Structured illumination microscopy (SIM) has emerged as a powerful super-resolution technique for studying protein dynamics in live cells thanks to its wide-field imaging mode and high photon efficiency. However, conventional SIM requires at least nine raw images to achieve super-resolution reconstruction, which limits its imaging speed and increases susceptibility to rapid sample dynamics. Moreover, the reliance of SIM on illumination parameters and algorithmic post-processing renders it vulnerable to reconstruction artifacts, especially at low signal-to-noise ratios. In this work, we propose a single-shot composite structured illumination microscopy method using ensemble deep learning (eDL-cSIM). Without modifying the original SIM setup, eDL-cSIM employs only one composite structured illumination pattern generated by 6-beam interferometry. The resultant composite-coded raw image, which contains multiplexed high-frequency spectral information beyond the diffraction limit, is further processed using ensemble deep learning to predict a high-quality, artifact-free super-resolved image. Experimental results demonstrate that eDL-cSIM integrates the advantages of various state-of-the-art neural networks, enabling robust super-resolution image predictions across different specimen types or structures of interest, and outperforms classical physics-driven methods in terms of imaging speed, reconstruction quality and environmental robustness, while avoiding intricate and specialized algorithmic procedures. These collective advantages make eDL-cSIM a promising tool for fast and robust live-cell super-resolution microscopy with significantly reduced phototoxicity and photobleaching.

Exosomes distributed by extracellular vesicles carry various information highly consistent with cells, becoming a new type of biomarker for tumor screening. However, although conventional characterization technologies can quantify size and morphology for exosomes, they are limited in related fields such as function tracing, protein quantification at unit point, and microstructural information. In this paper, firstly, different exosome characterization methods are systematically reviewed, such as dynamic light scattering, nanoparticle tracking analysis, flow cytometry, electron microscope, and emerging super-resolution imaging technologies. Then, advances in applications are described one by one. Last but not least, we compare the features of different technologies for exosomes and propose that super-resolution imaging technology can not only take into account the advantages of conventional characterization techniques but also provide accurate, real-time, and super-resolution quantitative analysis for exosomes. It provides a fine guide for exosome-related biomedical research, as well as application in liquid biopsy and analysis techniques.