method can identify where ribosomes become engaged for local protein production and how spatial regulation of mRNA-protein interactions mediates cell directionality.DOI: http://dx.doi.org/10.7554/eLife.10415.001