| الوصف: |
Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy video frames with precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors.Besides SPT, we used single-molecule localization microscopy (SMLM) to study cytokinetic protein in fission yeast. During cytokinesis, myosin-II constricts the contractile ring that separates one cell into two daughter cells. The fission yeast cytokinetic contractile ring contains two types of myosin Ⅱ, Myo2 and Myp2. However, the precise ultrastructural arrangement of the two type Ⅱ myosins remains in question. We investigated the relative spatial arrangement of Myo2p and Myp2p within contractile ring using two-color super-resolution microscopy based on salvaged fluorescence imaging. Quantitative analysis of the nanoscale images should provide useful information for modeling contractile ring assembly and constriction. |