Definition of stochastic optical reconstruction microscopy

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stochastic optical reconstruction microscopy: Stochastic optical reconstruction microscopy (STORM) is a superresolution microscopy technique that enables imaging of biological specimens at resolutions beyond the diffraction limit of conventional optical microscopy. It falls under the category of single-molecule localization microscopy (SMLM) methods. STORM was first introduced in 2006 and has since become a powerful tool in biological research for visualizing fine details of cellular structures.

The key principle behind STORM involves the precise localization of individual fluorophores (molecules that emit light) within a sample. Unlike traditional microscopy techniques limited by diffraction, STORM achieves super-resolution by using the stochastic blinking behavior of certain fluorophores. The process typically involves the following steps:

Fluorophore activation: Only a subset of fluorophores in the sample is allowed to emit light at a given time. This is achieved by activating a small fraction of the fluorophores with specific wavelengths of light.

Imaging and photobleaching: After activation, the emitted fluorescence is captured using a camera. Subsequently, the activated fluorophores are irreversibly photobleached, preventing them from emitting further photons.

Repetition: Steps 1 and 2 are repeated multiple times, with different subsets of fluorophores activated in each cycle.

Localization: The positions of individual fluorophores are determined with high precision using mathematical algorithms based on the recorded images. The final super-resolved image is reconstructed from the cumulative localization data.

STORM, along with other single-molecule localization microscopy techniques, has revolutionized the field of cell biology by providing unprecedented spatial resolution, allowing researchers to visualize structures as small as tens of nanometers. This enhanced resolution has led to a deeper understanding of cellular architecture and dynamics.