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Advancements in Super-Resolution Microscopy: Tracking Moving Biomolecules
Researchers have made a significant breakthrough in capturing high-quality images of rapidly moving molecules, marking a new era for microscopy. Assistant Professor Ioannis Sgouralis from the Department of Mathematics, alongside his team, has introduced an innovative algorithm that enhances super-resolution microscopy capabilities to observe dynamic molecular activities.
The field of super-resolution microscopy, which garnered the Nobel Prize in Chemistry in 2014, has revolutionized optical imaging techniques. By circumventing the limitations imposed by the physics of light, this technology allows scientists to visualize structures at a molecular level that were previously undetectable with standard microscopes. Traditional imaging methods are hindered by diffraction, which blurs details smaller than the wavelength of light itself, typically rendering biological structures like DNA, RNA, and proteins unrecognizable during visual assessments.
“In biochemistry and molecular biology, observing the minute details of individual biomolecules is essential,” Sgouralis noted. “Most significant biomolecules are approximately 1,000 times smaller than the wavelength of light. Consequently, their images often appear distorted and unclear, rendering them unsuitable for scientific investigation.”
Existing super-resolution techniques, such as PALM and STORM, have successfully improved visualization by using advanced image-analysis algorithms that remedy these distortions under stationary conditions. However, one major drawback is their inability to capture moving biomolecules, which is critical as these molecules are perpetually in motion within living organisms.
In an exciting development, Sgouralis and his colleagues recently published their research in Nature Methods, presenting a new framework known as Bayesian nonparametric track (BNP-Track). This pioneering algorithm allows for super-resolution microscopy even in the context of moving biomolecules.
“We have developed sophisticated mathematical methodologies that can analyze images from microscopy experiments, enabling us to retrieve essential information while biomolecules shift positions,” Sgouralis explained. “This innovation facilitates real-time observation of these molecules within live cells, capturing their dynamic movements with unprecedented precision, paving the way for advancements in various scientific domains, including biochemistry and biotechnology.”
The BNP-Track algorithm offers researchers a powerful tool to explore vital questions surrounding biomolecular dynamics. It can help uncover whether biomolecules aggregate in specific cellular locations, their origins, methods of transport within and between cells, and whether certain molecules exhibit a tendency to cluster or separate.
“These inquiries hold significant importance in drug development and in unraveling the complexities of molecular biology,” Sgouralis remarked.
Future research on BNP-Track aims to enhance the speed of the algorithm’s processing capabilities. Current analysis for individual experiments can take several hours, but the goal is to reduce this time to mere minutes or even seconds, enabling the rapid assessment of images from various experiments.
Moreover, the team intends to adapt the algorithm for use with diverse microscopy setups applicable across various laboratory environments. The advancements brought by BNP-Track have laid a solid foundation for ongoing discoveries in the molecular sciences.
More information: Ioannis Sgouralis et al, BNP-Track: a framework for superresolved tracking, Nature Methods (2024). DOI: 10.1038/s41592-024-02349-9
Provided by the University of Tennessee at Knoxville.
Source
phys.org