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- 摘要
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- 实验方案
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Photobleaching Enables Super-resolution Imaging of the FtsZ Ring in the Cyanobacterium <em>Prochlorococcus</em>
摘要： Super-resolution microscopy has been widely used to study protein interactions and subcellular structures in many organisms. In photosynthetic organisms, however, the lateral resolution of super-resolution imaging is only ~100 nm. The low resolution is mainly due to the high autofluorescence background of photosynthetic cells caused by high-intensity lasers that are required for super-resolution imaging, such as stochastic optical reconstruction microscopy (STORM). Here, we describe a photobleaching-assisted STORM method which was developed recently for imaging the marine picocyanobacterium Prochlorococcus. After photobleaching, the autofluorescence of Prochlorococcus is effectively reduced so that STORM can be performed with a lateral resolution of ~10 nm. Using this method, we acquire the in vivo three-dimensional (3-D) organization of the FtsZ protein and characterize four different FtsZ ring morphologies during the cell cycle of Prochlorococcus. The method we describe here might be adopted for the super-resolution imaging of other photosynthetic organisms.
关键词： Prochlorococcus,photobleaching,FtsZ ring,Immunology and Infection,STORM,cell division,cyanobacterium,super-resolution imaging,three-dimensional,Issue 141
更新于2025-09-23 15:21:01
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Feature-based Classification of Protein Networks using Confocal Microscopy Imaging and Machine Learning
摘要： Fluorescence imaging has become a powerful tool to investigate complex subcellular structures such as cytoskeletal filaments. Advanced microscopes generate 3D imaging data at high resolution, yet tools for quantification of the complex geometrical patterns are largely missing. Here we present a computational framework to classify protein network structures. We developed a machine-learning method that combines state-of-the-art morphological quantification with protein network classification through morphologically distinct structural features enabling live imaging–based screening. We demonstrate applicability in a confocal laser scanning microscopy (CLSM) study differentiating protein networks of the FtsZ (filamentous temperature sensitive Z) family inside plant organelles (Physcomitrella patens).
关键词： FtsZ,machine learning,classification,protein networks,confocal microscopy
更新于2025-09-04 15:30:14