|
Traditional fluorescence microscopes, limited in resolution, impede the precise identification of subcellular compartments. While super-resolution microscopes are frequently employed to compensate for this deficiency, their application in multicell or live tissue studies poses challenges due to inherent tradeoffs and high expenses. On the other side, understanding the functionality of living cells and exploring intracellular dynamics within their natural organismic environment demands sophisticated and costly equipment, which may not be affordable for all laboratories. In this investigation, we applied computational methods, specifically employed super-resolution radial fluctuations (SRRF), on the mouse thigh to capture sequences of tissue micrographs. By systematically exploring various numerical modifications and parameters, we identified specific factors that significantly enhanced the resolution of subcellular structures.
The subcellular imaging in the thigh muscle was conducted for this purpose. Standard imaging protocols encompassed capturing sequences of image series with varying frame counts at different rates, utilizing high numerical aperture (NA) objective lenses to explore multiple parameters for optimal results. Subsequently, following sequential numerical adjustments to minimize background noise and enhance signal intensity, the SRRF algorithm was employed on the image stacks. The result was frames of muscle fibers with significantly improved resolution. In the final images, discrete organelle structures and dynamics were discernible, overcoming the poor lateral resolution of the original microscope images that depicted indistinct schematics of organelles. Importantly, this technique is versatile, requiring neither specific systems nor components, and it does not entail additional costs.
|
