Mitosis is essential for the growth, development and homeostasis of eukaryotes. Kinetochores are protein machines that control mitotic progression by forming dynamic force-generating attachments to spindle microtubules. Here, we will use super-resolution microscopy to image large numbers of kinetochores at distinct mitotic stages at a resolution of tens of nm. A cutting-edge deep learning approach will be used to fit the data, where the training process of the network is used to optimise a model with no prior constraints being applied. This model will be integrated from data from live-cell experiments to create the first nanoscale model of kinetochore dynamics.
1 Auckland, Pᵼ., Roscioli, R., Coker, H.L.E., and McAinsh, A.Dᵼ. (2020). CENP-F stabilizes kinetochore-microtubule attachments and limits dynein stripping of corona cargoes. J Cell Biol, doi: 10.1083/jcb.201905018 ᵼco-correspondence
2 Auckland, P., Clarke, N., Royle, S. & McAinsh, A.D. (2017). Congressing kinetochores progressively load Ska complexes to prevent force dependent detachment. J Cell Biol, doi: 10.1083/jcb.201607096
3 Marsh RJ, Pfisterer K, Bennett P, Hirvonen LM, Gautel M, Jones GE, Cox S*, ‘Artifact-free high-density localization microscopy analysis’, 2018, Nature Methods, 15:689
4 Mahdi Rad, Vincent Lepetit, BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects Without Using Depth, Proceedings of the ICCV, 2017, 3828-3836