Have you ever wondered how scientists observe the tiniest details of living cells? Microscopes serve as essential tools in biology, enabling researchers to examine the inner workings of cells and the interactions between their components. However, the spatial resolution of conventional microscopes is limited by diffraction, restricting our ability to discern structures smaller than a few hundred nanometers.
In recent years, super-resolution microscopy has emerged as a powerful technique to overcome this limitation, achieving higher spatial resolution. Single-molecule localization microscopy (SMLM) is one such method, providing a tenfold improvement in resolution compared to conventional fluorescence microscopy. Nonetheless, the image acquisition time and phototoxicity associated with SMLM can hinder the observation of instantaneous intracellular dynamics.
Researchers at The Hong Kong University of Science and Technology have developed a deep-learning-based single-frame super-resolution microscopy (SFSRM) method capable of reconstructing a super-resolution image from a single frame of a diffraction-limited image. Utilizing a subpixel edge map and a multicomponent optimization strategy to guide the neural network, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics.
The SFSRM approach offers several advantages over existing super-resolution microscopy techniques. First, it dramatically reduces the image acquisition time and phototoxicity associated with SMLM, enabling the observation of instantaneous intracellular dynamics. Second, it adapts to various microscopes and spectra, making it a versatile tool for diverse imaging systems. Third, it allows high-speed live-cell imaging without sacrificing spatial resolution, enabling researchers to study dynamic processes in real-time.
SFSRM provides a powerful tool for visualizing subcellular structures and the dynamics of intracellular activities with unprecedented spatiotemporal resolution. In mammalian cells, microtubules (MTs) function as highways, while vesicles serve as transport vehicles, delivering proteins and lipids along MTs. Despite their fundamental importance, the directionality and mobility of vesicles moving on MTs at fine spatial and temporal resolutions remain largely unexplored. In this study, SFSRM revealed novel dynamics of vesicular transport on MTs based on time-lapse diffraction-limited single-frame images of living cells. Notably, MTs were detected to undergo high-frequency transverse vibrations. Vesicles exhibited distinct movement dynamics along the vibrating MTs, including moving back and forth, rotating around, switching between different MTs, and colliding with other vesicles before changing direction. Additionally, MTs generated local grids to trap endosomes, actively participating in the transport and fusion processes of endosomes during endocytosis.
Furthermore, SFSRM visualized vesicle movement from the Golgi area to the cell surface during exocytosis and detected interplays between mitochondria and the endoplasmic reticulum. These dynamic events, previously undetected, provide crucial insights into vesicular trafficking and other intracellular activities.
The SFSRM approach is made possible by the rapid development of artificial intelligence, particularly deep learning networks that have demonstrated exceptional performance in single-image super-resolution tasks. By modifying these networks to enhance the resolution of microscopic images, researchers have ushered in a new era for intracellular dynamics imaging.
In conclusion, the SFSRM approach represents a significant advancement in super-resolution microscopy. By enabling high-speed live-cell imaging with increased spatial resolution, it offers a powerful tool for studying the intricate dynamics of living cells. As deep learning networks and imaging technology continue to advance, we can anticipate even more exciting developments in the future of intracellular dynamics imaging.
Go check it out: https://doi.org/10.1038/s41467-023-38452-2
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